matrixStats/0000755000175100001440000000000012776367215012633 5ustar hornikusersmatrixStats/inst/0000755000175100001440000000000012776336765013616 5ustar hornikusersmatrixStats/inst/benchmarking/0000755000175100001440000000000012776336765016246 5ustar hornikusersmatrixStats/inst/benchmarking/colRowAlls.md.rsp0000644000175100001440000000256212726216471021443 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colAlls"%> <%@string rowname="rowAlls"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-18"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + all() * colSums() == n or rowSums() == n ## Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode="logical") })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colAlls = colAlls(X), "apply+all" = apply(X, MARGIN=2L, FUN=all), "colSums==n" = (colSums(X) == nrow(X)), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowAlls = rowAlls(X), "apply+all" = apply(X, MARGIN=1L, FUN=all), "rowSums==n" = (rowSums(X) == ncol(X)), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-18 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/binMeans.md.rsp0000644000175100001440000000371612776226773021133 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="binMeans"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-04"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=fcnname%>() against alternative methods. ## Alternative methods * binMeans_R() which is defined as ```r <%=withCapture({ binMeans_R <- function(y, x, bx, na.rm=FALSE, count=TRUE, right=FALSE) { B <- length(bx)-1L res <- double(B) counts <- integer(B) # For each bin... for (kk in seq_len(B)) { if (right) { idxs <- which(bx[kk] < x & x <= bx[kk+1L]) } else { idxs <- which(bx[kk] <= x & x < bx[kk+1L]) } yKK <- y[idxs] muKK <- mean(yKK) res[kk] <- muKK counts[kk] <- length(idxs) } # for (kk ...) if (count) attr(res, "count") <- counts res } # binMeans_R() })%> ``` ## Results ### Non-sorted simulated data ```r <%=withCapture({ nx <- 10e3 # Number of data points set.seed(0xBEEF) x <- runif(nx, min=0, max=1) y <- runif(nx, min=0, max=1) # Uniformely distributed bins nb <- 1e3 # Number of bins bx <- seq(from=0, to=1, length.out=nb+1L) bx <- c(-1, bx, 2) })%> ``` <% benchmark <- function() { %> <% dataLabel <- if (is.unsorted(x)) "unsorted" else "sorted" %> <% message(dataLabel) %> ```r <%=withCapture({ gc() stats <- microbenchmark( binMeans = binMeans(x=x, y=y, bx=bx, count=TRUE), binMeans_R = binMeans_R(x=x, y=y, bx=bx, count=TRUE), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=dataLabel) %> <% } # benchmark() %> <% benchmark() %> ### Sorted simulated data ```r <%=withCapture({ x <- sort(x) })%> ``` <% benchmark() %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-02 o Restructured. 2014-05-25 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/x_OP_y.md.rsp0000644000175100001440000000401312726216471020550 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="x_OP_y"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-26"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=fcnname%>() against alternative methods. ## Alternative methods * x_OP_y_R() as below ```r <%=withCapture({ x_OP_y_R <- function(x, y, OP, na.rm=FALSE) { if (na.rm) { xnok <- is.na(x) ynok <- is.na(y) anok <- xnok & ynok unit <- switch(OP, "+" = 0, "-" = NA_real_, "*" = 1, "/" = NA_real_, stop("Unknown 'OP' operator: ", OP) ) x[xnok] <- unit y[ynok] <- unit } ans <- switch(OP, "+" = x + y, "-" = x - y, "*" = x * y, "/" = x / y, stop("Unknown 'OP' operator: ", OP) ) if (na.rm) { ans[anok] <- NA_real_ } ans } # x_OP_y_R() })%> ``` <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) x <- data[[dataLabel]] gc() %> ### <%=dataLabel%> vector #### All elements ```r <%=withCapture({ x <- data[[.dataLabel.]] y <- x[,1L] })%> ``` <% for (OP in c("+", "-", "*", "/")) { %> <% OPTag <- c("+"="add", "-"="sub", "*"="mul", "/"="div")[OP] gc() %> ```r <%=withCapture({ OP stats <- microbenchmark( "x_OP_y" = x_OP_y(x, y, OP=OP, na.rm=FALSE), "x_OP_y_R" = x_OP_y_R(x, y, OP=OP, na.rm=FALSE), unit = "ms" ) gc() })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel, OPTag)) %> <% } # for (OP ...) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-26 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowAlls_subset.md.rsp0000644000175100001440000000305212726216471023023 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colAlls"%> <%@string rowname="rowAlls"%> <%@string fcnname="colRowAlls_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-06"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. ## Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode="logical") })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() colStats <- microbenchmark( "colAlls_X_S" = colAlls(X_S), "colAlls(X,rows,cols)" = colAlls(X, rows=rows, cols=cols), "colAlls(X[rows,cols])" = colAlls(X[rows,cols]), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowAlls_X_S" = rowAlls(X_S), "rowAlls(X,cols,rows)" = rowAlls(X, rows=cols, cols=rows), "rowAlls(X[cols,rows])" = rowAlls(X[cols,rows]), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-06 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowWeightedMeans_subset.md.rsp0000644000175100001440000000357612726216471024667 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colWeightedMeans"%> <%@string rowname="rowWeightedMeans"%> <%@string fcnname="colRowWeightedMeans_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. ## Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode="double") })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] w <- runif(nrow(X)) w_S <- w[rows] gc() colStats <- microbenchmark( "colWeightedMeans_X_w_S" = colWeightedMeans(X_S, w=w_S, na.rm=FALSE), "colWeightedMeans(X,w,rows,cols)" = colWeightedMeans(X, w=w, rows=rows, cols=cols, na.rm=FALSE), "colWeightedMeans(X[rows,cols],w[rows])" = colWeightedMeans(X[rows,cols], w=w[rows], na.rm=FALSE), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowWeightedMeans_X_w_S" = rowWeightedMeans(X_S, w=w_S, na.rm=FALSE), "rowWeightedMeans(X,w,cols,rows)" = rowWeightedMeans(X, w=w, rows=cols, cols=rows, na.rm=FALSE), "rowWeightedMeans(X[cols,rows],w[rows])" = rowWeightedMeans(X[cols,rows], w=w[rows], na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/varDiff.md.rsp0000644000175100001440000000243412726216471020741 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="varDiff"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-10"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=fcnname%>() against alternative methods. ## Alternative methods * N/A <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode=mode) data <- data[1:4] })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) x <- data[[dataLabel]] gc() %> ### <%=dataLabel%> vector #### All elements ```r <%=withCapture({ x <- data[[.dataLabel.]] stats <- microbenchmark( "varDiff" = varDiff(x), "var" = var(x), "diff" = diff(x), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel)) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-10 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/meanOver.md.rsp0000644000175100001440000000437512726216471021142 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="meanOver"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-02"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=fcnname%>() against alternative methods. ## Alternative methods * mean() + [() * mean.default() + [() - avoids method dispatching as below ```r <%=withCapture({ meanOver_R_v1 <- function(x, na.rm=FALSE, idxs) { mean(x[idxs], na.rm=na.rm) } })%> ``` and ```r <%=withCapture({ meanOver_R_v2 <- function(x, na.rm=FALSE, idxs) { mean.default(x[idxs], na.rm=na.rm) } })%> ``` <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode=mode) ##data <- data[1:3] })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) x <- data[[dataLabel]] gc() %> ### <%=dataLabel%> vector #### All elements ```r <%=withCapture({ x <- data[[.dataLabel.]] gc() stats <- microbenchmark( "meanOver" = meanOver(x, refine=TRUE), "meanOver_no_refine" = meanOver(x, refine=FALSE), "mean" = mean(x), "mean.default" = mean.default(x), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(dataLabel, "all")) %> <% for (subset in c(0.2, 0.4, 0.8)) { %> #### A <%=sprintf("%g", 100*subset)%>% subset ```r <%=withCapture({ x <- data[[.dataLabel.]] subset idxs <- sort(sample(length(x), size=subset*length(x), replace=FALSE)) gc() stats <- microbenchmark( "meanOver" = meanOver(x, idxs=idxs, refine=TRUE), "meanOver_no_refine" = meanOver(x, idxs=idxs, refine=FALSE), "mean+[()" = meanOver_R_v1(x, idxs=idxs), "mean.default+[()" = meanOver_R_v2(x, idxs=idxs), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel, subset)) %> <% } # for (subset in ...) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-02 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowMedians.md.rsp0000644000175100001440000000272512740227356022131 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colMedians"%> <%@string rowname="rowMedians"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-09"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + median() <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colMedians = colMedians(X, na.rm=FALSE), "apply+median" = apply(X, MARGIN=2L, FUN=median, na.rm=FALSE), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowMedians = rowMedians(X, na.rm=FALSE), "apply+median" = apply(X, MARGIN=1L, FUN=median, na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowCummins.md.rsp0000644000175100001440000000264112726216471022161 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colCummins"%> <%@string rowname="rowCummins"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-26"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + cummin() <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colCummins = colCummins(X), "apply+cummin" = apply(X, MARGIN=2L, FUN=cummin), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowCummins = rowCummins(X), "apply+cummin" = apply(X, MARGIN=1L, FUN=cummin), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-26 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowCumsums.md.rsp0000644000175100001440000000264112726216471022202 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colCumsums"%> <%@string rowname="rowCumsums"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-26"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + cumsum() <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colCumsums = colCumsums(X), "apply+cumsum" = apply(X, MARGIN=2L, FUN=cumsum), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowCumsums = rowCumsums(X), "apply+cumsum" = apply(X, MARGIN=1L, FUN=cumsum), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-26 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowProds_subset.md.rsp0000644000175100001440000000471412726216471023225 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colProds"%> <%@string rowname="rowProds"%> <%@string fcnname="colRowProds_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. ## Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode="double") })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] })%> <% gc() %> <%=withCapture({ colStats <- microbenchmark( "colProds_X_S w/ direct" = colProds(X_S, method="direct", na.rm=FALSE), "colProds_X_S w/ expSumLog" = colProds(X_S, method="expSumLog", na.rm=FALSE), "colProds(X,rows,cols) w/ direct" = colProds(X, rows=rows, cols=cols, method="direct", na.rm=FALSE), "colProds(X,rows,cols) w/ expSumLog" = colProds(X, rows=rows, cols=cols, method="expSumLog", na.rm=FALSE), "colProds(X[rows,cols]) w/ direct" = colProds(X[rows,cols], method="direct", na.rm=FALSE), "colProds(X[rows,cols]) w/ expSumLog" = colProds(X[rows,cols], method="expSumLog", na.rm=FALSE), unit = "ms" ) })%> <%=withCapture({ X <- t(X) X_S <- t(X_S) })%> <% gc() %> <%=withCapture({ rowStats <- microbenchmark( "rowProds_X_S w/ direct" = rowProds(X_S, method="direct", na.rm=FALSE), "rowProds_X_S w/ expSumLog" = rowProds(X_S, method="expSumLog", na.rm=FALSE), "rowProds(X,cols,rows) w/ direct" = rowProds(X, rows=cols, cols=rows, method="direct", na.rm=FALSE), "rowProds(X,cols,rows) w/ expSumLog" = rowProds(X, rows=cols, cols=rows, method="expSumLog", na.rm=FALSE), "rowProds(X[cols,rows]) w/ direct" = rowProds(X[cols,rows], method="direct", na.rm=FALSE), "rowProds(X[cols,rows]) w/ expSumLog" = rowProds(X[cols,rows], method="expSumLog", na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowAnyMissings.md.rsp0000644000175100001440000000347712726216471023022 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colAnyMissings"%> <%@string rowname="rowAnyMissings"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-09"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * colAnyMissings() and rowAnyMissings() * apply() + anyMissing() * colSums() + is.na() and rowSums() + is.na() where ```r <%=withCapture({ colAnyMissings <- function(x, ...) colAnys(x, value=NA) })%> ``` and ```r <%=withCapture({ rowAnyMissings <- function(x, ...) rowAnys(x, value=NA) })%> ``` <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colAnyMissings = colAnyMissings(X), "apply+anyMissing" = apply(X, MARGIN=2L, FUN=anyMissing), colSums = is.na(colSums(X, na.rm=FALSE)), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowAnyMissings = rowAnyMissings(X), "apply+anyMissing" = apply(X, MARGIN=1L, FUN=anyMissing), rowSums = is.na(rowSums(X, na.rm=FALSE)), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowMins.md.rsp0000644000175100001440000000355112726216471021455 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colMins"%> <%@string rowname="rowMins"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-09"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + min() * lapply() + pmin() * lapply() + pmin.int() See also [StackOverflow:colMins?]. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colMins = colMins(X, na.rm=FALSE), "apply+min" = apply(X, MARGIN=2L, FUN=min, na.rm=FALSE), "lapply+pmin" = do.call(pmin, lapply(seq_len(nrow(X)), function(i) X[i,])), "lapply+pmin.int" = do.call(pmin.int, lapply(seq_len(nrow(X)), function(i) X[i,])), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowMins = rowMins(X, na.rm=FALSE), "apply+min" = apply(X, MARGIN=1L, FUN=min, na.rm=FALSE), "lapply+pmin" = do.call(pmin, lapply(seq_len(ncol(X)), function(i) X[,i])), "lapply+pmin.int" = do.call(pmin.int, lapply(seq_len(ncol(X)), function(i) X[,i])), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowAnyMissings_subset.md.rsp0000644000175100001440000000341512726216471024377 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colAnyMissings"%> <%@string rowname="rowAnyMissings"%> <%@string fcnname="colRowAnyMissings_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-06"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() colStats <- microbenchmark( "colAnyMissings_X_S" = colAnyMissings(X_S), "colAnyMissings(X,rows,cols)" = colAnyMissings(X, rows=rows, cols=cols), "colAnyMissings(X[rows,cols])" = colAnyMissings(X[rows,cols]), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowAnyMissings_X_S" = rowAnyMissings(X_S), "rowAnyMissings(X,cols,rows)" = rowAnyMissings(X, rows=cols, cols=rows), "rowAnyMissings(X[cols,rows])" = rowAnyMissings(X[cols,rows]), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-06 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/includes/0000755000175100001440000000000012737057162020040 5ustar hornikusersmatrixStats/inst/benchmarking/includes/footer.md.rsp0000644000175100001440000000136312726216471022464 0ustar hornikusers<%--------------------------------------------------------------- Page footer ---------------------------------------------------------------%> <%@string appendix="${appendix}" default="false"%> <%@ifeq appendix="true"%> <%@include file="appendix.md.rsp"%> <%@endif%> <%@include file="${references}"%> --------------------------------------- Copyright <%@meta name="author"%>. Last updated on <%=format(Sys.time(), format="%Y-%m-%d %H:%M:%S (%z UTC)")%>. Powered by [RSP]. <%--------------------------------------------------------------- Dynamically insert an HTML favicon ---------------------------------------------------------------%> <%=toFavicon({ plot(1, col="blue", bg="yellow", pch=21, cex=4, lwd=4, axes=FALSE) }, force=FALSE)%> matrixStats/inst/benchmarking/includes/header.md.rsp0000644000175100001440000000011212726216471022405 0ustar hornikusers[matrixStats]: Benchmark report --------------------------------------- matrixStats/inst/benchmarking/includes/results.md.rsp0000644000175100001440000001167412726216471022675 0ustar hornikusers<%-------------------------------------------------------------- BENCHMARK RESULTS --------------------------------------------------------------%> <%-------------------------------------------------------------- Local functions --------------------------------------------------------------%> <% toImage <- function(stats, name=levels(stats$expr)[1L], tags=NULL, ylim="auto", col=NULL, alpha=NULL, ...) { %> ![](<%=toPNG(name, tags=c(tags, "benchmark"), aspectRatio=2/3, { if (identical(ylim, "auto")) { y <- stats$time/1e6 ymax <- max(y, na.rm=TRUE) y75 <- quantile(y, probs=0.75, na.rm=TRUE) yupper <- min(c(1.5*y75, ymax), na.rm=TRUE) ylim <- c(0, yupper) } if (!is.null(ylim)) { stats$outlier <- (stats$time > ylim[2]*1e6) stats$time[stats$outlier] <- ylim[2]*1e6 } gg <- ggplot(data=stats, aes(x=seq_along(time)/length(levels(expr)), y=time/1e6)) gg <- gg + geom_point(aes(colour=expr, shape=outlier)) gg <- gg + scale_shape_manual(values=c(16,4), guide="none") if (!is.null(col)) gg <- gg + scale_colour_manual(values=col) if (!is.null(alpha)) gg <- gg + scale_alpha_manual(values=alpha) gg <- gg + xlab("iteration") + ylab("time (ms)") if (!is.null(ylim)) gg <- gg + ylim(ylim) print(gg) })%>) <% } # toImage() %> <% toTable <- function(stats, tags=NULL, order="median", ...) { kable({ s <- summary(stats) s$neval <- NULL s$cld <- NULL s <- s[order(s[[order]]),] s }, row.names=TRUE) kable({ s <- summary(stats, unit="relative") s$neval <- NULL s$cld <- NULL s <- s[order(s[[order]]),] s }, row.names=TRUE) } %> <%-------------------------------------------------------------- Benchmark results for vector functions --------------------------------------------------------------%> <% benchmarkResults <- function(stats, tags=NULL, ...) { %> _Table: Benchmarking of <%=hpaste(sprintf("%s()", levels(stats$expr)), lastCollapse=" and ")%> on <%=paste(tags, collapse="+")%> data. The top panel shows times in milliseconds and the bottom panel shows relative times._ <% toTable(stats, tags=tags) %> _Figure: Benchmarking of <%=hpaste(sprintf("%s()", levels(stats$expr)), lastCollapse=" and ")%> on <%=paste(tags, collapse="+")%> data. Outliers are displayed as crosses. Times are in milliseconds._ <% toImage(stats, tags=tags) %> <% } # benchmarkResults() %> <%-------------------------------------------------------------- Benchmark results for col- and row-specific functions --------------------------------------------------------------%> <% crBenchmarkResults <- function(colStats, rowStats=NULL, tags=NULL, ...) { %> _Table: Benchmarking of <%=hpaste(sprintf("%s()", levels(colStats$expr)), lastCollapse=" and ")%> on <%=paste(tags, collapse="+")%> data. The top panel shows times in milliseconds and the bottom panel shows relative times._ <% toTable(colStats, tags=tags) %> _Table: Benchmarking of <%=hpaste(sprintf("%s()", levels(rowStats$expr)), lastCollapse=" and ")%> on <%=paste(tags, collapse="+")%> data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times._ <% if (!is.null(rowStats)) { toTable(rowStats, tags=tags) } %> _Figure: Benchmarking of <%=hpaste(sprintf("%s()", levels(colStats$expr)), lastCollapse=" and ")%> on <%=paste(tags, collapse="+")%> data <% if (!is.null(rowStats)) { %> as well as <%=hpaste(sprintf("%s()", levels(rowStats$expr)), lastCollapse=" and ")%> on the same data transposed<% } # if (!is.null(rowStats)) %>. Outliers are displayed as crosses. Times are in milliseconds._ <% y <- c(colStats$time, rowStats$time)/1e6 ymax <- max(y, na.rm=TRUE) y75 <- quantile(y, probs=0.75, na.rm=TRUE) yupper <- min(c(1.5*y75, ymax), na.rm=TRUE) ylim <- c(0, yupper) %> <% toImage(colStats, tags=tags, ylim=ylim) %> <% if (!is.null(rowStats)) toImage(rowStats, tags=tags, ylim=ylim) %> <% if (!is.null(rowStats)) { %> <% # Compare performance or the column- and the row-specific methods # for the "main" function. stats <- list(colStats, rowStats) stats <- lapply(stats, FUN=function(x) { level <- levels(x$expr)[1] x <- subset(x, expr %in% level) x$expr <- factor(as.character(x$expr)) x }) stats <- Reduce(rbind, stats) odd <- seq(from=1L, to=nrow(stats), by=2L) top <- 1:(nrow(stats)/2) stats0 <- stats stats[ odd,] <- stats0[ top,] stats[-odd,] <- stats0[-top,] %> _Table: Benchmarking of <%=hpaste(sprintf("%s()", levels(stats$expr)), lastCollapse=" and ")%> on <%=paste(tags, collapse="+")%> data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times._ <% toTable(stats, tags=tags) %> _Figure: Benchmarking of <%=hpaste(sprintf("%s()", levels(stats$expr)), lastCollapse=" and ")%> on <%=paste(tags, collapse="+")%> data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds._ <% toImage(stats, name=paste(levels(stats$expr), collapse="_vs_"), tags=tags, col=c("#000000", "#999999")) %> <% } # if (!is.null(rowStats)) %> <% } # crBenchmarkResults() %> matrixStats/inst/benchmarking/includes/setup.md.rsp0000644000175100001440000000236412726216471022330 0ustar hornikusers<%@string header="includes/header.md.rsp"%> <%@string footer="includes/footer.md.rsp"%> <%@string references="../includes/references.md.rsp"%> <%@string appendix="${appendix}" default="true"%> <%@string colname=""%> <%@string rowname=""%> <%@string fcnname=""%> <%@string fcntags=""%> <%@meta author="Henrik Bengtsson"%> <%-------------------------------------------------------------- RSP specific --------------------------------------------------------------%> <% R.utils::use("R.utils, R.devices (>= 2.12.0), knitr, ggplot2") devOptions("png", width=390) options("withCapture/newline"=FALSE) options(deparse.cutoff=100) kable <- function(...) { t <- knitr::kable(..., format="markdown") print(t) } %> <%-------------------------------------------------------------- Report/package specific --------------------------------------------------------------%> <% use("matrixStats") use("microbenchmark") %> <%@include file="results.md.rsp"%> <%-------------------------------------------------------------- Macros --------------------------------------------------------------%> <%-------------------------------------------------------------- Timing --------------------------------------------------------------%> <% rspStartTime <- Sys.time() %> matrixStats/inst/benchmarking/includes/references.md.rsp0000644000175100001440000000121212726216471023300 0ustar hornikusers<%--------------------------------------------------------------- REFERENCES ---------------------------------------------------------------%> [RSP]: http://cran.r-project.org/package=R.rsp [matrixStats]: http://cran.r-project.org/package=matrixStats [StackOverflow:colMins?]: http://stackoverflow.com/questions/13676878 "Stack Overflow: fastest way to get Min from every column in a matrix?" [StackOverflow:colSds?]: http://stackoverflow.com/questions/17549762 "Stack Overflow: Is there such 'colsd' in R?" [StackOverflow:rowProds?]: http://stackoverflow.com/questions/20198801/ "Stack Overflow: Row product of matrix and column sum of matrix" matrixStats/inst/benchmarking/includes/appendix.md.rsp0000644000175100001440000000067512726216471023003 0ustar hornikusers## Appendix ### Session information ```r <% print(sessionInfo()) %> ``` Total processing time was <%=rspDuration <- round(Sys.time()-rspStartTime, digits=2)%> <%=attr(rspDuration, "units")%>. ### Reproducibility To reproduce this report, do: ```r <%@ifeq fcnname=""%><%@string fcnname="${colname}"%><%@endif%> html <- matrixStats:::benchmark('<%@string name="fcnname"%>'<%@ifneq fcntags=""%>, tags='<%@string name="fcntags"%>'<%@endif%>) ``` matrixStats/inst/benchmarking/colRowOrderStats.md.rsp0000644000175100001440000000341612726216471022641 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colOrderStats"%> <%@string rowname="rowOrderStats"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-09"%> <%@include file="${header}"%> <% use("Biobase", how="load") rowQ <- Biobase::rowQ %> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + quantile(..., type=3L) * Biobase::rowQ() <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() probs <- 0.3 which <- round(probs*nrow(X)) colStats <- microbenchmark( colOrderStats = colOrderStats(X, which=which, na.rm=FALSE), "apply+quantile" = apply(X, MARGIN=2L, FUN=quantile, probs=probs, na.rm=FALSE, type=3L), "rowQ(t(X))" = rowQ(t(X), which=which), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowOrderStats = rowOrderStats(X, which=which, na.rm=FALSE), "apply+quantile" = apply(X, MARGIN=1L, FUN=quantile, probs=probs, na.rm=FALSE, type=3L), rowQ = rowQ(X, which=which), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/product_subset.md.rsp0000644000175100001440000000235312726216471022425 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="product_subset"%> <%@string subname="product"%> <%@meta title="${subname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=subname%>() on subsetted computation. ## Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode="double") data <- data[1:4] })%> ``` ## Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] message(dataLabel) %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] idxs <- sample.int(length(x), size=length(x)*0.7) x_S <- x[idxs] gc() stats <- microbenchmark( "product_x_S" = product(x_S, na.rm=FALSE), "product(x,idxs)" = product(x, idxs=idxs, na.rm=FALSE), "product(x[idxs])" = product(x[idxs], na.rm=FALSE), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=dataLabel) %> <% } # for (ii ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowAnys_subset.md.rsp0000644000175100001440000000305212726216471023042 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colAnys"%> <%@string rowname="rowAnys"%> <%@string fcnname="colRowAnys_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-06"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. ## Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode="logical") })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() colStats <- microbenchmark( "colAnys_X_S" = colAnys(X_S), "colAnys(X,rows,cols)" = colAnys(X, rows=rows, cols=cols), "colAnys(X[rows,cols])" = colAnys(X[rows,cols]), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowAnys_X_S" = rowAnys(X_S), "rowAnys(X,cols,rows)" = rowAnys(X, rows=cols, cols=rows), "rowAnys(X[cols,rows])" = rowAnys(X[cols,rows]), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-06 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowCounts.md.rsp0000644000175100001440000000335012726216471022017 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colCounts"%> <%@string rowname="rowCounts"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-09"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * colSums() and rowSums() * apply() + sum() <% for (mode in c("logical", "integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> #### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] value <- 42 })%> ``` <% gc() %> ```r <%=withCapture({ colStats <- microbenchmark( colCounts = colCounts(X, value=value, na.rm=FALSE), colSums = colSums(X == value, na.rm=FALSE), "apply+sum" = apply(X, MARGIN=2L, FUN=function(x) sum(x == value, na.rm=FALSE)), unit = "ms" ) })%> ``` ```r <%=withCapture({ X <- t(X) })%> ``` <% gc() %> ```r <%=withCapture({ rowStats <- microbenchmark( rowCounts = rowCounts(X, value=value, na.rm=FALSE), rowSums = rowSums(X == value, na.rm=FALSE), "apply+sum" = apply(X, MARGIN=1L, FUN=function(x) sum(x == value, na.rm=FALSE)), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowMads.md.rsp0000644000175100001440000000413512726216471021432 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colMads"%> <%@string rowname="rowMads"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-18"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + mad() * colMads2() and rowMads2() where `rowMads2()` and `colMads2()` are: ```r <%=withCapture({ rowMads2 <- function(x, const=1.4826, na.rm=FALSE) { mu <- rowMedians(x, na.rm=na.rm) x <- abs(x - mu) mad <- rowMedians(x, na.rm=FALSE) const * mad } colMads2 <- function(x, const=1.4826, na.rm=FALSE) { mu <- colMedians(x, na.rm=na.rm) x <- abs(x - mu) mad <- colMedians(x, na.rm=FALSE) const * mad } })%> ``` <% rowMads_R <- function(x, na.rm=FALSE) { apply(x, MARGIN=1L, FUN=mad, na.rm=na.rm) } colMads_R <- function(x, na.rm=FALSE) { apply(x, MARGIN=2L, FUN=mad, na.rm=na.rm) } %> <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colMads = colMads(X, na.rm=FALSE), colMads2 = colMads2(X, na.rm=FALSE), "apply+mad" = apply(X, MARGIN=2L, FUN=mad, na.rm=FALSE), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowMads = rowMads(X, na.rm=FALSE), rowMads2 = rowMads2(X, na.rm=FALSE), "apply+mad" = apply(X, MARGIN=1L, FUN=mad, na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-17 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/index.md.rsp0000644000175100001440000000362712726216471020474 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@meta title="Benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-05"%> <%@include file="${header}"%> <% use("matrixStats") use("R.utils (>= 1.34.0)") # Simple logging function lenter <- function(...) { s <- mprintf(...) s <- gsub("[.][.][.](\n)*", "...done\\1", s) lexit <<- function() mprintf(s) } %> # <%@meta name="title"%> List of benchmark report for some of the functions available in the [matrixStats] package. <% path <- cmdArg(path=system.file("benchmarking", package="matrixStats")) path <- getAbsolutePath(path) message("Processing benchmark report directory: ", path) pattern <- "[.]md[.]rsp$" filenames <- list.files(path=path, pattern=pattern) filenames <- setdiff(filenames, "index.md.rsp") names <- gsub(pattern, "", filenames) # col- and rowAnyMissing() does not really exist names <- setdiff(names, c("colAnyMissing", "rowAnyMissing")) message("Number of reports found: ", length(names)) mprintf("Report #%d: %s\n", seq_along(names), names) %> <% for (ii in seq_along(names)) { %> <% name <- names[ii] if (regexpr("^colRow", name) != -1L) { label <- gsub("^colRow", "", name) label <- sprintf("col%s() and row%s()", label, label) } else { label <- sprintf("%s()", name) } %> * [<%=label%>](<%={ lenter("%d of %d. Benchmarking %s...\n", ii, length(names), label) html <- sprintf("%s.html", name) if (!file_test("-f", html)) { html <- matrixStats:::benchmark(name, path=path, workdir=".", envir=new.env()) html <- getRelativePath(html) gc() } lexit() html }%>) <% } # for (ii ...) %> ## Appendix To reproduce this page and all of its reports, do: ```r path <- system.file("benchmarking", package="matrixStats") R.rsp::rfile("index.md.rsp", path=path) ``` _Note: Each of the above reports takes up to several minutes to complete._ <%@string appendix="false"%> <%@include file="${footer}"%> <%@string appendix="true"%> matrixStats/inst/benchmarking/sumOver.md.rsp0000644000175100001440000000351312726216471021017 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="sumOver"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-02"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=fcnname%>() against alternative methods. ## Alternative methods * sum() + [() as below ```r <%=withCapture({ sumOver_R <- function(x, na.rm=FALSE, idxs) { sum(x[idxs], na.rm=na.rm) } })%> ``` <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode=mode) ##data <- data[1:3] })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) x <- data[[dataLabel]] gc() %> ### <%=dataLabel%> vector #### All elements ```r <%=withCapture({ x <- data[[.dataLabel.]] gc() stats <- microbenchmark( "sumOver" = sumOver(x), "sum" = sum(x), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(dataLabel, "all")) %> <% for (subset in c(0.2, 0.4, 0.8)) { %> #### A <%=sprintf("%g", 100*subset)%>% subset ```r <%=withCapture({ x <- data[[.dataLabel.]] subset idxs <- sort(sample(length(x), size=subset*length(x), replace=FALSE)) gc() stats <- microbenchmark( "sumOver" = sumOver(x, idxs=idxs), "sum+[()" = sumOver_R(x, idxs=idxs), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel, subset)) %> <% } # for (subset in ...) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-02 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/binMeans_subset.md.rsp0000644000175100001440000000315312726216471022500 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="binMeans"%> <%@string subname="binMeans"%> <%@meta title="${subname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2014-06-05"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=subname%>() on subsetted computation. ## Results ### Non-sorted simulated data ```r <%=withCapture({ nx <- 100e3 # Number of data points set.seed(0xBEEF) x <- runif(nx, min=0, max=1) y <- runif(nx, min=0, max=1) # Uniformely distributed bins nb <- 1e3 # Number of bins bx <- seq(from=0, to=1, length.out=nb+1L) bx <- c(-1, bx, 2) # indices for subsetting idxs <- sample.int(length(x), size=length(x)*0.7) })%> ``` <% benchmark <- function() { %> <% dataLabel <- if (is.unsorted(x)) "unsorted" else "sorted" %> <% message(dataLabel) %> ```r <%=withCapture({ x_S <- x[idxs] y_S <- y[idxs] gc() stats <- microbenchmark( "binMeans_x_y_S" = binMeans(x=x_S, y=y_S, bx=bx, count=TRUE), "binMeans(x,y,idxs)" = binMeans(x=x, y=y, idxs=idxs, bx=bx, count=TRUE), "binMeans(x[idxs],y[idxs])" = binMeans(x=x[idxs], y=y[idxs], bx=bx, count=TRUE), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=dataLabel) %> <% } # benchmark() %> <% benchmark() %> ### Sorted simulated data ```r <%=withCapture({ x <- sort(x) idxs <- sort(idxs) })%> ``` <% benchmark() %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-05 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowDiffs_subset.md.rsp0000644000175100001440000000327612726216471023173 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colDiffs"%> <%@string rowname="rowDiffs"%> <%@string fcnname="colRowDiffs_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-06"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() colStats <- microbenchmark( "colDiffs_X_S" = colDiffs(X_S), "colDiffs(X,rows,cols)" = colDiffs(X, rows=rows, cols=cols), "colDiffs(X[rows,cols])" = colDiffs(X[rows,cols]), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowDiffs_X_S" = rowDiffs(X_S), "rowDiffs(X,cols,rows)" = rowDiffs(X, rows=cols, cols=rows), "rowDiffs(X[cols,rows])" = rowDiffs(X[cols,rows]), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-06 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowCounts_subset.md.rsp0000644000175100001440000000371612726216471023412 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colCounts"%> <%@string rowname="rowCounts"%> <%@string fcnname="colRowCounts_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-04-18"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. <% for (mode in c("logical", "integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> #### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] value <- 42 })%> ``` <% gc() %> ```r <%=withCapture({ colStats <- microbenchmark( "colCounts_X_S" = colCounts(X_S, value=value, na.rm=FALSE), "colCounts(X,rows,cols)" = colCounts(X, value=value, na.rm=FALSE, rows=rows, cols=cols), "colCounts(X[rows,cols])" = colCounts(X[rows,cols], value=value, na.rm=FALSE), unit = "ms" ) })%> ``` ```r <%=withCapture({ X <- t(X) X_S <- t(X_S) })%> ``` <% gc() %> ```r <%=withCapture({ rowStats <- microbenchmark( "rowCounts_X_S" = rowCounts(X_S, value=value, na.rm=FALSE), "rowCounts(X,cols,rows)" = rowCounts(X, value=value, na.rm=FALSE, rows=cols, cols=rows), "rowCounts(X[cols,rows])" = rowCounts(X[cols,rows], value=value, na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-04-18 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowWeightedMedians_subset.md.rsp0000644000175100001440000000363212726216471025175 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colWeightedMedians"%> <%@string rowname="rowWeightedMedians"%> <%@string fcnname="colRowWeightedMedians_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%> on subsetted computation. ## Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode="double") })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] w <- runif(nrow(X)) w_S <- w[rows] gc() colStats <- microbenchmark( "colWeightedMedians_X_w_S" = colWeightedMedians(X_S, w=w_S, na.rm=FALSE), "colWeightedMedians(X,w,rows,cols)" = colWeightedMedians(X, w=w, rows=rows, cols=cols, na.rm=FALSE), "colWeightedMedians(X[rows,cols],w[rows])" = colWeightedMedians(X[rows,cols], w=w[rows], na.rm=FALSE), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowWeightedMedians_X_w_S" = rowWeightedMedians(X_S, w=w_S, na.rm=FALSE), "rowWeightedMedians(X,w,cols,rows)" = rowWeightedMedians(X, w=w, rows=cols, cols=rows, na.rm=FALSE), "rowWeightedMedians(X[cols,rows],w[rows])" = rowWeightedMedians(X[cols,rows], w=w[rows], na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/product.md.rsp0000644000175100001440000000321712726216471021040 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="product"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-04"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=fcnname%>() against alternative methods. ## Alternative methods * product_R() * prod() where ```r <%=withCapture({ product_R <- function(x, na.rm=FALSE, ...) { # Nothing todo? if (length(x) == 0L) return(0); # Any missing values? if (na.rm) { x <- x[!is.na(x)]; } # Any zeros? if (is.integer(x) && any(x == 0)) return(0); # Calculate product via logarithmic sum sign <- if (sum(x < 0) %% 2 == 0) +1 else -1; x <- abs(x); x <- log(x); x <- sum(x, na.rm=FALSE); x <- exp(x); y <- sign*x; y; } # product_R() })%> ``` ## Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode="double") data <- data[1:4] })%> ``` ## Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] message(dataLabel) x <- data[[dataLabel]] gc() %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] gc() stats <- microbenchmark( product = product(x, na.rm=FALSE), product_R = product_R(x, na.rm=FALSE), prod = prod(x, na.rm=FALSE), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=dataLabel) %> <% } # for (ii ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-02 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/indexByRow.md.rsp0000644000175100001440000000551612726216471021456 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="indexByRow"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-09"%> <%@include file="${header}"%> <% lfun <- local({ locals <- list() function(txt=NULL) { if (is.null(txt)) return(locals) local <- list(txt) locals <<- c(locals, local) } }) %> # <%@meta name="title"%> This report benchmark the performance of `<%=fcnname%>()` against alternative methods: * `indexByRow_R1()` based in `matrix(..., byrow=TRUE)` * `indexByRow_R2()` is a modified version of `indexByRow_R1()` where `indexByRow_R1()` and `indexByRow_R2()` are defined as in the Appendix. <% lfun(withCapture({ indexByRow_R1 <- function(dim, idxs=NULL, ...) { n <- prod(dim) x <- matrix(seq_len(n), nrow=dim[2L], ncol=dim[1L], byrow=TRUE) if (!is.null(idxs)) x <- x[idxs] as.vector(x) } # indexByRow_R1() })) lfun(withCapture({ indexByRow_R2 <- function(dim, idxs=NULL, ...) { n <- prod(dim) if (is.null(idxs)) { x <- matrix(seq_len(n), nrow=dim[2L], ncol=dim[1L], byrow=TRUE) as.vector(x) } else { idxs <- idxs - 1 cols <- idxs %/% dim[2L] rows <- idxs %% dim[2L] cols + dim[1L]*rows + 1L } } # indexByRow_R2() })) %> ## Data <% lfun(withCapture({ <%@include file="R/random-matrices.R"%> })) %> ```r <%=withCapture({ data <- rmatrices(mode="index") })%> ``` where `rmatrices()` is defined in the Appendix. <% # data <- data[1:2] %> ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] dim <- dim(X) idxsList <- list( 'all-by-NULL' = NULL, all = seq_len(prod(dim)), odd = seq(from=1, to=prod(dim), by=2L) ) str(idxsList) })%> ``` <% for (ii in seq_along(idxsList)) { %> #### Index set '<%=names(idxsList)[ii]%>' <% idxs <- idxsList[[ii]] idxsTag <- names(idxsList)[ii] # Validate correctness res <- list( indexByRow = indexByRow(dim, idxs=idxs), indexByRow_R1 = indexByRow_R1(dim, idxs=idxs), indexByRow_R2 = indexByRow_R2(dim, idxs=idxs) ) lapply(res, FUN=function(x) stopifnot(all.equal(x, res[[1]]))) gc() %> ```r <%=withCapture({ stats <- microbenchmark( indexByRow = indexByRow(dim, idxs=idxs), indexByRow_R1 = indexByRow_R1(dim, idxs=idxs), indexByRow_R2 = indexByRow_R2(dim, idxs=idxs), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(dataLabel, idxsTag)) %> <% } # for (ii ...) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> ### Local functions ```r <%=lfun()[[1]]%> ``` ```r <%=lfun()[[2]]%> ``` ```r <%=lfun()[[3]]%> ``` <%--------------------------------------------------------------------------- HISTORY: 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/R/0000755000175100001440000000000012726216471016431 5ustar hornikusersmatrixStats/inst/benchmarking/R/random-matrices.R0000644000175100001440000000226212726216471021643 0ustar hornikusersrmatrix <- function(nrow, ncol, mode=c("logical", "double", "integer", "index"), range=c(-100,+100), naProb=0) { mode <- match.arg(mode) n <- nrow*ncol if (mode == "logical") { X <- sample(c(FALSE, TRUE), size=n, replace=TRUE) } else if (mode == "index") { X <- seq_len(n) mode <- "integer"; } else { X <- runif(n, min=range[1], max=range[2]) } storage.mode(X) <- mode if (naProb > 0) X[sample(n, size=naProb*n)] <- NA dim(X) <- c(nrow, ncol) X } # rmatrix() rmatrices <- function(scale=10, seed=1, ...) { set.seed(seed) data <- list() data[[1]] <- rmatrix(nrow=scale* 1, ncol=scale* 1, ...) data[[2]] <- rmatrix(nrow=scale* 10, ncol=scale* 10, ...) data[[3]] <- rmatrix(nrow=scale*100, ncol=scale* 1, ...) data[[4]] <- t(data[[3]]) data[[5]] <- rmatrix(nrow=scale* 10, ncol=scale*100, ...) data[[6]] <- t(data[[5]]) names(data) <- sapply(data, FUN=function(x) paste(dim(x), collapse="x")) data } # rmatrices() ############################################################################ # HISTORY: # 2014-11-09 # o Added 'index' mode. # 2014-06-02 # o Created. ############################################################################ matrixStats/inst/benchmarking/R/random-vectors.R0000644000175100001440000000163012726216471021517 0ustar hornikusersrvector <- function(n, mode=c("logical", "double", "integer"), range=c(-100,+100), naProb=0) { mode <- match.arg(mode) if (mode == "logical") { X <- sample(c(FALSE, TRUE), size=n, replace=TRUE) } else { x <- runif(n, min=range[1], max=range[2]) } storage.mode(x) <- mode if (naProb > 0) x[sample(n, size=naProb*n)] <- NA x } # rvector() rvectors <- function(scale=10, seed=1, ...) { set.seed(seed) data <- list() data[[1]] <- rvector(n=scale*1e2, ...) data[[2]] <- rvector(n=scale*1e3, ...) data[[3]] <- rvector(n=scale*1e4, ...) data[[4]] <- rvector(n=scale*1e5, ...) data[[5]] <- rvector(n=scale*1e6, ...) names(data) <- sprintf("n=%d", sapply(data, FUN=length)) data } # rvectors() ############################################################################ # HISTORY: # 2014-06-04 # o Created. ############################################################################ matrixStats/inst/benchmarking/colRowDiffs.md.rsp0000644000175100001440000000310512726216471021575 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colDiffs"%> <%@string rowname="rowDiffs"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-12-30"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + diff() * apply() + diff2() * diff() <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colDiffs = colDiffs(X), "apply+diff" = apply(X, MARGIN=2L, FUN=diff), "apply+diff2" = apply(X, MARGIN=2L, FUN=diff2), diff = diff(X), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowDiffs = rowDiffs(X), "apply+diff" = apply(X, MARGIN=1L, FUN=diff), "apply+diff2" = apply(X, MARGIN=1L, FUN=diff2), "diff + t" = diff(t(X)), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-17 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/meanOver_subset.md.rsp0000644000175100001440000000312412726216471022516 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="meanOver_subset"%> <%@string subname="meanOver"%> <%@meta title="${subname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=subname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode=mode) ##data <- data[1:3] })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] idxs <- sample.int(length(x), size=length(x)*0.7) x_S <- x[idxs] gc() stats <- microbenchmark( "meanOver_x_S" = meanOver(x_S, refine=TRUE), "meanOver_x_S_no_refine" = meanOver(x_S, refine=FALSE), "meanOver(x,idxs)" = meanOver(x, idxs=idxs, refine=TRUE), "meanOver_no_refine(x,idxs)" = meanOver(x, idxs=idxs, refine=FALSE), "meanOver(x[idxs])" = meanOver(x[idxs], refine=TRUE), "meanOver_no_refine(x[idxs])" = meanOver(x[idxs], refine=FALSE), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel)) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowMedians_subset.md.rsp0000644000175100001440000000345112740227356023513 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colMedians"%> <%@string rowname="rowMedians"%> <%@string fcnname="colRowMedians_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-06"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() colStats <- microbenchmark( "colMedians_X_S" = colMedians(X_S, na.rm=FALSE), "colMedians(X,rows,cols)" = colMedians(X, rows=rows, cols=cols, na.rm=FALSE), "colMedians(X[rows,cols])" = colMedians(X[rows,cols], na.rm=FALSE), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowMedians_X_S" = rowMedians(X_S, na.rm=FALSE), "rowMedians(X,cols,rows)" = rowMedians(X, rows=cols, cols=rows, na.rm=FALSE), "rowMedians(X[cols,rows])" = rowMedians(X[cols,rows], na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-06 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/anyMissing.md.rsp0000644000175100001440000000254412726216471021503 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="anyMissing"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-01"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=fcnname%>() against alternative methods. ## Alternative methods * anyNA() * any() + is.na() as below ```r <%=withCapture({ any_is.na <- function(x) { any(is.na(x)) } })%> ``` <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode=mode) })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] gc() stats <- microbenchmark( "anyMissing" = anyMissing(x), "anyNA" = anyNA(x), "any_is.na" = any_is.na(x), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel)) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-01 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowCummins_subset.md.rsp0000644000175100001440000000333312726216471023545 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colCummins"%> <%@string rowname="rowCummins"%> <%@string fcnname="colRowCummins_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-06"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() colStats <- microbenchmark( "colCummins_X_S" = colCummins(X_S), "colCummins(X,rows,cols)" = colCummins(X, rows=rows, cols=cols), "colCummins(X[rows,cols])" = colCummins(X[rows,cols]), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowCummins_X_S" = rowCummins(X_S), "rowCummins(X,cols,rows)" = rowCummins(X, rows=cols, cols=rows), "rowCummins(X[cols,rows])" = rowCummins(X[cols,rows]), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-06 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowCumprods.md.rsp0000644000175100001440000000267312726216471022347 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colCumprods"%> <%@string rowname="rowCumprods"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-26"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + cumprod() <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode, range=c(-1,1)) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colCumprods = colCumprods(X), "apply+cumprod" = apply(X, MARGIN=2L, FUN=cumprod), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowCumprods = rowCumprods(X), "apply+cumprod" = apply(X, MARGIN=1L, FUN=cumprod), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-26 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowTabulates.md.rsp0000644000175100001440000000233612726216471022473 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colTabulates"%> <%@string rowname="rowTabulates"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-09"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * ??? ## Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode="integer", range=c(-10,10)) })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colTabulates = colTabulates(X, na.rm=FALSE), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowTabulates = rowTabulates(X, na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowRanges.md.rsp0000644000175100001440000000272712726216471021772 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colRanges"%> <%@string rowname="rowRanges"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-09"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + range() <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colRanges = colRanges(X, na.rm=FALSE), "apply+range" = apply(X, MARGIN=2L, FUN=range, na.rm=FALSE), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowRanges = rowRanges(X, na.rm=FALSE), "apply+range" = apply(X, MARGIN=1L, FUN=range, na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowRanks_subset.md.rsp0000644000175100001440000000341412726216471023210 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colRanks"%> <%@string rowname="rowRanks"%> <%@string fcnname="colRowRanks_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() colStats <- microbenchmark( "colRanks_X_S" = colRanks(X_S, na.rm=FALSE), "colRanks(X,rows,cols)" = colRanks(X, rows=rows, cols=cols, na.rm=FALSE), "colRanks(X[rows,cols])" = colRanks(X[rows,cols], na.rm=FALSE), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowRanks_X_S" = rowRanks(X_S, na.rm=FALSE), "rowRanks(X,cols,rows)" = rowRanks(X, rows=cols, cols=rows, na.rm=FALSE), "rowRanks(X[cols,rows])" = rowRanks(X[cols,rows], na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/x_OP_y_subset.md.rsp0000644000175100001440000000333312726216471022141 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="x_OP_y_subset"%> <%@string subname="x_OP_y"%> <%@meta title="${subname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=subname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] y <- x[,1L] xrows <- sample.int(nrow(x), size=nrow(x)*0.7) xcols <- sample.int(ncol(x), size=ncol(x)*0.7) x_S <- x[xrows,xcols] yidxs <- xrows y_S <- y[yidxs] })%> ``` <% for (OP in c("+", "-", "*", "/")) { %> <% OPTag <- c("+"="add", "-"="sub", "*"="mul", "/"="div")[OP] gc() %> ```r <%=withCapture({ OP stats <- microbenchmark( "x_OP_y_x_y_S" = x_OP_y(x_S, y_S, OP=OP, na.rm=FALSE), "x_OP_y(x,y,OP,xrows,xcols,yidxs)" = x_OP_y(x, y, OP=OP, xrows=xrows, xcols=xcols, yidxs=yidxs, na.rm=FALSE), "x_OP_y(x[xrows,xcols],y[yidxs],OP)" = x_OP_y(x[xrows,xcols], y[yidxs], OP=OP, na.rm=FALSE), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel, OPTag)) %> <% } # for (OP ...) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/allocVector.md.rsp0000644000175100001440000000415412726216471021636 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="allocVector"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-09"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=fcnname%>() against alternative methods. ## Alternative methods * vector() + assignment * rep() * matrix() + as.vector() where ```r <%=withCapture({ allocVector_R1 <- function(length, value=NA) { x <- vector(mode=typeof(value), length=length) if (!is.finite(value) || value != 0) x[] <- value x } # allocVector_R1() allocVector_R2 <- function(length, value=NA) { x <- matrix(data=value, nrow=length, ncol=1L) as.vector(x) } # allocVector_R2() })%> ``` <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode=mode) values <- list(zero=0, one=1, "NA"=NA_real_) if (mode != "double") values <- lapply(values, FUN=function(x) { storage.mode(x) <- mode; x }) })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] x <- data[[dataLabel]] gc() %> ### <%=dataLabel%> matrix <% for (value in values) { %> <% valueLabel <- as.character(value) mprintf("%s: %s, value=%s\n", mode, dataLabel, valueLabel) %> ```r <%=withCapture({ n <- length(data[[.dataLabel.]]) str(value) })%> ``` <% gc() %> ```r <%=withCapture({ stats <- microbenchmark( "allocVector" = allocVector(length=n, value=value), "rep" = rep(value, times=n), "allocVector_R1" = allocVector_R1(length=n, value=value), "allocVector_R2" = allocVector_R2(length=n, value=value), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel, valueLabel)) %> <% } # for (value in values) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-01 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowLogSumExps_subset.md.rsp0000644000175100001440000000332012726216471024174 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colLogSumExps"%> <%@string rowname="rowLogSumExps"%> <%@string fcnname="colRowLogSumExps_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-06"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. ## Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode="double") })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() colStats <- microbenchmark( "colLogSumExps_X_S" = colLogSumExps(X_S, na.rm=FALSE), "colLogSumExps(X,rows,cols)" = colLogSumExps(X, rows=rows, cols=cols, na.rm=FALSE), "colLogSumExps(X[rows,cols])" = colLogSumExps(X[rows,cols], na.rm=FALSE), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowLogSumExps_X_S" = rowLogSumExps(X_S, na.rm=FALSE), "rowLogSumExps(X,cols,rows)" = rowLogSumExps(X, rows=cols, cols=rows, na.rm=FALSE), "rowLogSumExps(X[cols,rows])" = rowLogSumExps(X[cols,rows], na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-06 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowMeans.md.rsp0000644000175100001440000000313612726216471021611 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colMeans"%> <%@string rowname="rowMeans"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-09"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * .colMeans() and .rowMeans() * apply() + mean() <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colMeans = colMeans(X, na.rm=FALSE), .colMeans = .colMeans(X, m=nrow(X), n=ncol(X), na.rm=FALSE), "apply+mean" = apply(X, MARGIN=2L, FUN=mean, na.rm=FALSE), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowMeans = rowMeans(X, na.rm=FALSE), .rowMeans = .rowMeans(X, m=nrow(X), n=ncol(X), na.rm=FALSE), "apply+mean" = apply(X, MARGIN=1L, FUN=mean, na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/weightedMean_subset.md.rsp0000644000175100001440000000274012726216471023346 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="weightedMean_subset"%> <%@string subname="weightedMean"%> <%@meta title="${subname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=subname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode=mode) data <- data[1:4] })%> ``` ## Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] idxs <- sample.int(length(x), size=length(x)*0.7) x_S <- x[idxs] w <- runif(length(x)) w_S <- w[idxs] gc() stats <- microbenchmark( "weightedMean_x_w_S" = weightedMean(x_S, w=w_S, na.rm=FALSE), "weightedMean(x,w,idxs)" = weightedMean(x, w=w, idxs=idxs, na.rm=FALSE), "weightedMean(x[idxs],w[idxs])" = weightedMean(x[idxs], w=w[idxs], na.rm=FALSE), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel)) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/t_tx_OP_y.md.rsp0000644000175100001440000000406512726216471021266 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="t_tx_OP_y"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-26"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=fcnname%>() against alternative methods. ## Alternative methods * t_tx_OP_y_R() as below ```r <%=withCapture({ t_tx_OP_y_R <- function(x, y, OP, na.rm=FALSE) { x <- t(x) if (na.rm) { xnok <- is.na(x) ynok <- is.na(y) anok <- xnok & ynok unit <- switch(OP, "+" = 0, "-" = NA_real_, "*" = 1, "/" = NA_real_, stop("Unknown 'OP' operator: ", OP) ) x[xnok] <- unit y[ynok] <- unit } ans <- switch(OP, "+" = x + y, "-" = x - y, "*" = x * y, "/" = x / y, stop("Unknown 'OP' operator: ", OP) ) if (na.rm) { ans[anok] <- NA_real_ } t(ans) } # t_tx_OP_y_R() })%> ``` <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) x <- data[[dataLabel]] gc() %> ### <%=dataLabel%> vector #### All elements ```r <%=withCapture({ x <- data[[.dataLabel.]] y <- x[,1L] })%> ``` <% for (OP in c("+", "-", "*", "/")) { %> <% OPTag <- c("+"="add", "-"="sub", "*"="mul", "/"="div")[OP] gc() %> ```r <%=withCapture({ OP stats <- microbenchmark( "t_tx_OP_y" = t_tx_OP_y(x, y, OP=OP, na.rm=FALSE), "t_tx_OP_y_R" = t_tx_OP_y_R(x, y, OP=OP, na.rm=FALSE), unit = "ms" ) gc() })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel, OPTag)) %> <% } # for (OP ...) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-26 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowMins_subset.md.rsp0000644000175100001440000000337512726216471023046 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colMins"%> <%@string rowname="rowMins"%> <%@string fcnname="colRowMins_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-06"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() colStats <- microbenchmark( "colMins_X_S" = colMins(X_S, na.rm=FALSE), "colMins(X,rows,cols)" = colMins(X, rows=rows, cols=cols, na.rm=FALSE), "colMins(X[rows,cols])" = colMins(X[rows,cols], na.rm=FALSE), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowMins_X_S" = rowMins(X_S, na.rm=FALSE), "rowMins(X,cols,rows)" = rowMins(X, rows=cols, cols=rows, na.rm=FALSE), "rowMins(X[cols,rows])" = rowMins(X[cols,rows], na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/madDiff.md.rsp0000644000175100001440000000243412726216471020712 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="madDiff"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-10"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=fcnname%>() against alternative methods. ## Alternative methods * N/A <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode=mode) data <- data[1:4] })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) x <- data[[dataLabel]] gc() %> ### <%=dataLabel%> vector #### All elements ```r <%=withCapture({ x <- data[[.dataLabel.]] stats <- microbenchmark( "madDiff" = madDiff(x), "mad" = mad(x), "diff" = diff(x), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel)) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-10 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowTabulates_subset.md.rsp0000644000175100001440000000332312726216471024055 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colTabulates"%> <%@string rowname="rowTabulates"%> <%@string fcnname="colRowTabulates_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. ## Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode="integer", range=c(-10,10)) })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() colStats <- microbenchmark( "colTabulates_X_S" = colTabulates(X_S, na.rm=FALSE), "colTabulates(X,rows,cols)" = colTabulates(X, rows=rows, cols=cols, na.rm=FALSE), "colTabulates(X[rows,cols])" = colTabulates(X[rows,cols], na.rm=FALSE), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowTabulates_X_S" = rowTabulates(X_S, na.rm=FALSE), "rowTabulates(X,cols,rows)" = rowTabulates(X, rows=cols, cols=rows, na.rm=FALSE), "rowTabulates(X[cols,rows])" = rowTabulates(X[cols,rows], na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowQuantiles_subset.md.rsp0000644000175100001440000000346412726216471024104 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colQuantiles"%> <%@string rowname="rowQuantiles"%> <%@string fcnname="colRowQuantiles_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. ## Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode="double") })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() probs <- seq(from=0, to=1, by=0.25) colStats <- microbenchmark( "colQuantiles_X_S" = colQuantiles(X_S, probs=probs, na.rm=FALSE), "colQuantiles(X,rows,cols)" = colQuantiles(X, rows=rows, cols=cols, probs=probs, na.rm=FALSE), "colQuantiles(X[rows,cols])" = colQuantiles(X[rows,cols], probs=probs, na.rm=FALSE), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowQuantiles_X_S" = rowQuantiles(X_S, probs=probs, na.rm=FALSE), "rowQuantiles(X,cols,rows)" = rowQuantiles(X, rows=cols, cols=rows, probs=probs, na.rm=FALSE), "rowQuantiles(X[cols,rows])" = rowQuantiles(X[cols,rows], probs=probs, na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowQuantiles.md.rsp0000644000175100001440000000267612726216471022523 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colQuantiles"%> <%@string rowname="rowQuantiles"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-09"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + quantile() ## Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode="double") })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() probs <- seq(from=0, to=1, by=0.25) colStats <- microbenchmark( colQuantiles = colQuantiles(X, probs=probs, na.rm=FALSE), "apply+quantile" = apply(X, MARGIN=2L, FUN=quantile, probs=probs, na.rm=FALSE), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowQuantiles = rowQuantiles(X, probs=probs, na.rm=FALSE), "apply+quantile" = apply(X, MARGIN=1L, FUN=quantile, probs=probs, na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/weightedMean.md.rsp0000644000175100001440000000305412726216471021760 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="weightedMean"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-12-09"%> <%@include file="${header}"%> <% library("stats") weighted.mean.default <- stats:::weighted.mean.default %> # <%@meta name="title"%> This report benchmark the performance of <%=fcnname%>() against alternative methods. ## Alternative methods * stats::weighted.mean() * stats:::weighted.mean.default() <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode=mode) data <- data[1:4] })%> ``` ## Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) x <- data[[dataLabel]] gc() %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] w <- runif(length(x)) gc() stats <- microbenchmark( "weightedMean" = weightedMean(x, w=w, na.rm=FALSE), "stats::weighted.mean" = weighted.mean(x, w=w, na.rm=FALSE), "stats:::weighted.mean.default" = weighted.mean.default(x, w=w, na.rm=FALSE), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel)) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-12-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowWeightedMeans.md.rsp0000644000175100001440000000267412726216471023300 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colWeightedMeans"%> <%@string rowname="rowWeightedMeans"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-09"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + weighted.mean() ## Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode="double") })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] w <- runif(nrow(X)) gc() colStats <- microbenchmark( colWeightedMeans = colWeightedMeans(X, w=w, na.rm=FALSE), "apply+weigthed.mean" = apply(X, MARGIN=2L, FUN=weighted.mean, w=w, na.rm=FALSE), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowWeightedMeans = rowWeightedMeans(X, w=w, na.rm=FALSE), "apply+weigthed.mean" = apply(X, MARGIN=1L, FUN=weighted.mean, w=w, na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowVars_subset.md.rsp0000644000175100001440000000337612726216471023054 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colVars"%> <%@string rowname="rowVars"%> <%@string fcnname="colRowVars_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() colStats <- microbenchmark( "colVars_X_S" = colVars(X_S, na.rm=FALSE), "colVars(X,rows,cols)" = colVars(X, rows=rows, cols=cols, na.rm=FALSE), "colVars(X[rows,cols])" = colVars(X[rows,cols], na.rm=FALSE), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowVars_X_S" = rowVars(X_S, na.rm=FALSE), "rowVars(X,cols,rows)" = rowVars(X, rows=cols, cols=rows, na.rm=FALSE), "rowVars(X[cols,rows])" = rowVars(X[cols,rows], na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/count.md.rsp0000644000175100001440000000240312726216471020504 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="count"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-12-08"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=fcnname%>() against alternative methods. ## Alternative methods * sum(x == value) <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode=mode) })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) x <- data[[dataLabel]] value <- 42 gc() %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] gc() stats <- microbenchmark( "count" = count(x, value), "sum(x == value)" = sum(x == value), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel)) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-01 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowCumprods_subset.md.rsp0000644000175100001440000000337212726216471023731 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colCumprods"%> <%@string rowname="rowCumprods"%> <%@string fcnname="colRowCumprods_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-06"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode, range=c(-1,1)) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() colStats <- microbenchmark( "colCumprods_X_S" = colCumprods(X_S), "colCumprods(X,rows,cols)" = colCumprods(X, rows=rows, cols=cols), "colCumprods(X[rows,cols])" = colCumprods(X[rows,cols]), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowCumprods_X_S" = rowCumprods(X_S), "rowCumprods(X,cols,rows)" = rowCumprods(X, rows=cols, cols=rows), "rowCumprods(X[cols,rows])" = rowCumprods(X[cols,rows]), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-06 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/weightedMedian.md.rsp0000644000175100001440000000431512726216471022276 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="weightedMedian"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-03"%> <%@include file="${header}"%> <%--- 'ergm' could be hard to install, because it imports 'Rglpk', which requires GLPK library on the system. ---%> <%@string test_ergm="FALSE"%> <% use("limma", how="load") limma_weighted.median <- limma::weighted.median use("cwhmisc", how="load") cwhmisc_w.median <- cwhmisc::w.median use("laeken", how="load") laeken_weightedMedian <- laeken::weightedMedian <%@ifeq test_ergm="TRUE"%> use("ergm", how="load") ergm_wtd.median <- ergm::wtd.median <%@endif%> weightedMedian <- matrixStats::weightedMedian %> # <%@meta name="title"%> This report benchmark the performance of <%=fcnname%>() against alternative methods. ## Alternative methods * apply() + limma::weighted.median() * apply() + cwhmisc::w.median() * apply() + laeken::weightedMedian() <%@ifeq test_ergm="TRUE"%> * apply() + ergm::wtd.median() --%> <%@endif%> ## Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode="double") data <- data[1:3] })%> ``` ## Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] message(dataLabel) x <- data[[dataLabel]] gc() %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] w <- runif(length(x)) gc() stats <- microbenchmark( "weightedMedian" = weightedMedian(x, w=w, ties="mean", na.rm=FALSE), "limma::weighted.median" = limma_weighted.median(x, w=w, na.rm=FALSE), "cwhmisc::w.median" = cwhmisc_w.median(x, w=w), "laeken::weightedMedian" = laeken_weightedMedian(x, w=w), <%@ifeq test_ergm="TRUE"%> "ergm::wtd.median" = ergm_wtd.median(x, w=w), <%@endif%> unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=dataLabel) %> <% } # for (ii ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-12-17 o Made 'ergm::wtd.median' optional. 2014-06-03 o Created using benchmark snippet in incl/weightedMedian.Rex. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/anyMissing_subset.md.rsp0000644000175100001440000000250412726216471023064 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="anyMissing_subset"%> <%@string subname="anyMissing"%> <%@meta title="${subname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-04"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=subname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode=mode) })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] idxs <- sample.int(length(x), size=length(x)*0.7) x_S <- x[idxs] gc() stats <- microbenchmark( "anyMissing_x_S" = anyMissing(x_S), "anyMissing(x,idxs)" = anyMissing(x, idxs=idxs), "anyMissing(x[idxs])" = anyMissing(x[idxs]), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel)) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-04 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/binCounts.md.rsp0000644000175100001440000000363712726216471021332 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="binCounts"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-05-25"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=fcnname%>() against alternative methods. ## Alternative methods * hist() as below ```r <%=withCapture({ hist <- graphics::hist binCounts_hist <- function(x, bx, right=FALSE, ...) { hist(x, breaks=bx, right=right, include.lowest=TRUE, plot=FALSE)$counts } })%> ``` <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Non-sorted simulated data ```r <%=withCapture({ set.seed(0xBEEF) nx <- 100e3 # Number of data points xmax <- 0.01*nx x <- runif(nx, min=0, max=xmax) storage.mode(x) <- mode str(x) # Uniformely distributed bins nb <- 10e3 # Number of bins bx <- seq(from=0, to=xmax, length.out=nb+1L) bx <- c(-1, bx, xmax+1) })%> ``` ### Results <% benchmark <- function() { %> <% dataLabel <- if (is.unsorted(x)) "unsorted" else "sorted" %> <% mprintf("%s: %s\n", mode, dataLabel) %> ```r <%=withCapture({ gc() stats <- microbenchmark( binCounts = binCounts(x, bx=bx), hist = binCounts_hist(x, bx=bx), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel)) %> <% # Sanity checks n0 <- binCounts_hist(x, bx=bx) n1 <- binCounts(x, bx=bx) stopifnot(identical(n1, n0)) n1r <- rev(binCounts(-x, bx=rev(-bx), right=TRUE)) stopifnot(identical(n1r, n1)) %> <% } # benchmark() %> <% benchmark() %> ### Sorted simulated data ```r <%=withCapture({ x <- sort(x) })%> ``` <% benchmark() %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-02 o Restructured. 2014-05-25 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowLogSumExps.md.rsp0000644000175100001440000000332112726216471022610 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colLogSumExps"%> <%@string rowname="rowLogSumExps"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-09"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + matrixStats::logSumExp() * apply() + logSumExp0() where ```r <%=withCapture({ logSumExp0 <- function(lx, ...) { iMax <- which.max(lx) log1p(sum(exp(lx[-iMax] - lx[iMax]))) + lx[iMax] } # logSumExp0() })%> ``` ## Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode="double") })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colLogSumExps = colLogSumExps(X, na.rm=FALSE), "apply+logSumExp" = apply(X, MARGIN=2L, FUN=logSumExp, na.rm=FALSE), "apply+logSumExp0" = apply(X, MARGIN=2L, FUN=logSumExp0, na.rm=FALSE), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowLogSumExps = rowLogSumExps(X, na.rm=FALSE), "apply+logSumExp" = apply(X, MARGIN=1L, FUN=logSumExp, na.rm=FALSE), "apply+logSumExp0" = apply(X, MARGIN=1L, FUN=logSumExp0, na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/logSumExp.md.rsp0000644000175100001440000000247212726216471021305 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="logSumExp"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-01"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=fcnname%>() against alternative methods. ## Alternative methods * logSumExp_R() where ```r <%=withCapture({ logSumExp_R <- function(lx, ...) { iMax <- which.max(lx) log1p(sum(exp(lx[-iMax] - lx[iMax]))) + lx[iMax] } # logSumExp_R() })%> ``` ## Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode="double") data <- data[1:4] })%> ``` ## Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] message(dataLabel) x <- data[[dataLabel]] gc() %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] gc() stats <- microbenchmark( "logSumExp" = logSumExp(x), "logSumExp_R" = logSumExp_R(x), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=dataLabel) %> <% } # for (ii ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-01 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowRanks.md.rsp0000644000175100001440000000275612726216471021633 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colRanks"%> <%@string rowname="rowRanks"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-09"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + rank() <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colRanks = colRanks(X, na.rm=FALSE), "apply+rank" = apply(X, MARGIN=2L, FUN=rank, na.last="keep", ties.method="max"), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowRanks = rowRanks(X, na.rm=FALSE), "apply+rank" = apply(X, MARGIN=1L, FUN=rank, na.last="keep", ties.method="max"), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/count_subset.md.rsp0000644000175100001440000000247412726216471022101 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="count_subset"%> <%@string subname="count"%> <%@meta title="${subname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=subname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode=mode) })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) value <- 42 %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] idxs <- sample.int(length(x), size=length(x)*0.7) x_S <- x[idxs] gc() stats <- microbenchmark( "count_x_S" = count(x_S, value), "count(x,idxs)" = count(x, idxs=idxs, value), "count(x[idxs])" = count(x[idxs], value), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel)) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/binCounts_subset.md.rsp0000644000175100001440000000350712726216471022713 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="binCounts_subset"%> <%@string subname="binCounts"%> <%@meta title="${subname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-04"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=subname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Non-sorted simulated data ```r <%=withCapture({ set.seed(0xBEEF) nx <- 100e3 # Number of data points xmax <- 0.01*nx x <- runif(nx, min=0, max=xmax) storage.mode(x) <- mode str(x) # Uniformely distributed bins nb <- 10e3 # Number of bins bx <- seq(from=0, to=xmax, length.out=nb+1L) bx <- c(-1, bx, xmax+1) # indices for subsetting idxs <- sample.int(length(x), size=length(x)*0.7) })%> ``` ### Results <% benchmark <- function() { %> <% dataLabel <- if (is.unsorted(x)) "unsorted" else "sorted" %> <% mprintf("%s: %s\n", mode, dataLabel) %> ```r <%=withCapture({ x_S <- x[idxs] gc() stats <- microbenchmark( "binCounts_x_S" = binCounts(x_S, bx=bx), "binCounts(x,idxs)" = binCounts(x, idxs=idxs, bx=bx), "binCounts(x[idxs])" = binCounts(x[idxs], bx=bx), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel)) %> <% # Sanity checks n1 <- binCounts(x, idxs=idxs, bx=bx) n1r <- rev(binCounts(-x, idxs=idxs, bx=rev(-bx), right=TRUE)) stopifnot(identical(n1r, n1)) %> <% } # benchmark() %> <% benchmark() %> ### Sorted simulated data ```r <%=withCapture({ x <- sort(x) idxs <- sort(idxs) })%> ``` <% benchmark() %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-04 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowWeightedMedians.md.rsp0000644000175100001440000000271312726216471023607 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colWeightedMedians"%> <%@string rowname="rowWeightedMedians"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-09"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + weightedMedian() ## Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode="double") })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] w <- runif(nrow(X)) gc() colStats <- microbenchmark( colWeightedMedians = colWeightedMedians(X, w=w, na.rm=FALSE), "apply+weigthedMedian" = apply(X, MARGIN=2L, FUN=weightedMedian, w=w, na.rm=FALSE), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowWeightedMedians = rowWeightedMedians(X, w=w, na.rm=FALSE), "apply+weigthedMedian" = apply(X, MARGIN=1L, FUN=weightedMedian, w=w, na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/madDiff_subset.md.rsp0000644000175100001440000000245512726216471022302 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="madDiff_subset"%> <%@string subname="madDiff"%> <%@meta title="${subname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=subname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode=mode) })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] idxs <- sample.int(length(x), size=length(x)*0.7) x_S <- x[idxs] gc() stats <- microbenchmark( "madDiff_x_S" = madDiff(x_S), "madDiff(x,idxs)" = madDiff(x, idxs=idxs), "madDiff(x[idxs])" = madDiff(x[idxs]), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel)) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/weightedMedian_subset.md.rsp0000644000175100001440000000272112726216471023662 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="weightedMedian_subset"%> <%@string subname="weightedMedian"%> <%@meta title="${subname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> <% weightedMedian <- matrixStats::weightedMedian %> # <%@meta name="title"%> This report benchmark the performance of <%=subname%>() on subsetted computation. ## Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode="double") data <- data[1:3] })%> ``` ## Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] message(dataLabel) %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] idxs <- sample.int(length(x), size=length(x)*0.7) x_S <- x[idxs] w <- runif(length(x)) w_S <- x[idxs] gc() stats <- microbenchmark( "weightedMedian_x_w_S" = weightedMedian(x_S, w=w_S, ties="mean", na.rm=FALSE), "weightedMedian(x,w,idxs)" = weightedMedian(x, w=w, idxs=idxs, ties="mean", na.rm=FALSE), "weightedMedian(x[idxs],w[idxs])" = weightedMedian(x[idxs], w=w[idxs], ties="mean", na.rm=FALSE), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=dataLabel) %> <% } # for (ii ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowVars.md.rsp0000644000175100001440000000536412726216471021466 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colVars"%> <%@string rowname="rowVars"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2015-01-06"%> <%@include file="${header}"%> <%@string test_genefilter="TRUE"%> <%@ifeq test_genefilter="TRUE"%> <% use("genefilter", how="load") genefilter_rowVars <- genefilter::rowVars genefilter_colVars <- function(x, ...) genefilter_rowVars(t(x), ...) %> <%@endif%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + var() * colVarColMeans() and rowVarColMeans() <%@ifeq test_genefilter="TRUE"%> * genefilter::rowVars(t(.)) and genefilter::rowVars() <%@endif%> where ```r <%=withCapture({ colVarColMeans <- function(x, na.rm=TRUE) { if (na.rm) { n <- colSums(!is.na(x)) } else { n <- nrow(x) } var <- colMeans(x*x, na.rm=na.rm) - (colMeans(x, na.rm=na.rm))^2 var * n/(n-1) } })%> ``` and ```r <%=withCapture({ rowVarRowMeans <- function(x, na.rm=TRUE) { if (na.rm) { n <- rowSums(!is.na(x)) } else { n <- ncol(x) } mu <- rowMeans(x, na.rm=na.rm) var <- rowMeans(x*x, na.rm=na.rm) - mu^2 var * (n/(n-1)) } })%> ``` <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colVars = colVars(X, na.rm=FALSE), colVarColMeans = colVarColMeans(X, na.rm=FALSE), "apply+var" = apply(X, MARGIN=2L, FUN=var, na.rm=FALSE), <%@ifeq test_genefilter="TRUE"%> "genefilter::rowVars(t(.))" = genefilter_colVars(X, na.rm=FALSE), <%@endif%> unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowVars = rowVars(X, na.rm=FALSE), rowVarRowMeans = rowVarRowMeans(X, na.rm=FALSE), "apply+var" = apply(X, MARGIN=1L, FUN=var, na.rm=FALSE), <%@ifeq test_genefilter="TRUE"%> "genefilter::rowVars" = genefilter_rowVars(X, na.rm=FALSE), <%@endif%> unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-01-06 o Now benchmarking 'genefilter' functions too. 2014-11-23 o Now benchmarking rowVars() instead of rowSds() since the latter uses the former. 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowSums.md.rsp0000644000175100001440000000311612726216471021473 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colSums"%> <%@string rowname="rowSums"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-06-09"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * .colSums() and .rowSums() * apply() + sum() <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colSums = colSums(X, na.rm=FALSE), .colSums = .colSums(X, m=nrow(X), n=ncol(X), na.rm=FALSE), "apply+sum" = apply(X, MARGIN=2L, FUN=sum, na.rm=FALSE), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowSums = rowSums(X, na.rm=FALSE), .rowSums = .rowSums(X, m=nrow(X), n=ncol(X), na.rm=FALSE), "apply+sum" = apply(X, MARGIN=1L, FUN=sum, na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowAnys.md.rsp0000644000175100001440000000255412726216471021463 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colAnys"%> <%@string rowname="rowAnys"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-18"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * apply() + any() * colSums() > 0 or rowSums() > 0 ## Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode="logical") })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] gc() colStats <- microbenchmark( colAnys = colAnys(X), "apply+any" = apply(X, MARGIN=2L, FUN=any), "colSums > 0" = (colSums(X) > 0L), unit = "ms" ) X <- t(X) gc() rowStats <- microbenchmark( rowAnys = rowAnys(X), "apply+any" = apply(X, MARGIN=1L, FUN=any), "rowSums > 0" = (rowSums(X) > 0L), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-18 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/varDiff_subset.md.rsp0000644000175100001440000000251612726216471022327 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="varDiff_subset"%> <%@string subname="varDiff"%> <%@meta title="${subname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=subname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode=mode) data <- data[1:4] })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) %> ### <%=dataLabel%> vector #### All elements ```r <%=withCapture({ x <- data[[.dataLabel.]] idxs <- sample.int(length(x), size=length(x)*0.7) x_S <- x[idxs] gc() stats <- microbenchmark( "varDiff_x_S" = varDiff(x_S), "varDiff(x,idxs)" = varDiff(x, idxs=idxs), "varDiff(x[idxs])" = varDiff(x[idxs]), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel)) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/allocMatrix.md.rsp0000644000175100001440000000402612726216471021636 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="allocMatrix"%> <% fcnname <- "<%@string name="fcnname"%>" %> <%@meta title="${fcnname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-09"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=fcnname%>() against alternative methods. ## Alternative methods * matrix() * matrix() special trick for NA where ```r <%=withCapture({ allocMatrix_R <- function(nrow, ncol, value=NA) { if (is.na(value) && !is.nan(value)) { matrix(data=value[c()], nrow=nrow, ncol=ncol) } else { matrix(data=value, nrow=nrow, ncol=ncol) } } # allocMatrix_R() })%> ``` <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) values <- list(zero=0, one=1, "NA"=NA_real_) if (mode != "double") values <- lapply(values, FUN=function(x) { storage.mode(x) <- mode; x }) })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] x <- data[[dataLabel]] gc() %> ### <%=dataLabel%> matrix <% for (value in values) { %> <% valueLabel <- as.character(value) mprintf("%s: %s, value=%s\n", mode, dataLabel, valueLabel) %> ```r <%=withCapture({ dim <- dim(data[[.dataLabel.]]) nrow <- dim[1L] ncol <- dim[2L] str(value) })%> ``` <% gc() %> ```r <%=withCapture({ stats <- microbenchmark( "allocMatrix" = allocMatrix(nrow=nrow, ncol=ncol, value=value), "matrix" = matrix(data=value, nrow=nrow, ncol=ncol), "allocMatrix_R" = allocMatrix_R(nrow=nrow, ncol=ncol, value=value), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel, valueLabel)) %> <% } # for (value in values) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-11-01 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowOrderStats_subset.md.rsp0000644000175100001440000000372212726216471024226 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colOrderStats"%> <%@string rowname="rowOrderStats"%> <%@string fcnname="colRowOrderStats_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() probs <- 0.3 which <- round(probs*nrow(X)) colStats <- microbenchmark( "colOrderStats_X_S" = colOrderStats(X_S, which=which, na.rm=FALSE), "colOrderStats(X,rows,cols)" = colOrderStats(X, rows=rows, cols=cols, which=which, na.rm=FALSE), "colOrderStats(X[rows,cols])" = colOrderStats(X[rows,cols], which=which, na.rm=FALSE), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowOrderStats_X_S" = rowOrderStats(X_S, which=which, na.rm=FALSE), "rowOrderStats(X,cols,rows)" = rowOrderStats(X, rows=cols, cols=rows, which=which, na.rm=FALSE), "rowOrderStats(X[cols,rows])" = rowOrderStats(X[cols,rows], which=which, na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/t_tx_OP_y_subset.md.rsp0000644000175100001440000000336312726216471022653 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="t_tx_OP_y_subset"%> <%@string subname="t_tx_OP_y"%> <%@meta title="${subname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=subname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] y <- x[,1L] xrows <- sample.int(nrow(x), size=nrow(x)*0.7) xcols <- sample.int(ncol(x), size=ncol(x)*0.7) x_S <- x[xrows,xcols] yidxs <- xrows y_S <- y[yidxs] })%> ``` <% for (OP in c("+", "-", "*", "/")) { %> <% OPTag <- c("+"="add", "-"="sub", "*"="mul", "/"="div")[OP] gc() %> ```r <%=withCapture({ OP stats <- microbenchmark( "t_tx_OP_y_x_y_S" = t_tx_OP_y(x_S, y_S, OP=OP, na.rm=FALSE), "t_tx_OP_y(x,y,OP,xrows,xcols,yidxs)" = t_tx_OP_y(x, y, OP=OP, xrows=xrows, xcols=xcols, yidxs=yidxs, na.rm=FALSE), "t_tx_OP_y(x[xrows,xcols],y[yidxs],OP)" = t_tx_OP_y(x[xrows,xcols], y[yidxs], OP=OP, na.rm=FALSE), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel, OPTag)) %> <% } # for (OP ...) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowProds.md.rsp0000644000175100001440000000350712726216471021637 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colProds"%> <%@string rowname="rowProds"%> <%@meta title="${colname}() and ${rowname}() benchmarks"%> <%@meta author="Henrik Bengtsson"%> <%@meta date="2014-11-15"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods. ## Alternative methods * <%=colname%>()/<%=rowname%>() using method="expSumLog" * apply() + prod() * apply() + product() ## Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode="double") })%> ``` ## Results <% for (dataLabel in names(data)) { %> <% message(dataLabel) %> ### <%=dataLabel%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] })%> <% gc() %> <%=withCapture({ colStats <- microbenchmark( "colProds w/ direct" = colProds(X, method="direct", na.rm=FALSE), "colProds w/ expSumLog" = colProds(X, method="expSumLog", na.rm=FALSE), "apply+prod" = apply(X, MARGIN=2L, FUN=prod, na.rm=FALSE), "apply+product" = apply(X, MARGIN=2L, FUN=product, na.rm=FALSE), unit = "ms" ) })%> <%=withCapture({ X <- t(X) })%> <% gc() %> <%=withCapture({ rowStats <- microbenchmark( "rowProds w/ direct" = rowProds(X, method="direct", na.rm=FALSE), "rowProds w/ expSumLog" = rowProds(X, method="expSumLog", na.rm=FALSE), "apply+prod" = apply(X, MARGIN=1L, FUN=prod, na.rm=FALSE), "apply+product" = apply(X, MARGIN=1L, FUN=product, na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=dataLabel) %> <% } # for (dataLabel ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2014-06-09 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowRanges_subset.md.rsp0000644000175100001440000000343312726216471023352 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colRanges"%> <%@string rowname="rowRanges"%> <%@string fcnname="colRowRanges_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() colStats <- microbenchmark( "colRanges_X_S" = colRanges(X_S, na.rm=FALSE), "colRanges(X,rows,cols)" = colRanges(X, rows=rows, cols=cols, na.rm=FALSE), "colRanges(X[rows,cols])" = colRanges(X[rows,cols], na.rm=FALSE), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowRanges_X_S" = rowRanges(X_S, na.rm=FALSE), "rowRanges(X,cols,rows)" = rowRanges(X, rows=cols, cols=rows, na.rm=FALSE), "rowRanges(X[cols,rows])" = rowRanges(X[cols,rows], na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowMads_subset.md.rsp0000644000175100001440000000337412726216471023023 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colMads"%> <%@string rowname="rowMads"%> <%@string fcnname="colRowMads_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-06"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() colStats <- microbenchmark( "colMads_X_S" = colMads(X_S, na.rm=FALSE), "colMads(X,rows,cols)" = colMads(X, rows=rows, cols=cols, na.rm=FALSE), "colMads(X[rows,cols])" = colMads(X[rows,cols], na.rm=FALSE), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowMads_X_S" = rowMads(X_S, na.rm=FALSE), "rowMads(X,cols,rows)" = rowMads(X, rows=cols, cols=rows, na.rm=FALSE), "rowMads(X[cols,rows])" = rowMads(X[cols,rows], na.rm=FALSE), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-06 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/logSumExp_subset.md.rsp0000644000175100001440000000232312726216471022665 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="logSumExp_subset"%> <%@string subname="logSumExp"%> <%@meta title="${subname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=subname%>() on subsetted computation. ## Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode="double") data <- data[1:4] })%> ``` ## Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] message(dataLabel) %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] idxs <- sample.int(length(x), size=length(x)*0.7) x_S <- x[idxs] gc() stats <- microbenchmark( "logSumExp_x_S" = logSumExp(x_S), "logSumExp(x,idxs)" = logSumExp(x, idxs=idxs), "logSumExp(x[idxs])" = logSumExp(x[idxs]), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=dataLabel) %> <% } # for (ii ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/sumOver_subset.md.rsp0000644000175100001440000000253412726216471022406 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string fcnname="sumOver_subset"%> <%@string subname="sumOver"%> <%@meta title="${subname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-07"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=subname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-vectors.R"%> data <- rvectors(mode=mode) ##data <- data[1:3] })%> ``` ### Results <% for (ii in seq_along(data)) { %> <% dataLabel <- names(data)[ii] mprintf("%s: %s\n", mode, dataLabel) x <- data[[dataLabel]] gc() %> ### <%=dataLabel%> vector ```r <%=withCapture({ x <- data[[.dataLabel.]] idxs <- sample.int(length(x), size=length(x)*0.7) x_S <- x[idxs] gc() stats <- microbenchmark( "sumOver_x_S" = sumOver(x_S), "sumOver(x,idxs)" = sumOver(x, idxs=idxs), "sumOver(x[idxs])" = sumOver(x[idxs]), unit = "ms" ) })%> ``` <% benchmarkResults(stats, tags=c(mode, dataLabel)) %> <% } # for (ii ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-07 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/benchmarking/colRowCumsums_subset.md.rsp0000644000175100001440000000333312726216471023566 0ustar hornikusers<%@include file="includes/setup.md.rsp"%> <%@string colname="colCumsums"%> <%@string rowname="rowCumsums"%> <%@string fcnname="colRowCumsums_subset"%> <%@meta title="${colname}() and ${rowname}() benchmarks on subsetted computation"%> <%@meta author="Dongcan Jiang"%> <%@meta date="2015-06-06"%> <%@include file="${header}"%> # <%@meta name="title"%> This report benchmark the performance of <%=colname%>() and <%=rowname%>() on subsetted computation. <% for (mode in c("integer", "double")) { %> ## Data type "<%=mode%>" ### Data ```r <%=withCapture({ <%@include file="R/random-matrices.R"%> data <- rmatrices(mode=mode) })%> ``` ### Results <% for (dataLabel in names(data)) { %> <% mprintf("%s: %s\n", mode, dataLabel) %> #### <%=dataLabel%> <%=mode%> matrix ```r <%=withCapture({ X <- data[[.dataLabel.]] rows <- sample.int(nrow(X), size=nrow(X)*0.7) cols <- sample.int(ncol(X), size=ncol(X)*0.7) X_S <- X[rows,cols] gc() colStats <- microbenchmark( "colCumsums_X_S" = colCumsums(X_S), "colCumsums(X,rows,cols)" = colCumsums(X, rows=rows, cols=cols), "colCummins(X[rows,cols])" = colCumsums(X[rows,cols]), unit = "ms" ) X <- t(X) X_S <- t(X_S) gc() rowStats <- microbenchmark( "rowCumsums_X_S" = rowCumsums(X_S), "rowCumsums(X,cols,rows)" = rowCumsums(X, rows=cols, cols=rows), "rowCumsums(X[cols,rows])" = rowCumsums(X[cols,rows]), unit = "ms" ) })%> ``` <% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %> <% } # for (dataLabel ...) %> <% } # for (mode ...) %> <%@include file="${footer}"%> <%--------------------------------------------------------------------------- HISTORY: 2015-06-06 o Created. ---------------------------------------------------------------------------%> matrixStats/inst/doc/0000755000175100001440000000000012776336765014363 5ustar hornikusersmatrixStats/inst/doc/matrixStats-methods.md.rsp0000644000175100001440000002061012776336765021473 0ustar hornikusers<%@meta language="R-vignette" content="-------------------------------- DIRECTIVES FOR R: %\VignetteIndexEntry{matrixStats: Summary of functions} %\VignetteAuthor{Henrik Bengtsson} %\VignetteKeyword{matrix} %\VignetteKeyword{vector} %\VignetteKeyword{apply} %\VignetteKeyword{rows} %\VignetteKeyword{columns} %\VignetteKeyword{memory} %\VignetteKeyword{speed} %\VignetteEngine{R.rsp::rsp} %\VignetteTangle{FALSE} --------------------------------------------------------------------"%> <% pkgName <- "matrixStats" library(pkgName, character.only=TRUE) ns <- getNamespace(pkgName) env <- as.environment(sprintf("package:%s", pkgName)) R.utils::use("R.utils") kable <- function(df, ...) { fcns <- as.character(df$Functions) fcns <- strsplit(fcns, split=",") fcns <- sapply(fcns, FUN=function(names) { names <- trim(names) ok <- sapply(names, FUN=exists, envir=ns, mode="function") names[ok] <- sprintf("%s()", names[ok]) names[!ok] <- sprintf("~~%s()~~", names[!ok]) names <- paste(names, collapse=", ") }) df$Functions <- fcns df$Example <- sprintf("`%s`", df$Example) print(knitr::kable(df, ..., format="markdown")) } # Find all functions all <- ls(envir=env) keep <- sapply(all, FUN=function(name) { is.function(get(name, envir=env)) }) all <- all[keep] keep <- !grepl("[.]([^.]*)$", all) all <- all[keep] # Hidden functions skip <- c("rowAvgsPerColSet", "colAvgsPerRowSet") skip <- c(skip, "allocArray", "allocMatrix", "allocVector") all <- setdiff(all, skip) # Column and row functions crfcns <- grep("^(col|row)", all, value=TRUE) # Vector functions vfcns <- setdiff(all, crfcns) %> # <%@meta name="title"%> <% pkg <- R.oo::Package(pkgName) %> <%@meta name="author"%> on <%=format(as.Date(pkg$date), format="%B %d, %Y")%> <% fcns <- crfcns base <- gsub("^(col|row)", "", fcns) groups <- tapply(fcns, base, FUN=list) stopifnot(all(sapply(groups, FUN=length) == 2L)) groups <- matrix(unlist(groups, use.names=FALSE), nrow=2L) %> <%--- ## Functions that apply to column and rows of matrices ``` <% print(fcns) %> ``` ---%> <% fcns <- vfcns %> <%--- ## Functions that apply to vectors ``` <% print(fcns) %> ``` ---%> ## Location and scale estimators <% tbl <- NULL row <- data.frame( "Estimator" = "Weighted sample mean", "Functions" = "weightedMean, colWeightedMeans, rowWeightedMeans", "Example" = "weightedMean(x, w); rowWeightedMeans(x, w)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Median", "Functions" = "median, colMedians, rowMedians", "Example" = "median(x); rowMedians(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Weighted median", "Functions" = "weightedMedian, colWeightedMedians, rowWeightedMedians", "Example" = "weightedMedian(x, w); rowWeightedMedians(x, w)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Sample variance", "Functions" = "var, colVars, rowVars", "Example" = "var(x); rowVars(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Weighted sample variance", "Functions" = "weightedVar, colWeightedVars, rowWeightedVars", "Example" = "weightedVar(x, w), rowWeightedVars(x, w)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Sample variance by n-order differences", "Functions" = "varDiff, colVarDiffs, rowVarDiffs", "Example" = "varDiff(x); rowVarDiffs(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Sample standard deviation", "Functions" = "sd, colSds, rowSds", "Example" = "sd(x); rowSds(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Weighted sample deviation", "Functions" = "weightedSd, colWeightedSds, rowWeightedSds", "Example" = "weightedSd(x, w), rowWeightedSds(x, w)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Sample standard deviation by n-order differences", "Functions" = "sdDiff, colSdDiffs, rowSdDiffs", "Example" = "sdDiff(x); rowSdDiffs(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Median absolute deviation (MAD)", "Functions" = "mad, colMads, rowMads", "Example" = "mad(x); rowMads(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Weighted median absolute deviation (MAD)", "Functions" = "weightedMad, colWeightedMads, rowWeightedMads", "Example" = "weightedMad(x, w), rowWeightedMads(x, w)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Median absolute deviation (MAD) by n-order differences", "Functions" = "madDiff, colMadDiffs, rowMadDiffs", "Example" = "madDiff(x); rowMadDiffs()" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Quantile", "Functions" = "quantile, colQuantiles, rowQuantiles", "Example" = "quantile(x, probs); rowQuantiles(x, probs)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Interquartile range (IQR)", "Functions" = "iqr, colIQRs, rowIQRs", "Example" = "iqr(x); rowIQRs(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Interquartile range (IQR) by n-order differences", "Functions" = "iqrDiff, colIQRDiffs, rowIQRDiffs", "Example" = "iqrDiff(x); rowIQRDiffs(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Range", "Functions" = "range, colRanges, rowRanges", "Example" = "range(x); rowRanges(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Minimum", "Functions" = "min, colMins, rowMins", "Example" = "min(x); rowMins(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Maximum", "Functions" = "max, colMaxs, rowMaxs", "Example" = "max(x); rowMaxs(x)" ) tbl <- rbind(tbl, row) %> <% kable(tbl) %> ## Testing for and counting values <% tbl <- NULL row <- data.frame( "Operator" = "Are there any missing values?", "Functions" = "anyMissing, colAnyMissings, rowAnyMissings", "Example" = "anyMissing(x); rowAnyMissings(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Operator" = "Does TRUE exists?", "Functions" = "any, colAnys, rowAnys", "Example" = "any(x); rowAnys(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Operator" = "Are all values TRUE?", "Functions" = "all, colAlls, rowAlls", "Example" = "all(x); rowAlls(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Operator" = "Does value exists?", "Functions" = "anyValue, colAnys, rowAnys", "Example" = "anyValue(x, value); rowAnys(x, value)" ) tbl <- rbind(tbl, row) row <- data.frame( "Operator" = "Do all elements have a given value?", "Functions" = "allValue, colAlls, rowAlls", "Example" = "allValue(x, value); rowAlls(x, value)" ) tbl <- rbind(tbl, row) row <- data.frame( "Operator" = "Number of occurrences of a value?", "Functions" = "count, colCounts, rowCounts", "Example" = "count(x, value); rowCounts(x, value)" ) tbl <- rbind(tbl, row) %> <% kable(tbl) %> ## Cumulative functions <% tbl <- NULL row <- data.frame( "Operator" = "Cumulative sum", "Functions" = "cumsum, colCumsums, rowCumsums", "Example" = "cumsum(x); rowCumsums(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Operator" = "Cumulative product", "Functions" = "cumprod, colCumprods, rowCumprods", "Example" = "cumprod(x); rowCumprods(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Operator" = "Cumulative minimum", "Functions" = "cummin, colCummins, rowCummins", "Example" = "cummin(x); rowCummins(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Operator" = "Cumulative maximum", "Functions" = "cummax, colCummaxs, rowCummaxs", "Example" = "cummax(x); rowCummaxs(x)" ) tbl <- rbind(tbl, row) %> <% kable(tbl) %> ## Binning <% tbl <- NULL row <- data.frame( "Estimator" = "Counts in disjoint bins", "Functions" = "binCounts", "Example" = "binCounts(x, bx)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Sample means (and counts) in disjoint bins", "Functions" = "binMeans", "Example" = "binMeans(y, x, bx)" ) tbl <- rbind(tbl, row) %> <% kable(tbl) %> ## Miscellaneous <% tbl <- NULL row <- data.frame( "Operation" = "Lagged differences", "Functions" = c("diff2, colDiffs, rowDiffs"), "Example" = "diff2(x), rowDiffs(x)" ) tbl <- rbind(tbl, row) %> <% kable(tbl) %> ------------------------------------------------------------- <%=pkgName%> v<%=getVersion(pkg)%>. Release: [CRAN](https://cran.r-project.org/package=<%=pkgName%>), Development: [GitHub](<%=getUrl(pkg)%>). matrixStats/inst/doc/matrixStats-methods.html0000644000175100001440000002177512776336765021251 0ustar hornikusers matrixStats: Summary of functions

matrixStats: Summary of functions

Henrik Bengtsson on October 08, 2016

Location and scale estimators

Estimator Functions Example
Weighted sample mean weightedMean(), colWeightedMeans(), rowWeightedMeans() weightedMean(x, w); rowWeightedMeans(x, w)
Median median(), colMedians(), rowMedians() median(x); rowMedians(x)
Weighted median weightedMedian(), colWeightedMedians(), rowWeightedMedians() weightedMedian(x, w); rowWeightedMedians(x, w)
Sample variance var(), colVars(), rowVars() var(x); rowVars(x)
Weighted sample variance weightedVar(), colWeightedVars(), rowWeightedVars() weightedVar(x, w), rowWeightedVars(x, w)
Sample variance by n-order differences varDiff(), colVarDiffs(), rowVarDiffs() varDiff(x); rowVarDiffs(x)
Sample standard deviation sd(), colSds(), rowSds() sd(x); rowSds(x)
Weighted sample deviation weightedSd(), colWeightedSds(), rowWeightedSds() weightedSd(x, w), rowWeightedSds(x, w)
Sample standard deviation by n-order differences sdDiff(), colSdDiffs(), rowSdDiffs() sdDiff(x); rowSdDiffs(x)
Median absolute deviation (MAD) mad(), colMads(), rowMads() mad(x); rowMads(x)
Weighted median absolute deviation (MAD) weightedMad(), colWeightedMads(), rowWeightedMads() weightedMad(x, w), rowWeightedMads(x, w)
Median absolute deviation (MAD) by n-order differences madDiff(), colMadDiffs(), rowMadDiffs() madDiff(x); rowMadDiffs()
Quantile quantile(), colQuantiles(), rowQuantiles() quantile(x, probs); rowQuantiles(x, probs)
Interquartile range (IQR) iqr(), colIQRs(), rowIQRs() iqr(x); rowIQRs(x)
Interquartile range (IQR) by n-order differences iqrDiff(), colIQRDiffs(), rowIQRDiffs() iqrDiff(x); rowIQRDiffs(x)
Range range(), colRanges(), rowRanges() range(x); rowRanges(x)
Minimum min(), colMins(), rowMins() min(x); rowMins(x)
Maximum max(), colMaxs(), rowMaxs() max(x); rowMaxs(x)

Testing for and counting values

Operator Functions Example
Are there any missing values? anyMissing(), colAnyMissings(), rowAnyMissings() anyMissing(x); rowAnyMissings(x)
Does TRUE exists? any(), colAnys(), rowAnys() any(x); rowAnys(x)
Are all values TRUE? all(), colAlls(), rowAlls() all(x); rowAlls(x)
Does value exists? anyValue(), colAnys(), rowAnys() anyValue(x, value); rowAnys(x, value)
Do all elements have a given value? allValue(), colAlls(), rowAlls() allValue(x, value); rowAlls(x, value)
Number of occurrences of a value? count(), colCounts(), rowCounts() count(x, value); rowCounts(x, value)

Cumulative functions

Operator Functions Example
Cumulative sum cumsum(), colCumsums(), rowCumsums() cumsum(x); rowCumsums(x)
Cumulative product cumprod(), colCumprods(), rowCumprods() cumprod(x); rowCumprods(x)
Cumulative minimum cummin(), colCummins(), rowCummins() cummin(x); rowCummins(x)
Cumulative maximum cummax(), colCummaxs(), rowCummaxs() cummax(x); rowCummaxs(x)

Binning

Estimator Functions Example
Counts in disjoint bins binCounts() binCounts(x, bx)
Sample means (and counts) in disjoint bins binMeans() binMeans(y, x, bx)

Miscellaneous

Operation Functions Example
Lagged differences diff2(), colDiffs(), rowDiffs() diff2(x), rowDiffs(x)

matrixStats v0.51.0. Release: CRAN, Development: GitHub.

matrixStats/tests/0000755000175100001440000000000012776336765014003 5ustar hornikusersmatrixStats/tests/rowOrderStats_subset.R0000644000175100001440000000212712726216471020321 0ustar hornikuserslibrary("matrixStats") rowOrderStats_R <- function(x, probs, ...) { ans <- apply(x, MARGIN=1L, FUN=quantile, probs=probs, type=3L) # Remove Attributes attributes(ans) <- NULL ans } # rowOrderStats_R() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) storage.mode(x) <- "integer" probs <- 0.3 for (rows in indexCases) { for (cols in indexCases) { if (is.null(cols)) which <- round(probs*ncol(x)) else { xxrows <- rows suppressWarnings({ xx <- tryCatch(x[,cols,drop=FALSE], error=function(c) "error") if (identical(xx, "error")) which <- 0 else which <- round(probs*ncol(xx)) }) } if (which == 0) next validateIndicesTestMatrix(x, rows, cols, ftest=rowOrderStats, fsure=rowOrderStats_R, which=which, probs=probs) validateIndicesTestMatrix(x, rows, cols, fcolTest=colOrderStats, fsure=rowOrderStats_R, which=which, probs=probs) } } matrixStats/tests/x_OP_y_subset.R0000644000175100001440000000446112726216471016677 0ustar hornikuserslibrary("matrixStats") x_OP_y_R <- function(x, y, OP, na.rm=FALSE) { if (na.rm) { xnok <- is.na(x) ynok <- is.na(y) anok <- xnok & ynok unit <- switch(OP, "+" = 0, "-" = NA_real_, "*" = 1, "/" = NA_real_, stop("Unknown 'OP' operator: ", OP) ) x[xnok] <- unit y[ynok] <- unit } ans <- switch(OP, "+" = x + y, "-" = x - y, "*" = x * y, "/" = x / y, stop("Unknown 'OP' operator: ", OP) ) if (na.rm) { ans[anok] <- NA_real_ } ans } # x_OP_y_R() t_tx_OP_y_R <- function(x, y, OP, na.rm=FALSE) { t(x_OP_y_R(x=t(x), y=y, OP=OP, na.rm=na.rm)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") for (OP in c("+", "-", "*", "/")) { for (mode in c("numeric", "integer")) { x <- matrix(runif(6*6, min=-6, max=6), nrow=6L, ncol=6L) y <- runif(6, min=0, max=6) storage.mode(x) <- mode storage.mode(y) <- mode if (mode == "numeric") y[1] <- Inf for (xrows in indexCases) { for (xcols in indexCases) { if (is.null(xrows)) xrows <- seq_len(nrow(x)) if (is.null(xcols)) xcols <- seq_len(ncol(x)) for (yidxs in list(xrows,xcols)) { for (na.rm in c(TRUE, FALSE)) { suppressWarnings({ actual <- tryCatch( x_OP_y(x, y, OP, xrows=xrows, xcols=xcols, yidxs=yidxs, na.rm=na.rm), error=function(c) "error" ) expect <- tryCatch( x_OP_y_R(x[xrows,xcols,drop=FALSE], y[yidxs], OP, na.rm=na.rm), error=function(c) "error" ) }) stopifnot(all.equal(as.vector(actual), as.vector(expect))) suppressWarnings({ actual <- tryCatch( t_tx_OP_y(x, y, OP, xrows=xrows, xcols=xcols, yidxs=yidxs, na.rm=na.rm), error=function(c) "error" ) expect <- tryCatch( t_tx_OP_y_R(x[xrows,xcols,drop=FALSE], y[yidxs], OP, na.rm=na.rm), error=function(c) "error" ) }) stopifnot(all.equal(as.vector(actual), as.vector(expect))) } } } } } } matrixStats/tests/rowWeightedVars_subset.R0000644000175100001440000000214612726216471020624 0ustar hornikuserslibrary("matrixStats") FUNs <- list( weightedVar=c(rowWeightedVars, colWeightedVars), weightedSd=c(rowWeightedSds, colWeightedSds), weightedMad=c(rowWeightedMads, colWeightedMads) ) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") for (fcn in names(FUNs)) { cat(sprintf("subsetted tests on matrix %s()...\n", fcn)) rowFUN <- FUNs[[fcn]][[1]] colFUN <- FUNs[[fcn]][[2]] for (mode in c("numeric", "integer")) { x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) w <- runif(6, min=0, max=6) storage.mode(x) <- mode storage.mode(w) <- mode if (mode == "numeric") w[1] <- Inf for (rows in indexCases) { for (cols in indexCases) { for (na.rm in c(TRUE, FALSE)) { validateIndicesTestMatrix_w(x, w, rows, cols, ftest=rowFUN, fsure=rowFUN, na.rm=na.rm) validateIndicesTestMatrix_w(x, w, rows, cols, fcolTest=colFUN, fsure=rowFUN, na.rm=na.rm) } } } } cat(sprintf("%s()...DONE\n", fcn)) } matrixStats/tests/signTabulate.R0000644000175100001440000000200312726216471016525 0ustar hornikuserslibrary("matrixStats") signTabulate0 <- function(x, ...) { nneg <- sum(x < 0, na.rm=TRUE) nzero <- sum(x == 0, na.rm=TRUE) npos <- sum(x > 0, na.rm=TRUE) nna <- sum(is.na(x)) nneginf <- sum(is.infinite(x) & x < 0, na.rm=TRUE) nposinf <- sum(is.infinite(x) & x > 0, na.rm=TRUE) res <- c(nneg, nzero, npos, nna, nneginf, nposinf) res <- as.double(res) names(res) <- c("-1", "0", "+1", "NA", "-Inf", "+Inf") if (is.integer(x)) res <- res[1:4] res } # signTabulate0() # Simulate data set.seed(0xBEEF) n <- 1e3 x <- runif(n) x[sample(n, size=0.1*n)] <- 0 x[sample(n, size=0.1*n)] <- NA_real_ x[sample(n, size=0.1*n)] <- -Inf x[sample(n, size=0.1*n)] <- +Inf # Doubles message("Doubles:") counts0 <- signTabulate0(x) print(counts0) counts1 <- signTabulate(x) print(counts1) stopifnot(identical(counts1, counts0)) # Integers message("Integers:") x <- suppressWarnings(as.integer(x)) counts0 <- signTabulate0(x) print(counts0) counts1 <- signTabulate(x) print(counts1) stopifnot(identical(counts1, counts0)) matrixStats/tests/diff2.R0000644000175100001440000000105012726216471015076 0ustar hornikuserslibrary("matrixStats") set.seed(0x42) for (mode in c("integer", "double")) { x <- rnorm(10, sd=5) storage.mode(x) <- mode str(x) for (hasNA in c(FALSE, TRUE)) { if (hasNA) { x[sample(1:10, size=3)] <- NA } for (l in 1:3) { for (d in 1:4) { cat(sprintf("%s: NAs=%s, lag=%d, differences=%d\n", mode, hasNA, l, d)) y0 <- diff(x, lag=l, differences=d) str(y0) y1 <- diff2(x, lag=l, differences=d) str(y1) stopifnot(identical(y1, y0)) } } } # for (hasNA ...) } matrixStats/tests/rowCumMinMaxs_subset.R0000644000175100001440000000234712726216471020254 0ustar hornikuserslibrary("matrixStats") rowCummins_R <- function(x) { suppressWarnings({ y <- t(apply(x, MARGIN=1L, FUN=cummin)) }) dim(y) <- dim(x) y } rowCummaxs_R <- function(x) { mode <- storage.mode(x) # Change mode because a bug is detected on cummax for integer in R-3.2.0 storage.mode(x) <- "numeric" suppressWarnings({ y <- t(apply(x, MARGIN=1L, FUN=cummax)) }) dim(y) <- dim(x) storage.mode(y) <- mode y } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-3, max=3), nrow=6, ncol=6) storage.mode(x) <- "integer" for (rows in indexCases) { for (cols in indexCases) { validateIndicesTestMatrix(x, rows, cols, ftest=rowCummins, fsure=rowCummins_R) validateIndicesTestMatrix(x, rows, cols, ftest=function(x, rows, cols, ...) { t(colCummins(t(x), rows=cols, cols=rows)) }, fsure=rowCummins_R) validateIndicesTestMatrix(x, rows, cols, ftest=rowCummaxs, fsure=rowCummaxs_R) validateIndicesTestMatrix(x, rows, cols, ftest=function(x, rows, cols, ...) { t(colCummaxs(t(x), rows=cols, cols=rows)) }, fsure=rowCummaxs_R) } } matrixStats/tests/rowRanks.R0000644000175100001440000000263012726216471015717 0ustar hornikuserslibrary("matrixStats") set.seed(1) cat("Consistency checks:\n") for (kk in 1:4) { cat("Random test #", kk, "\n", sep="") # Simulate data in a matrix of any shape dim <- sample(40:80, size=2L) n <- prod(dim) x <- rnorm(n, sd=10) dim(x) <- dim # Add NAs? if ((kk %% 4) %in% c(3,0)) { cat("Adding NAs\n") nna <- sample(n, size=1L) x[sample(length(x), size=nna)] <- NA_real_ } # Integer or double? if ((kk %% 4) %in% c(2,0)) { cat("Coercing to integers\n") storage.mode(x) <- "integer" } str(x) for (ties in c("max", "min", "average")) { cat(sprintf("ties.method=%s\n", ties)) # rowRanks(): y1 <- matrixStats::rowRanks(x, ties.method=ties) y2 <- t(apply(x, MARGIN=1L, FUN=rank, na.last="keep", ties.method=ties)) stopifnot(identical(y1,y2)) y3 <- matrixStats::colRanks(t(x), ties.method=ties) stopifnot(identical(y1,y3)) # colRanks(): y1 <- matrixStats::colRanks(x, ties.method=ties) y2 <- t(apply(x, MARGIN=2L, FUN=rank, na.last="keep", ties.method=ties)) stopifnot(identical(y1,y2)) y3 <- matrixStats::rowRanks(t(x), ties.method=ties) stopifnot(identical(y1,y3)) } } # for (kk ...) ## Exception handling x <- matrix(1:12, nrow=3L) y <- try(rowRanks(x, ties.method="unknown"), silent=TRUE) stopifnot(inherits(y, "try-error")) y <- try(colRanks(x, ties.method="unknown"), silent=TRUE) stopifnot(inherits(y, "try-error")) matrixStats/tests/rowCollapse.R0000644000175100001440000000124512726216471016404 0ustar hornikuserslibrary("matrixStats") x <- matrix(1:27, ncol=3) idxs <- 1L y <- rowCollapse(x, idxs) stopifnot(identical(y, x[,idxs])) y2 <- colCollapse(t(x), idxs) stopifnot(identical(y2, y)) idxs <- 2L y <- rowCollapse(x, idxs) stopifnot(identical(y, x[,idxs])) y2 <- colCollapse(t(x), idxs) stopifnot(identical(y2, y)) idxs <- c(1,1,1,1,1,3,3,3,3) y <- rowCollapse(x, idxs) stopifnot(identical(y, c(x[1:5,1], x[6:9,3]))) y2 <- colCollapse(t(x), idxs) stopifnot(identical(y2, y)) idxs <- 1:3 y <- rowCollapse(x, idxs) print(y) yT <- c(x[1,1],x[2,2],x[3,3],x[4,1],x[5,2],x[6,3],x[7,1],x[8,2],x[9,3]) stopifnot(identical(y, yT)) y2 <- colCollapse(t(x), idxs) stopifnot(identical(y2, y)) matrixStats/tests/count_subset.R0000644000175100001440000000137112726216471016627 0ustar hornikuserslibrary("matrixStats") count_R <- function(x, value=TRUE, na.rm=FALSE, ...) { if (is.na(value)) { counts <- sum(is.na(x)) } else { counts <- sum(x == value, na.rm=na.rm) } as.integer(counts) } # count_R() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- runif(6, min=-3, max=3) storage.mode(x) <- "integer" for (idxs in indexCases) { validateIndicesTestVector(x, idxs, ftest=count, fsure=count_R, value=0, na.rm=TRUE) validateIndicesTestVector(x, idxs, ftest=count, fsure=count_R, value=0, na.rm=FALSE) validateIndicesTestVector(x, idxs, ftest=count, fsure=count_R, value=NA_integer_) } matrixStats/tests/weightedMean.R0000644000175100001440000000425412726216471016516 0ustar hornikuserslibrary("matrixStats") for (mode in c("integer", "double")) { cat("mode: ", mode, "", sep="") n <- 2L x <- runif(n, min=-5, max=5) storage.mode(x) <- mode str(x) cat("All weights are 1\n") w <- rep(1, times=n) m0 <- weighted.mean(x, w) m1 <- weightedMean(x, w) str(list(m0=m0, m1=m1)) stopifnot(identical(m1,m0)) cat("First weight is 5\n") # Pull the mean towards zero w[1] <- 5 str(w) m0 <- weighted.mean(x, w) m1 <- weightedMean(x, w) str(list(m0=m0, m1=m1)) stopifnot(identical(m1,m0)) cat("All weights are 0\n") # All weights set to zero w <- rep(0, times=n) m0 <- weighted.mean(x, w) m1 <- weightedMean(x, w) str(list(m0=m0, m1=m1)) stopifnot(identical(m1,m0)) cat("First weight is 8.5\n") # Put even more weight on the zero w[1] <- 8.5 m0 <- weighted.mean(x, w) m1 <- weightedMean(x, w) str(list(m0=m0, m1=m1)) stopifnot(identical(m1,m0)) cat("First weight is Inf\n") # All weight on the first value w[1] <- Inf m0 <- weighted.mean(x, w) m1 <- weightedMean(x, w) str(list(m0=m0, m1=m1)) stopifnot(identical(m1,m0)) cat("Last weight is Inf\n") # All weight on the last value w[1] <- 1 w[n] <- Inf m0 <- weighted.mean(x, w) m1 <- weightedMean(x, w) str(list(m0=m0, m1=m1)) stopifnot(identical(m1,m0)) } # for (mode ...) message("*** Testing for missing values") # NA tests xs <- list(A=c(1, 2, 3), B=c(NA, 2, 3), C=c(NA, 2, 3)) ws <- list(A=c(1, 1, 1), B=c(NA, 1, 1), C=c(1, NA, 1)) ## NOTE: The (B,B) case with na.rm=TRUE is interesting because ## even if NAs in 'w' are not dropped by na.rm=TRUE, this one ## is because 'x' is dropped and therefore that first element ## is skipped in the computation. It basically does ## keep <- !is.na(x); x <- x[keep]; w <- w[keep] ## without looking at 'w'. for (x in xs) { for (mode in c("integer", "double")) { storage.mode(x) <- mode for (w in ws) { for (na.rm in c(FALSE, TRUE)) { cat(sprintf("mode: %s, na.rm=%s\n", mode, na.rm)) str(list(x=x, w=w)) m0 <- weighted.mean(x, w, na.rm=na.rm) m1 <- weightedMean(x, w, na.rm=na.rm) str(list(m0=m0, m1=m1)) stopifnot(all.equal(m1,m0)) } } } } matrixStats/tests/rowProds.R0000644000175100001440000000415012726216471015727 0ustar hornikuserslibrary("matrixStats") rowProds_R <- function(x, FUN=prod, na.rm=FALSE, ...) { y <- apply(x, MARGIN=1L, FUN=FUN, na.rm=na.rm) y } all.equal.na <- function(target, current, ...) { # Computations involving NaN may return NaN or NA, cf. ?is.nan current[is.nan(current)] <- NA_real_ target[is.nan(target)] <- NA_real_ all.equal(target, current, ...) } for (mode in c("integer", "double")) { # Missing values x <- matrix(c(1,NA,NaN,1, 1,0,1,0), nrow=4, ncol=2) cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) y0 <- rowProds_R(x, na.rm=TRUE) print(y0) y1 <- rowProds(x, na.rm=TRUE) print(y1) y2 <- colProds(t(x), na.rm=TRUE) print(y2) stopifnot(all.equal(y1, y0)) stopifnot(all.equal(y1, x[,2])) stopifnot(all.equal(y2, y1)) # Missing values y0 <- rowProds_R(x, na.rm=FALSE) print(y0) y1 <- rowProds(x, na.rm=FALSE) print(y1) y2 <- colProds(t(x), na.rm=FALSE) print(y2) stopifnot(all.equal.na(y1, y0)) stopifnot(all.equal(y2, y1)) y3 <- x[,1]*x[,2] print(y3) stopifnot(all.equal.na(y1, y3)) # "Empty" rows y0 <- rowProds_R(x[integer(0),,drop=FALSE], na.rm=FALSE) print(y0) y1 <- rowProds(x[integer(0),,drop=FALSE], na.rm=FALSE) print(y1) y2 <- colProds(t(x[integer(0),,drop=FALSE]), na.rm=FALSE) print(y2) stopifnot(all.equal.na(y1, y0)) stopifnot(all.equal(y2, y1)) stopifnot(length(y1) == 0L) # Using product() y1 <- rowProds(x, method="expSumLog", na.rm=FALSE) print(y1) y2 <- colProds(t(x), method="expSumLog", na.rm=FALSE) print(y2) stopifnot(all.equal(y2, y1)) } # for (mode ...) # Bug report 2012-06-25 x <- matrix(c(1,1,1,1, 1,0,1,0), nrow=4, ncol=2) y0 <- rowProds_R(x) print(y0) y1 <- rowProds(x) print(y1) y2 <- colProds(t(x)) print(y2) stopifnot(all.equal.na(y1, y0)) stopifnot(all.equal.na(y1, x[,1]*x[,2])) stopifnot(all.equal.na(y2, y1)) # Bug report 2014-03-25 ("all rows contains a zero") x <- matrix(c(0,1,1,0), nrow=2, ncol=2) y0 <- rowProds_R(x) print(y0) y1 <- rowProds(x) print(y1) y2 <- colProds(t(x)) print(y2) stopifnot(all.equal.na(y1, y0)) stopifnot(all.equal.na(y1, c(0,0))) stopifnot(all.equal.na(y2, y1)) matrixStats/tests/x_OP_y.R0000644000175100001440000000713012726216471015306 0ustar hornikuserslibrary("matrixStats") x_OP_y_R <- function(x, y, OP, na.rm=FALSE) { if (na.rm) { xnok <- is.na(x) ynok <- is.na(y) anok <- xnok & ynok unit <- switch(OP, "+" = 0, "-" = NA_real_, "*" = 1, "/" = NA_real_, stop("Unknown 'OP' operator: ", OP) ) x[xnok] <- unit y[ynok] <- unit } ans <- switch(OP, "+" = x + y, "-" = x - y, "*" = x * y, "/" = x / y, stop("Unknown 'OP' operator: ", OP) ) if (na.rm) { ans[anok] <- NA_real_ } ans } # x_OP_y_R() t_tx_OP_y_R <- function(x, y, OP, na.rm=FALSE) { t(x_OP_y_R(x=t(x), y=y, OP=OP, na.rm=na.rm)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # No missing values # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(1:16, nrow=4, ncol=4) y <- 1:nrow(x) storage.mode(y) <- storage.mode(x) for (OP in c("+", "-", "*", "/")) { for (na.rm in c(FALSE, TRUE)) { cat(sprintf("OP='%s', na.rm=%s\n", OP, na.rm)) a0 <- x_OP_y_R(x,y, OP, na.rm=na.rm) a1 <- x_OP_y(x,y, OP, na.rm=na.rm) str(a1) stopifnot(all.equal(a1, a0)) b0 <- t_tx_OP_y_R(x,y, OP, na.rm=na.rm) b1 <- t_tx_OP_y(x,y, OP, na.rm=na.rm) str(b1) stopifnot(all.equal(b1, b0)) } } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Missing values in x, y, or both. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (which in c("x", "y", "both")) { x <- matrix(1:16, nrow=4, ncol=4) y <- 1:nrow(x) storage.mode(y) <- storage.mode(x) if (which == "x") { x[3:6] <- NA_real_ } else if (which == "y") { y[c(1,3)] <- NA_real_ } else if (which == "both") { x[3:6] <- NA_real_ y[c(1,3)] <- NA_real_ } for (OP in c("+", "-", "*", "/")) { for (na.rm in c(FALSE, TRUE)) { cat(sprintf("OP='%s', na.rm=%s\n", OP, na.rm)) a0 <- x_OP_y_R(x,y, OP, na.rm=na.rm) a1 <- x_OP_y(x,y, OP, na.rm=na.rm) str(a1) stopifnot(all.equal(a1, a0)) b0 <- t_tx_OP_y_R(x,y, OP, na.rm=na.rm) b1 <- t_tx_OP_y(x,y, OP, na.rm=na.rm) str(b1) stopifnot(all.equal(b1, b0)) } } } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Length differences # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(1:8, nrow=2, ncol=4) y <- 1:ncol(x) storage.mode(y) <- storage.mode(x) for (OP in c("+", "-", "*", "/")) { for (na.rm in c(FALSE, TRUE)) { cat(sprintf("OP='%s', na.rm=%s\n", OP, na.rm)) a0 <- x_OP_y_R(x,y, OP, na.rm=na.rm) a1 <- x_OP_y(x,y, OP, na.rm=na.rm) str(a1) stopifnot(all.equal(a1, a0)) b0 <- t_tx_OP_y_R(x,y, OP, na.rm=na.rm) b1 <- t_tx_OP_y(x,y, OP, na.rm=na.rm) str(b1) stopifnot(all.equal(b1, b0)) } } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # All missing values # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - xs <- list( A=matrix(1:2, nrow=2, ncol=2), B=matrix(NA_integer_, nrow=2, ncol=2) ) ys <- list( A=1L, B=NA_integer_ ) for (x in xs) { for (y in ys) { for (mode in c("integer", "double")) { storage.mode(x) <- mode storage.mode(y) <- mode str(list(x=x, y=y)) for (OP in c("+", "-", "*", "/")) { for (na.rm in c(FALSE, TRUE)) { cat(sprintf("mode='%s', OP='%s', na.rm=%s\n", mode, OP, na.rm)) suppressWarnings({ z0 <- x_OP_y_R(x, y, OP, na.rm=na.rm) z <- x_OP_y(x, y, OP, na.rm=na.rm) }) str(z) stopifnot(all.equal(z, z0)) } } } # for (mode ...) } # for (y ...) } # for (x ...) matrixStats/tests/rowWeightedMeans_subset.R0000644000175100001440000000162212726216471020752 0ustar hornikuserslibrary("matrixStats") rowWeightedMeans_R <- function(x, w, na.rm=FALSE, ...) { apply(x, MARGIN=1L, FUN=weighted.mean, w=w, na.rm=na.rm, ...) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") for (mode in c("numeric", "integer")) { x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) w <- runif(6, min=0, max=6) storage.mode(x) <- mode storage.mode(w) <- mode if (mode == "numeric") w[1] <- Inf for (rows in indexCases) { for (cols in indexCases) { for (na.rm in c(TRUE, FALSE)) { validateIndicesTestMatrix_w(x, w, rows, cols, ftest=rowWeightedMeans, fsure=rowWeightedMeans_R, na.rm=na.rm) validateIndicesTestMatrix_w(x, w, rows, cols, fcolTest=colWeightedMeans, fsure=rowWeightedMeans_R, na.rm=na.rm) } } } } matrixStats/tests/rowCollapse_subset.R0000644000175100001440000000171212726216471017770 0ustar hornikuserslibrary("matrixStats") rowCollapse_R <- function(x, idxs, ...) { ans <- c() storage.mode(ans) <- storage.mode(x) for (ii in seq_len(length(idxs))) { ans[ii] <- x[ii,idxs[ii]] } ans } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) storage.mode(x) <- "integer" idxs <- seq_len(6) for (rows in indexCases) { if (is.null(rows)) rows <- seq_len(nrow(x)) suppressWarnings({ actual <- tryCatch(rowCollapse(x, idxs, rows=rows), error=function(c) "error") expect <- tryCatch(rowCollapse_R(x[rows,,drop=FALSE], idxs[rows]), error=function(c) "error") }) stopifnot(all.equal(actual, expect)) suppressWarnings({ actual <- tryCatch(colCollapse(t(x), idxs, cols=rows), error=function(c) "error") }) stopifnot(all.equal(actual, expect)) } matrixStats/tests/validateIndices.R0000644000175100001440000000175612726216471017211 0ustar hornikuserslibrary(matrixStats) source("utils/validateIndicesFramework.R") x <- 1:6 for (idxs in indexCases) { for (mode in c("integer", "numeric", "logical")) { if (!is.null(idxs)) { suppressWarnings({storage.mode(idxs) <- mode}) } validateIndicesTestVector(x, idxs, ftest=function(x, idxs) { validateIndices(idxs, length(x)) }, fsure=function(x) x) } } for (idxs in indexErrorCases) { validateIndicesTestVector(x, idxs, ftest=function(x, idxs) { validateIndices(idxs, length(x)) }, fsure=function(x) x) } x <- matrix(1:36, 6, 6) for (rows in indexCases) { for (cols in indexCases) { validateIndicesTestMatrix(x, rows, cols, ftest=function(x, rows, cols) { vr <- validateIndices(rows, dim(x)[1], FALSE) vc <- validateIndices(cols, dim(x)[2], FALSE) stopifnot(all((vr > 0 & vr <= dim(x)[1]) | is.na(vr))) stopifnot(all((vc > 0 & vc <= dim(x)[2]) | is.na(vc))) suppressWarnings({x[vr,vc,drop=FALSE]}) }, fsure=function(x) x) } } matrixStats/tests/rowAvgsPerColSet_subset.R0000644000175100001440000000233112726216471020705 0ustar hornikuserslibrary("matrixStats") # - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") X <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) #W <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) for (rows in indexCases) { for (cols in indexCases) { if (is.null(rows)) { rows <- seq_len(nrow(X)) rows. <- rows } else { rows. <- rows[is.finite(rows)] } if (is.null(cols)) { cols <- seq_len(ncol(X)) cols. <- cols } else { cols. <- cols[is.finite(cols)] } suppressWarnings({ actual <- tryCatch(rowAvgsPerColSet(X, rows=rows, S=matrix(cols, ncol=1), FUN=rowMeans), error=function(c) "error") expect <- tryCatch(rowMeans(X[rows,cols.,drop=FALSE], na.rm=TRUE), error=function(c) "error") }) stopifnot(all.equal(as.vector(actual), expect)) suppressWarnings({ actual <- tryCatch(colAvgsPerRowSet(X, cols=cols, S=matrix(rows, ncol=1), FUN=colMeans), error=function(c) "error") expect <- tryCatch(colMeans(X[rows.,cols,drop=FALSE], na.rm=TRUE), error=function(c) "error") }) stopifnot(all.equal(as.vector(actual), expect)) } } matrixStats/tests/rowSds_subset.R0000644000175100001440000000255412726216471016764 0ustar hornikuserslibrary("matrixStats") rowSds_R <- function(x, na.rm=FALSE) { suppressWarnings({ apply(x, MARGIN=1L, FUN=sd, na.rm=na.rm) }) } colSds_R <- function(x, na.rm=FALSE) { suppressWarnings({ apply(x, MARGIN=2L, FUN=sd, na.rm=na.rm) }) } rowSds_center <- function(x, rows=NULL, cols=NULL, na.rm=FALSE) { center <- rowWeightedMeans(x, cols=cols, na.rm=na.rm) rowSds(x, rows=rows, cols=cols, center=center, na.rm=na.rm) } colSds_center <- function(x, rows=NULL, cols=NULL, na.rm=FALSE) { center <- colWeightedMeans(x, rows=rows, na.rm=na.rm) colSds(x, rows=rows, cols=cols, center=center, na.rm=na.rm) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) storage.mode(x) <- "integer" for (rows in indexCases) { for (cols in indexCases) { for (na.rm in c(TRUE, FALSE)) { validateIndicesTestMatrix(x, rows, cols, ftest=rowSds, fsure=rowSds_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, ftest=rowSds_center, fsure=rowSds_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, fcolTest=colSds, fsure=rowSds_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, fcolTest=colSds_center, fsure=rowSds_R, na.rm=na.rm) } } } matrixStats/tests/rowDiffs_subset.R0000644000175100001440000000206712726216471017265 0ustar hornikuserslibrary("matrixStats") rowDiffs_R <- function(x, lag=1L, differences=1L, ...) { ncol2 <- ncol(x) - lag*differences if (ncol2 <= 0) { return(matrix(x[integer(0L)], nrow=nrow(x), ncol=0L)) } suppressWarnings({ y <- apply(x, MARGIN=1L, FUN=diff, lag=lag, differences=differences) }) y <- t(y) dim(y) <- c(nrow(x), ncol2) y } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) storage.mode(x) <- "integer" for (rows in indexCases) { for (cols in indexCases) { for (lag in 1:2) { for (differences in 1:3) { validateIndicesTestMatrix(x, rows, cols, ftest=rowDiffs, fsure=rowDiffs_R, lag=lag, differences=differences) validateIndicesTestMatrix(x, rows, cols, ftest=function(x, rows, cols, ...) { t(colDiffs(t(x), rows=cols, cols=rows, ...)) }, fsure=rowDiffs_R, lag=lag, differences=differences) } } } } matrixStats/tests/rowRanges.R0000644000175100001440000001141312726216471016057 0ustar hornikuserslibrary("matrixStats") rowMins_R <- function(x, ...) { suppressWarnings({ apply(x, MARGIN=1L, FUN=min, ...) }) } # rowMins_R() rowMaxs_R <- function(x, ...) { suppressWarnings({ apply(x, MARGIN=1L, FUN=max, ...) }) } # rowMaxs_R() rowRanges_R <- function(x, ...) { suppressWarnings({ ans <- t(apply(x, MARGIN=1L, FUN=range, ...)) }) dim(ans) <- c(dim(x)[1], 2) ans } # rowRanges_R() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # With and without some NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { cat("mode: ", mode, "\n", sep="") for (addNA in c(FALSE, TRUE)) { cat("addNA=", addNA, "\n", sep="") x <- matrix(1:100+0.1, nrow=20, ncol=5) if (addNA) { x[13:17,c(2,4)] <- NA_real_ } storage.mode(x) <- mode str(x) # Row/column extremes for (na.rm in c(FALSE, TRUE)) { cat("na.rm=", na.rm, "\n", sep="") # Ranges cat("range:\n") r0 <- rowRanges_R(x, na.rm=na.rm) r1 <- rowRanges(x, na.rm=na.rm) r2 <- colRanges(t(x), na.rm=na.rm) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) # Min cat("min:\n") m0 <- rowMins_R(x, na.rm=na.rm) m1 <- rowMins(x, na.rm=na.rm) m2 <- colMins(t(x), na.rm=na.rm) stopifnot(all.equal(m1, m2)) stopifnot(all.equal(m1, m0)) # Max cat("max:\n") m0 <- rowMaxs_R(x, na.rm=na.rm) m1 <- rowMaxs(x, na.rm=na.rm) m2 <- colMaxs(t(x), na.rm=na.rm) stopifnot(all.equal(m1, m2)) stopifnot(all.equal(m1, m0)) } } # for (addNA ...) } # for (mode ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # All NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { cat("mode: ", mode, "\n", sep="") x <- matrix(NA_real_, nrow=20, ncol=5) storage.mode(x) <- mode str(x) for (na.rm in c(FALSE, TRUE)) { cat("na.rm=", na.rm, "\n", sep="") r0 <- rowRanges_R(x, na.rm=na.rm) r1 <- rowRanges(x, na.rm=na.rm) r2 <- colRanges(t(x), na.rm=na.rm) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) } } # for (mode ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Special cases # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Nx0 matrix x <- matrix(double(0L), nrow=5L, ncol=0L) r0 <- rowRanges_R(x) #r1 <- rowRanges(x) #rT <- matrix(c(Inf,-Inf), nrow=nrow(x), ncol=2L, byrow=TRUE) #stopifnot(all.equal(r1,rT)) # 0xN matrix x <- t(x) #r1 <- colRanges(x) #stopifnot(all.equal(r1,rT)) # Nx1 matrix x <- matrix(1:5, nrow=5L, ncol=1L) r1 <- rowRanges(x) rT <- matrix(1:5, nrow=nrow(x), ncol=2L, byrow=FALSE) stopifnot(all.equal(r1,rT)) # 1xN matrix x <- t(x) r1 <- colRanges(x) stopifnot(all.equal(r1,rT)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Additional tests with NA_integer_, NA_real, NaN, -Inf, +Inf # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(1:12, nrow=4, ncol=3) naList <- list( "integer" = matrix(1:12, nrow=4, ncol=3), "integer w/ NA" = matrix(NA_integer_, nrow=4, ncol=3), "real" = matrix(as.double(1:12), nrow=4, ncol=3), "real w/ NA" = matrix(NA_real_, nrow=4, ncol=3) ) na <- naList[["real"]] na[2,2] <- NA naList[["real + NA cell"]] <- na na <- naList[["real"]] na[2,] <- NA naList[["real + NA row"]] <- na na <- naList[["real"]] na[2,] <- NaN naList[["real + NaN row"]] <- na na <- naList[["real"]] na[2,2] <- Inf naList[["real + Inf cell"]] <- na na <- naList[["real"]] na[2,] <- Inf naList[["real + Inf row"]] <- na na <- naList[["real"]] na[2,2] <- NaN naList[["real + NaN cell"]] <- na na <- naList[["real w/ NA"]] na[2,2] <- NaN naList[["real w/ NA + NaN cell"]] <- na na <- naList[["real w/ NA"]] na[2,] <- NaN naList[["real w/ NA + NaN row"]] <- na for (na.rm in c(FALSE, TRUE)) { for (name in names(naList)) { na <- naList[[name]] cat(sprintf("%s (%s) w/ na.rm=%s:\n", name, typeof(na), na.rm)) print(na) cat(" min:\n") y0 <- rowMins_R(na, na.rm=na.rm) str(y0) y1 <- rowMins(na, na.rm=na.rm) str(y1) stopifnot(all.equal(y1, y0)) y1c <- colMins(t(na), na.rm=na.rm) str(y1c) stopifnot(all.equal(y1c, y1)) cat(" max:\n") y0 <- rowMaxs_R(na, na.rm=na.rm) str(y0) y1 <- rowMaxs(na, na.rm=na.rm) str(y1) stopifnot(all.equal(y1, y0)) y1c <- colMaxs(t(na), na.rm=na.rm) str(y1c) stopifnot(all.equal(y1c, y1)) cat(" range:\n") y0 <- rowRanges_R(na, na.rm=na.rm) str(y0) y1 <- rowRanges(na, na.rm=na.rm) str(y1) stopifnot(all.equal(y1, y0)) y1c <- colRanges(t(na), na.rm=na.rm) str(y1c) stopifnot(all.equal(y1c, y1)) } # for (name ...) } # for (na.rm ...) matrixStats/tests/allocArray.R0000644000175100001440000000103312726216471016176 0ustar hornikuserslibrary("matrixStats") allocArray_R <- function(nrow, ncol, value=NA) { array(data=value, dim=dim) } # allocArray_R() values <- list( -1L, 0L, +1L, NA_integer_, .Machine$integer.max, -1, 0, +1, NA_real_, NaN, -Inf, +Inf, .Machine$double.xmin, .Machine$double.xmax, .Machine$double.eps, .Machine$double.neg.eps, FALSE, TRUE, NA ) dim <- c(5L, 10L, 4L) for (value in values) { X0 <- allocArray_R(dim, value=value) X <- allocArray(dim, value=value) str(list(dim=dim, value=value, X=X, X0=X0)) stopifnot(identical(X,X0)) } matrixStats/tests/count.R0000644000175100001440000000474712726216471015254 0ustar hornikuserslibrary("matrixStats") count_R <- function(x, value=TRUE, na.rm=FALSE, ...) { if (is.na(value)) { counts <- sum(is.na(x)) } else { counts <- sum(x == value, na.rm=na.rm) } as.integer(counts) } # count_R() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Data type: integer and numeric # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { x <- runif(20*5, min=-3, max=3) x[sample.int(length(x), size=7)] <- 0 storage.mode(x) <- mode for (na.rm in c(FALSE, TRUE)) { # Count zeros n0 <- count_R(x, value=0, na.rm=na.rm) n1 <- count(x, value=0, na.rm=na.rm) stopifnot(identical(n1, n0)) all <- allValue(x, value=0, na.rm=na.rm) any <- anyValue(x, value=0, na.rm=na.rm) # Count NAs n0 <- count_R(x, value=NA, na.rm=na.rm) n1 <- count(x, value=NA, na.rm=na.rm) stopifnot(identical(n1, n0)) all <- allValue(x, value=NA, na.rm=na.rm) any <- anyValue(x, value=NA, na.rm=na.rm) if (mode == "integer") { ux <- unique(as.vector(x)) n0 <- n1 <- integer(length(x)) for (value in ux) { n0 <- n0 + count_R(x, value=value, na.rm=na.rm) n1 <- n1 + count(x, value=value, na.rm=na.rm) stopifnot(identical(n1, n0)) } stopifnot(all(n0 == ncol(x))) } # if (mode == "integer") } # for (na.rm ...) } # for (mode ...) # All NAs naList <- list(NA_integer_, NA_real_, NaN) for (naValue in naList) { x <- rep(naValue, times=100L) for (na.rm in c(FALSE, TRUE)) { n0 <- count_R(x, na.rm=na.rm) n1 <- count(x, na.rm=na.rm) stopifnot(identical(n1, n0)) # Count NAs n0 <- count_R(x, value=NA, na.rm=na.rm) n1 <- count(x, value=NA, na.rm=na.rm) stopifnot(identical(n1, n0)) any <- anyValue(x, value=NA, na.rm=na.rm) all <- allValue(x, value=NA, na.rm=na.rm) stopifnot(any) stopifnot(all) } } # for (naValue ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Data type: logical # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- logical(length=100L) x[13:17] <- TRUE # Row/column counts for (na.rm in c(FALSE, TRUE)) { n0 <- count_R(x, na.rm=na.rm) n1 <- count(x, na.rm=na.rm) stopifnot(identical(n1, n0)) nT <- count(x, value=TRUE, na.rm=na.rm) nF <- count(x, value=FALSE, na.rm=na.rm) stopifnot(nT + nF == ncol(x)) # Count NAs n0 <- count_R(x, value=NA, na.rm=na.rm) n1 <- count(x, value=NA, na.rm=na.rm) stopifnot(identical(n1, n0)) } matrixStats/tests/sumOver.R0000644000175100001440000001060612726216471015553 0ustar hornikuserslibrary("matrixStats") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Consistency checks # - - - - - - - - - - - - - - - - - - - - - - - - - - - - set.seed(1) sumOver_R <- function(x, na.rm=FALSE, idxs=NULL) { if (is.null(idxs)) { sum(x, na.rm=na.rm) } else { sum(x[idxs], na.rm=na.rm) } } # sumOver_R() cat("Consistency checks:\n") K <- if (Sys.getenv("_R_CHECK_FULL_") == "") 4 else 20 for (kk in seq_len(K)) { cat("Random test #", kk, "\n", sep="") # Simulate data in a matrix of any shape n <- sample(1e3, size=1L) x <- rnorm(n, sd=100) # Add NAs? if ((kk %% 4) %in% c(3,0)) { cat("Adding NAs\n") nna <- sample(n, size=1L) naValues <- c(NA_real_, NaN) x[sample(length(x), size=nna)] <- sample(naValues, size=nna, replace=TRUE) } # Integer or double? if ((kk %% 4) %in% c(2,0)) { cat("Coercing to integers\n") storage.mode(x) <- "integer" } na.rm <- sample(c(TRUE,FALSE), size=1L) # Sum over all y0 <- sumOver_R(x, na.rm=na.rm) y1 <- sumOver(x, na.rm=na.rm) stopifnot(all.equal(y1,y0)) # Sum over subset nidxs <- sample(n, size=1L) idxs <- sample(n, size=nidxs) y0 <- sumOver_R(x, na.rm=na.rm, idxs=idxs) y1 <- sumOver(x, na.rm=na.rm, idxs=idxs) stopifnot(all.equal(y1,y0)) } # for (kk ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # All missing values # - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (n in 0:2) { for (na.rm in c(FALSE, TRUE)) { x <- rep(NA_real_, times=n) y0 <- sum(x, na.rm=na.rm) y <- sumOver(x, na.rm=na.rm) stopifnot(all.equal(y, y0)) x <- rep(NA_integer_, times=n) y0 <- sum(x, na.rm=na.rm) y <- sumOver(x, na.rm=na.rm) stopifnot(all.equal(y, y0)) } } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Summing of zero elements # - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- integer(0) s1 <- sum(x) s2 <- sumOver(x) stopifnot(identical(s1, s2)) x <- 1:10 idxs <- integer(0) s1 <- sum(x[idxs]) s2 <- sumOver(x, idxs=idxs) stopifnot(identical(s1, s2)) x <- rep(NA_integer_, times=10L) s1 <- sum(x, na.rm=TRUE) s2 <- sumOver(x, na.rm=TRUE) stopifnot(identical(s1, s2)) x <- rep(NA_integer_, times=10L) idxs <- 1:5 s1 <- sum(x[idxs], na.rm=TRUE) s2 <- sumOver(x, idxs=idxs, na.rm=TRUE) stopifnot(identical(s1, s2)) x <- double(0) s1 <- sum(x) s2 <- sumOver(x) stopifnot(identical(s1, s2)) x <- as.double(1:10) idxs <- integer(0) s1 <- sum(x[idxs]) s2 <- sumOver(x, idxs=idxs) stopifnot(identical(s1, s2)) x <- rep(NA_real_, times=10L) s1 <- sum(x, na.rm=TRUE) s2 <- sumOver(x, na.rm=TRUE) stopifnot(identical(s1, s2)) x <- rep(NA_real_, times=10L) idxs <- 1:5 s1 <- sum(x[idxs], na.rm=TRUE) s2 <- sumOver(x, idxs=idxs, na.rm=TRUE) stopifnot(identical(s1, s2)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Summing of large integers # - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- c(.Machine$integer.max, 1L, -.Machine$integer.max) # Total gives integer overflow s1 <- sum(x[1:2]) # NA_integer_ s2 <- sumOver(x[1:2]) # NA_integer_ stopifnot(identical(s1, s2)) # Total gives integer overflow (coerce to numeric) s1 <- sum(as.numeric(x[1:2])) # 2147483648 s2 <- sumOver(as.numeric(x[1:2])) # 2147483648 s3 <- sumOver(x[1:2], mode="double") # 2147483648 stopifnot(identical(s1, s2)) stopifnot(identical(s1, s3)) # Cumulative sum would give integer overflow but not the total s1 <- sum(x) # 1L s2 <- sumOver(x) # 1L stopifnot(identical(s1, s2)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Summing of large doubles # - - - - - - - - - - - - - - - - - - - - - - - - - - - - ## Double overflow x <- rep(.Machine$double.xmax, times=2L) y0 <- sum(x) print(y0) y <- sumOver(x) print(y) stopifnot(is.infinite(y) && y > 0) stopifnot(identical(y, y0)) x <- rep(-.Machine$double.xmax, times=2L) y0 <- sum(x) print(y0) y <- sumOver(x) print(y) stopifnot(is.infinite(y) && y < 0) stopifnot(identical(y, y0)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'idxs' # - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- 1:10 idxsList <- list( integer=1:5, double=as.double(1:5), logical=(x <= 5) ) for (idxs in idxsList) { cat("idxs:\n") str(idxs) s1 <- sum(x[idxs], na.rm=TRUE) s2 <- sumOver(x, idxs=idxs, na.rm=TRUE) stopifnot(identical(s1, s2)) } matrixStats/tests/rowWeightedMeans.R0000644000175100001440000000452612726216471017373 0ustar hornikuserslibrary("matrixStats") set.seed(1) x <- matrix(rnorm(20), nrow=5, ncol=4) print(x) # Non-weighted row averages xM0 <- rowMeans(x) xM1 <- rowWeightedMeans(x) print(xM1) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedMeans(t(x)) stopifnot(all.equal(xM2, xM0)) # Weighted row averages (uniform weights) w <- rep(2.5, ncol(x)) xM1 <- rowWeightedMeans(x, w=w) print(xM1) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedMeans(t(x), w=w) stopifnot(all.equal(xM2, xM0)) # Weighted row averages (excluding some columns) w <- c(1,1,0,1) xM0 <- rowMeans(x[,(w == 1),drop=FALSE]) xM1 <- rowWeightedMeans(x, w=w) print(xM1) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedMeans(t(x), w=w) stopifnot(all.equal(xM2, xM0)) # Weighted row averages (excluding some columns) w <- c(0,1,0,0) xM0 <- rowMeans(x[,(w == 1),drop=FALSE]) xM1 <- rowWeightedMeans(x, w=w) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedMeans(t(x), w=w) stopifnot(all.equal(xM2, xM0)) # Weighted row averages (all zero weights) w <- c(0,0,0,0) xM0 <- rowMeans(x[,(w == 1),drop=FALSE]) xM1 <- rowWeightedMeans(x, w=w) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedMeans(t(x), w=w) stopifnot(all.equal(xM2, xM0)) # Weighted averages by rows and columns w <- 1:4 xM1 <- rowWeightedMeans(x, w=w) print(xM1) xM2 <- colWeightedMeans(t(x), w=w) stopifnot(all.equal(xM2, xM1)) x[sample(length(x), size=0.3*length(x))] <- NA print(x) # Non-weighted row averages with missing values xM0 <- rowMeans(x, na.rm=TRUE) xM1 <- rowWeightedMeans(x, na.rm=TRUE) print(xM1) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedMeans(t(x), na.rm=TRUE) stopifnot(all.equal(xM2, xM0)) # Weighted row averages with missing values xM0 <- apply(x, MARGIN=1, FUN=weighted.mean, w=w, na.rm=TRUE) print(xM0) xM1 <- rowWeightedMeans(x, w=w, na.rm=TRUE) print(xM1) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedMeans(t(x), w=w, na.rm=TRUE) stopifnot(all.equal(xM2, xM0)) # Weighted averages by rows and columns w <- 1:4 xM1 <- rowWeightedMeans(x, w=w, na.rm=TRUE) xM2 <- colWeightedMeans(t(x), w=w, na.rm=TRUE) stopifnot(all.equal(xM2, xM1)) # w contains missing value w[1] <- NA_integer_ xM1 <- rowWeightedMeans(x, w=w, na.rm=TRUE) xM2 <- colWeightedMeans(t(x), w=w, na.rm=TRUE) stopifnot(all.equal(xM2, xM1)) xM1 <- rowWeightedMeans(x, w=w, na.rm=FALSE) xM2 <- colWeightedMeans(t(x), w=w, na.rm=FALSE) stopifnot(all.equal(xM2, xM1)) matrixStats/tests/rowCounts_subset.R0000644000175100001440000000326712726216471017510 0ustar hornikuserslibrary("matrixStats") rowCounts_R <- function(x, value=TRUE, na.rm=FALSE, ...) { if (is.na(value)) { counts <- apply(x, MARGIN=1L, FUN=function(x) sum(is.na(x))) } else { counts <- apply(x, MARGIN=1L, FUN=function(x) sum(x == value, na.rm=na.rm)) } as.integer(counts) } # rowCounts_R() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-3, max=3), nrow=6, ncol=6) x[2:3,3:4] <- NA_real_ storage.mode(x) <- "integer" for (rows in indexCases) { for (cols in indexCases) { validateIndicesTestMatrix(x, rows, cols, ftest=rowCounts, fsure=rowCounts_R, value=0, na.rm=TRUE) validateIndicesTestMatrix(x, rows, cols, fcolTest=colCounts, fsure=rowCounts_R, value=0, na.rm=TRUE) for (value in c(0, NA_integer_)) { validateIndicesTestMatrix(x, rows, cols, ftest=rowCounts, fsure=rowCounts_R, value=value) validateIndicesTestMatrix(x, rows, cols, fcolTest=colCounts, fsure=rowCounts_R, value=value) } } } x <- matrix(rep(letters, length.out=6*6), nrow=6, ncol=6) x[2:3,3:4] <- NA_character_ for (rows in indexCases) { for (cols in indexCases) { validateIndicesTestMatrix(x, rows, cols, ftest=rowCounts, fsure=rowCounts_R, value="g", na.rm=TRUE) validateIndicesTestMatrix(x, rows, cols, fcolTest=colCounts, fsure=rowCounts_R, value="g", na.rm=TRUE) for (value in c("g", NA_character_)) { validateIndicesTestMatrix(x, rows, cols, ftest=rowCounts, fsure=rowCounts_R, value=value) validateIndicesTestMatrix(x, rows, cols, fcolTest=colCounts, fsure=rowCounts_R, value=value) } } } matrixStats/tests/rowCumMinMaxs.R0000644000175100001440000000712312726216471016664 0ustar hornikuserslibrary("matrixStats") rowCummins_R <- function(x) { suppressWarnings({ y <- t(apply(x, MARGIN=1L, FUN=cummin)) }) dim(y) <- dim(x) y } rowCummaxs_R <- function(x) { mode <- storage.mode(x) # Change mode because a bug is detected on cummax for integer in R-3.2.0 storage.mode(x) <- "numeric" suppressWarnings({ y <- t(apply(x, MARGIN=1L, FUN=cummax)) }) dim(y) <- dim(x) storage.mode(y) <- mode y } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # With and without some NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { for (addNA in c(FALSE, TRUE)) { cat("addNA=", addNA, "\n", sep="") x <- matrix(1:100, nrow=20, ncol=5) if (addNA) { x[13:17,c(2,4)] <- NA_real_ } cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) # Row/column ranges r0 <- rowCummins_R(x) r1 <- rowCummins(x) r2 <- t(colCummins(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) r0 <- rowCummaxs_R(x) r1 <- rowCummaxs(x) r2 <- t(colCummaxs(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # for (addNA ...) } # for (mode ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # All NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { x <- matrix(NA_real_, nrow=20, ncol=5) cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) r0 <- rowCummins_R(x) r1 <- rowCummins(x) r2 <- t(colCummins(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) r0 <- rowCummaxs_R(x) r1 <- rowCummaxs(x) r2 <- t(colCummaxs(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # for (mode ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A 1x1 matrix # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { x <- matrix(0, nrow=1, ncol=1) cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) r0 <- rowCummins_R(x) r1 <- rowCummins(x) r2 <- t(colCummins(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) r0 <- rowCummaxs_R(x) r1 <- rowCummaxs(x) r2 <- t(colCummaxs(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # for (mode ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Corner cases # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { cat("mode: ", mode, "\n", sep="") value <- 0 storage.mode(value) <- mode # A 0x0 matrix x <- matrix(value, nrow=0L, ncol=0L) str(x) r0 <- matrix(value, nrow=nrow(x), ncol=ncol(x)) r1 <- rowCummins(x) r2 <- t(colCummins(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) # A 0xK matrix x <- matrix(value, nrow=0L, ncol=5L) str(x) r0 <- matrix(value, nrow=nrow(x), ncol=ncol(x)) r1 <- rowCummins(x) r2 <- t(colCummins(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) # A Nx0 matrix x <- matrix(value, nrow=5L, ncol=0L) str(x) r0 <- matrix(value, nrow=nrow(x), ncol=ncol(x)) r1 <- rowCummins(x) r2 <- t(colCummins(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # for (mode ...) matrixStats/tests/meanOver.R0000644000175100001440000000530212726216471015664 0ustar hornikuserslibrary("matrixStats") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Consistency checks # - - - - - - - - - - - - - - - - - - - - - - - - - - - - set.seed(1) meanOver_R <- function(x, na.rm=FALSE, idxs=NULL) { if (is.null(idxs)) { mean(x, na.rm=na.rm) } else { mean(x[idxs], na.rm=na.rm) } } # meanOver_R() cat("Consistency checks:\n") K <- if (Sys.getenv("_R_CHECK_FULL_") == "") 4 else 20 for (kk in seq_len(K)) { cat("Random test #", kk, "\n", sep="") # Simulate data in a matrix of any shape n <- sample(1e3, size=1L) x <- rnorm(n, sd=100) # Add NAs? if ((kk %% 4) %in% c(3,0)) { cat("Adding NAs\n") nna <- sample(n, size=1L) naValues <- c(NA_real_, NaN) x[sample(length(x), size=nna)] <- sample(naValues, size=nna, replace=TRUE) } # Integer or double? if ((kk %% 4) %in% c(2,0)) { cat("Coercing to integers\n") storage.mode(x) <- "integer" } na.rm <- sample(c(TRUE,FALSE), size=1L) # Sum over all y0 <- meanOver_R(x, na.rm=na.rm) y1 <- meanOver(x, na.rm=na.rm) stopifnot(all.equal(y1,y0)) # Sum over subset nidxs <- sample(n, size=1L) idxs <- sample(n, size=nidxs) y0 <- meanOver_R(x, na.rm=na.rm, idxs=idxs) y1 <- meanOver(x, na.rm=na.rm, idxs=idxs) stopifnot(all.equal(y1,y0)) } # for (kk ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Averaging over zero elements # - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- integer(0) s1 <- mean(x) s2 <- meanOver(x) stopifnot(identical(s1, s2)) x <- 1:10 idxs <- integer(0) s1 <- mean(x[idxs]) s2 <- meanOver(x, idxs=idxs) stopifnot(identical(s1, s2)) x <- rep(NA_integer_, times=10L) s1 <- mean(x, na.rm=TRUE) s2 <- meanOver(x, na.rm=TRUE) stopifnot(identical(s1, s2)) x <- rep(NA_integer_, times=10L) idxs <- 1:5 s1 <- mean(x[idxs], na.rm=TRUE) s2 <- meanOver(x, idxs=idxs, na.rm=TRUE) stopifnot(identical(s1, s2)) x <- double(0) s1 <- mean(x) s2 <- meanOver(x) stopifnot(identical(s1, s2)) x <- as.double(1:10) idxs <- integer(0) s1 <- mean(x[idxs]) s2 <- meanOver(x, idxs=idxs) stopifnot(identical(s1, s2)) x <- rep(NA_real_, times=10L) s1 <- mean(x, na.rm=TRUE) s2 <- meanOver(x, na.rm=TRUE) stopifnot(identical(s1, s2)) x <- rep(NA_real_, times=10L) idxs <- 1:5 s1 <- mean(x[idxs], na.rm=TRUE) s2 <- meanOver(x, idxs=idxs, na.rm=TRUE) stopifnot(identical(s1, s2)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'idxs' # - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- 1:10 idxsList <- list( integer=1:5, double=as.double(1:5), logical=(x <= 5) ) for (idxs in idxsList) { cat("idxs:\n") str(idxs) s1 <- mean(x[idxs], na.rm=TRUE) s2 <- meanOver(x, idxs=idxs, na.rm=TRUE) stopifnot(identical(s1, s2)) } matrixStats/tests/meanOver_subset.R0000644000175100001440000000067312726216471017257 0ustar hornikuserslibrary("matrixStats") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- runif(6, min=-6, max=6) storage.mode(x) <- "integer" for (idxs in indexCases) { validateIndicesTestVector(x, idxs, ftest=meanOver, fsure=mean, na.rm=FALSE) validateIndicesTestVector(x, idxs, ftest=meanOver, fsure=mean, na.rm=TRUE) } matrixStats/tests/rowMedians_subset.R0000644000175100001440000000142112726216471017603 0ustar hornikuserslibrary("matrixStats") rowMedians_R <- function(x, na.rm=FALSE, ...) { apply(x, MARGIN=1L, FUN=median, na.rm=na.rm) } colMedians_R <- function(x, na.rm=FALSE, ...) { apply(x, MARGIN=2L, FUN=median, na.rm=na.rm) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-3, max=3), nrow=6, ncol=6) storage.mode(x) <- "integer" for (rows in indexCases) { for (cols in indexCases) { for (na.rm in c(TRUE, FALSE)) { validateIndicesTestMatrix(x, rows, cols, ftest=rowMedians, fsure=rowMedians_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, fcolTest=colMedians, fsure=rowMedians_R, na.rm=na.rm) } } } matrixStats/tests/indexByRow.R0000644000175100001440000000300212726216471016175 0ustar hornikuserslibrary("matrixStats") indexByRow_R1 <- function(dim, idxs=NULL, ...) { n <- prod(dim) x <- matrix(seq_len(n), nrow=dim[2L], ncol=dim[1L], byrow=TRUE) if (!is.null(idxs)) x <- x[idxs] as.vector(x) } # indexByRow_R1() indexByRow_R2 <- function(dim, idxs=NULL, ...) { n <- prod(dim) if (is.null(idxs)) { x <- matrix(seq_len(n), nrow=dim[2L], ncol=dim[1L], byrow=TRUE) as.vector(x) } else { idxs <- idxs - 1 cols <- idxs %/% dim[2L] rows <- idxs %% dim[2L] cols + dim[1L]*rows + 1L } } # indexByRow_R2() dim <- c(5L, 4L) X <- matrix(NA_integer_, nrow=dim[1L], ncol=dim[2L]) Y <- t(X) idxsByCols <- seq_along(X) # Assign by columns X[idxsByCols] <- idxsByCols print(X) # Truth Y0 <- t(X) idxsByRows <- as.vector(Y0) # Assert idxs <- indexByRow(dim) stopifnot(all.equal(idxs, idxsByRows)) Y <- X Y[idxsByRows] <- idxs print(Y) stopifnot(all(as.vector(Y) == as.vector(X))) idxs_R1 <- indexByRow_R1(dim) stopifnot(all.equal(idxs_R1, idxsByRows)) idxs_R2 <- indexByRow_R2(dim) stopifnot(all.equal(idxs_R2, idxsByRows)) # Assert idxsByCols <- seq(from=1, to=length(X), by=3L) idxsByRows <- as.vector(t(X)[idxsByCols]) idxs <- indexByRow(dim, idxs=idxsByCols) stopifnot(all(idxs == idxsByRows)) idxs_R1 <- indexByRow_R1(dim, idxs=idxsByCols) stopifnot(all(idxs_R1 == idxsByRows)) idxs_R2 <- indexByRow_R2(dim, idxs=idxsByCols) stopifnot(all(idxs_R2 == idxsByRows)) ## DEPRECATED: Backward compatibility idxs0 <- indexByRow(dim) idxs1 <- indexByRow(X) stopifnot(identical(idxs1, idxs0)) matrixStats/tests/binCounts.R0000644000175100001440000000477412726216471016070 0ustar hornikuserslibrary("matrixStats") library("stats") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Local functions # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - binCounts_hist <- function(x, bx, right=FALSE, ...) { n0 <- graphics::hist(x, breaks=bx, right=right, include.lowest=TRUE, plot=FALSE)$counts } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Non-sorted and sorted positions # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - nx <- 1e5 # Number of data points nb <- 2e3 # Number of bins # Uniformely distributed bins bx <- seq(from=0, to=1, length.out=nb+1L) bx <- c(-1, bx, 2) # Sample data points set.seed(0xBEEF) x <- runif(nx, min=0, max=1) for (kk in 1:2) { n0 <- binCounts_hist(x, bx=bx) n1 <- binCounts(x, bx=bx) # Sanity check stopifnot(identical(n1, n0)) # Check reversed n1r <- rev(binCounts(-x, bx=rev(-bx), right=TRUE)) stopifnot(identical(n1r, n1)) # Retry with a sorted vector x <- sort(x) } # for (kk in 1:2) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Missing values # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- 1:200 x[100] <- NA_integer_ nx <- length(x) # Bins bx <- c(0.5,50.5,100.5,150.5,200.5) yS0 <- binCounts_hist(x, bx=bx) yS <- binCounts(x, bx=bx) # Sanity check stopifnot(all.equal(yS, yS0)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Border cases # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- 1:10 bx <- min(x) - c(10,1) yS <- binCounts(x, bx=bx) stopifnot(all.equal(yS, 0L)) bx <- range(x) yS <- binCounts(x, bx=bx) stopifnot(all.equal(yS, length(x)-1L)) bx <- max(x) + c(1,10) yS <- binCounts(x, bx=bx) stopifnot(all.equal(yS, 0L)) # Every second empty x <- 1:10 bx <- rep(x, each=2L) yS <- binCounts(x, bx=bx) stopifnot(all.equal(yS, rep(c(0L,1L), length.out=length(bx)-1L))) ## NOTE: binCounts_hist() does not give the same last bin count # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Exception handling # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Zero bin bounderies (invalid bin definition) bx <- double(0L) res <- try(yS <- binCounts(1:10, bx=bx), silent=TRUE) stopifnot(inherits(res, "try-error")) # One bin boundery (invalid bin definition) bx <- double(1L) res <- try(yS <- binCounts(1:10, bx=bx), silent=TRUE) stopifnot(inherits(res, "try-error")) matrixStats/tests/varDiff_etal.R0000644000175100001440000000516012726216471016500 0ustar hornikuserslibrary("matrixStats") set.seed(1) x <- rnorm(1e4) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Variance estimators # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - sigma2A <- var(x) cat(sprintf("var(x)=%g\n", sigma2A)) sigma2B <- varDiff(x) cat(sprintf("varDiff(x)=%g\n", sigma2B)) d <- abs(sigma2B - sigma2A) cat(sprintf("Absolute difference=%g\n", d)) stopifnot(d < 0.02) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Standard deviation estimators # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - sigmaA <- sd(x) cat(sprintf("sd(x)=%g\n", sigmaA)) sigmaB <- sdDiff(x) cat(sprintf("sdDiff(x)=%g\n", sigmaB)) d <- abs(sigmaB - sigmaA) cat(sprintf("Absolute difference=%g\n", d)) stopifnot(d < 0.01) # Sanity checks stopifnot(abs(sigma2A - sigmaA^2) < 1e-9) stopifnot(abs(sigma2B - sigmaB^2) < 1e-9) sigmaA2 <- mad(x) cat(sprintf("mad(x)=%g\n", sigmaA2)) sigmaB2 <- madDiff(x) cat(sprintf("madDiff(x)=%g\n", sigmaB2)) d <- abs(sigmaB2 - sigmaA2) cat(sprintf("Absolute difference=%g\n", d)) stopifnot(d < 0.05) sigmaA3 <- IQR(x) cat(sprintf("IQR(x)=%g\n", sigmaA3)) sigmaB3 <- iqrDiff(x) cat(sprintf("iqrDiff(x)=%g\n", sigmaB3)) d <- abs(sigmaB3 - sigmaA3) cat(sprintf("Absolute difference=%g\n", d)) stopifnot(d < 0.05) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Trimmed estimators # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - y <- x outliers <- sample(length(x), size=0.1*length(x)) y[outliers] <- 100*y[outliers] sigmaAo <- sd(y[-outliers]) cat(sprintf("sd(y)=%g\n", sigmaAo)) sigmaBo <- sdDiff(y[-outliers]) cat(sprintf("sdDiff(y)=%g\n", sigmaBo)) d <- abs(sigmaB - sigmaA) cat(sprintf("Absolute difference=%g\n", d)) stopifnot(d < 0.01) sigmaBot <- sdDiff(y, trim=0.05) cat(sprintf("sdDiff(y, trim=0.05)=%g\n", sigmaBot)) d <- abs(sigmaBot - sigmaA) cat(sprintf("Absolute difference=%g\n", d)) #stopifnot(d < 1e-3) sigmaCot <- madDiff(y, trim=0.05) cat(sprintf("madDiff(y, trim=0.05)=%g\n", sigmaCot)) sigmaDot <- iqrDiff(y, trim=0.05) cat(sprintf("iqrDiff(y, trim=0.05)=%g\n", sigmaDot)) FUNs <- list( varDiff=varDiff, sdDiff=sdDiff, madDiff=madDiff, iqrDiff=iqrDiff ) for (fcn in names(FUNs)) { cat(sprintf("%s()...\n", fcn)) FUN <- FUNs[[fcn]] for (mode in c("integer", "double")) { cat("mode: ", mode, "", sep="") for (n in 0:3) { x <- runif(n, min=-5, max=5) storage.mode(x) <- mode str(x) y <- FUN(x) yt <- FUN(x, trim=0.1) str(list("non-trimmed"=y, trimmed=yt)) } # for (mode ...) } cat(sprintf("%s()...DONE\n", fcn)) } # for (fcn ...) matrixStats/tests/rowOrderStats.R0000644000175100001440000000273212726216471016736 0ustar hornikuserslibrary("matrixStats") library("stats") rowOrderStats_R <- function(x, probs, ...) { ans <- apply(x, MARGIN=1L, FUN=quantile, probs=probs, type=3L) # Remove Attributes attributes(ans) <- NULL ans } # rowOrderStats_R() set.seed(1) K <- if (Sys.getenv("_R_CHECK_FULL_") == "") 5 else 3 # Simulate data in a matrix of any shape nrow <- 300 ncol <- 100 x <- rnorm(nrow*ncol) dim(x) <- c(nrow, ncol) probs <- 0.3 which <- round(probs*ncol) y0 <- rowOrderStats_R(x, probs=probs) y1 <- rowOrderStats(x, which=which) stopifnot(all.equal(y1,y0)) y2 <- colOrderStats(t(x), which=which) stopifnot(all.equal(y2,y0)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Consistency checks # - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { cat("Consistency checks without NAs:\n") for (kk in seq_len(K)) { cat("Random test #", kk, "\n", sep="") # Simulate data in a matrix of any shape nrow <- sample(100, size=1) ncol <- sample(100, size=1) x <- rnorm(nrow*ncol) dim(x) <- c(nrow, ncol) cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) probs <- runif(1) which <- round(probs*ncol) y0 <- rowOrderStats_R(x, probs=probs) y1 <- rowOrderStats(x, which=which) stopifnot(all.equal(y1,y0)) y2 <- colOrderStats(t(x), which=which) stopifnot(all.equal(y2,y0)) } # for (kk in ...) } # for (mode ...) matrixStats/tests/rowMads_subset.R0000644000175100001440000000255712726216471017122 0ustar hornikuserslibrary("matrixStats") rowMads_R <- function(x, na.rm=FALSE) { suppressWarnings({ apply(x, MARGIN=1L, FUN=mad, na.rm=na.rm) }) } colMads_R <- function(x, na.rm=FALSE) { suppressWarnings({ apply(x, MARGIN=2L, FUN=mad, na.rm=na.rm) }) } rowMads_center <- function(x, rows=NULL, cols=NULL, na.rm=FALSE) { center <- rowMedians(x, cols=cols, na.rm=na.rm) rowMads(x, rows=rows, cols=cols, center=center, na.rm=na.rm) } colMads_center <- function(x, rows=NULL, cols=NULL, na.rm=FALSE) { center <- colMedians(x, rows=rows, na.rm=na.rm) colMads(x, rows=rows, cols=cols, center=center, na.rm=na.rm) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) storage.mode(x) <- "integer" for (rows in indexCases) { for (cols in indexCases) { for (na.rm in c(TRUE, FALSE)) { validateIndicesTestMatrix(x, rows, cols, ftest=rowMads, fsure=rowMads_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, ftest=rowMads_center, fsure=rowMads_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, fcolTest=colMads, fsure=rowMads_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, fcolTest=colMads_center, fsure=rowMads_R, na.rm=na.rm) } } } matrixStats/tests/rowVarDiffs.R0000644000175100001440000000370712726216471016353 0ustar hornikuserslibrary("matrixStats") FUNs <- list( rowVarDiffs=list(rowVarDiffs, colVarDiffs), rowSdDiffs=list(rowSdDiffs, colSdDiffs), rowMadDiffs=list(rowMadDiffs, colMadDiffs), rowIQRDiffs=list(rowIQRDiffs, colIQRDiffs) ) for (fcn in names(FUNs)) { cat(sprintf("%s()...\n", fcn)) rFUN <- FUNs[[fcn]][[1L]] cFUN <- FUNs[[fcn]][[2L]] # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # With and without some NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { for (addNA in c(FALSE, TRUE)) { cat("addNA=", addNA, "\n", sep="") x <- matrix(1:100+0.1, nrow=20, ncol=5) if (addNA) { x[13:17,c(2,4)] <- NA_real_ } cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) # Row/column ranges for (na.rm in c(FALSE, TRUE)) { cat("na.rm=", na.rm, "\n", sep="") r1 <- rFUN(x, na.rm=na.rm) r2 <- cFUN(t(x), na.rm=na.rm) stopifnot(all.equal(r1, r2)) } } # for (addNA ...) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # All NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { x <- matrix(NA_real_, nrow=20, ncol=5) cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) for (na.rm in c(FALSE, TRUE)) { cat("na.rm=", na.rm, "\n", sep="") r1 <- rFUN(x, na.rm=na.rm) r2 <- cFUN(t(x), na.rm=na.rm) stopifnot(all.equal(r1, r2)) } } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A 1x1 matrix # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(0, nrow=1, ncol=1) for (na.rm in c(FALSE, TRUE)) { cat("na.rm=", na.rm, "\n", sep="") r1 <- rFUN(x, na.rm=na.rm) r2 <- cFUN(t(x), na.rm=na.rm) stopifnot(all.equal(r1, r2)) } cat(sprintf("%s()...DONE\n", fcn)) } # for (fcn ...) matrixStats/tests/rowCumsums.R0000644000175100001440000000542112726216471016276 0ustar hornikuserslibrary("matrixStats") rowCumsums_R <- function(x) { suppressWarnings({ y <- t(apply(x, MARGIN=1L, FUN=cumsum)) }) dim(y) <- dim(x) y } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # With and without some NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { for (addNA in c(FALSE, TRUE)) { cat("addNA=", addNA, "\n", sep="") x <- matrix(1:100, nrow=20, ncol=5) if (addNA) { x[13:17,c(2,4)] <- NA_real_ } cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) # Row/column ranges r0 <- rowCumsums_R(x) r1 <- rowCumsums(x) r2 <- t(colCumsums(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # for (addNA ...) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # All NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { x <- matrix(NA_real_, nrow=20, ncol=5) cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) r0 <- rowCumsums_R(x) r1 <- rowCumsums(x) r2 <- t(colCumsums(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # for (mode ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A 1x1 matrix # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { x <- matrix(0, nrow=1, ncol=1) cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) r0 <- rowCumsums_R(x) r1 <- rowCumsums(x) r2 <- t(colCumsums(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Corner cases # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { cat("mode: ", mode, "\n", sep="") value <- 0 storage.mode(value) <- mode # A 0x0 matrix x <- matrix(value, nrow=0L, ncol=0L) str(x) r0 <- matrix(value, nrow=nrow(x), ncol=ncol(x)) r1 <- rowCumsums(x) r2 <- t(colCumsums(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) # A 0xK matrix x <- matrix(value, nrow=0L, ncol=5L) str(x) r0 <- matrix(value, nrow=nrow(x), ncol=ncol(x)) r1 <- rowCumsums(x) r2 <- t(colCumsums(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) # A Nx0 matrix x <- matrix(value, nrow=5L, ncol=0L) str(x) r0 <- matrix(value, nrow=nrow(x), ncol=ncol(x)) r1 <- rowCumsums(x) r2 <- t(colCumsums(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # for (mode ...) matrixStats/tests/binMeans,binCounts_subset.R0000644000175100001440000000260112726216471021171 0ustar hornikuserslibrary("matrixStats") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Naive R implementation of binMeans() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - binMeans0 <- function(y, x, bx, na.rm=TRUE, count=TRUE, right=FALSE) { B <- length(bx)-1L res <- double(B) counts <- rep(NaN, times=B) if (na.rm) { keep <- !is.na(x) & !is.na(y) x <- x[keep] y <- y[keep] } # For each bin... for (kk in seq_len(B)) { if (right) { idxs <- which(bx[kk] < x & x <= bx[kk+1L]) } else { idxs <- which(bx[kk] <= x & x < bx[kk+1L]) } yKK <- y[idxs] muKK <- mean(yKK) res[kk] <- muKK counts[kk] <- length(idxs) } # for (kk ...) if (count) attr(res, "count") <- counts res } # binMeans0() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") y <- runif(6, min=-6, max=6) x <- runif(6, min=-6, max=6) storage.mode(x) <- "integer" bx <- c(-6, 0, 3, 4, 10) for (idxs in indexCases) { for (na.rm in c(TRUE, FALSE)) { validateIndicesTestVector_w(y, x, idxs, ftest=binMeans, fsure=binMeans0, bx=bx, na.rm=na.rm, count=TRUE, right=FALSE) validateIndicesTestVector_w(y, x, idxs, ftest=binMeans, fsure=binMeans0, bx=bx, na.rm=na.rm, count=TRUE, right=TRUE) } } matrixStats/tests/benchmark.R0000644000175100001440000000043212726216471016041 0ustar hornikusersif (Sys.getenv("_R_CHECK_FULL_") != "" && Sys.getenv("_R_CHECK_USE_VALGRIND_") == "") { ## Benchmarks requires knitr, which requires R (>= 3.0.2) if (require("R.rsp") && getRversion() >= "3.0.2") { html <- matrixStats:::benchmark('binCounts') print(html) } } # _R_CHECK_FULL_ matrixStats/tests/rowVarDiffs_var,sd_subset.R0000644000175100001440000000213012726216471021200 0ustar hornikuserslibrary("matrixStats") FUNs <- list( varDiffs=c(rowVarDiffs, colVarDiffs), sdDiff=c(rowSdDiffs, colSdDiffs) ) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") for (fcn in names(FUNs)) { cat(sprintf("subsetted tests on %s()...\n", fcn)) rowFUN <- FUNs[[fcn]][[1L]] colFUN <- FUNs[[fcn]][[2L]] for (mode in c("numeric", "integer")) { x <- matrix(runif(6*6, min=-6, max=6), nrow=6L, ncol=6L) storage.mode(x) <- mode trim <- runif(1, min=0, max=0.5) if (mode == "numeric") x[1:2,3:4] <- Inf for (diff in 1:2) { for (rows in indexCases) { for (cols in indexCases) { for (na.rm in c(TRUE, FALSE)) { validateIndicesTestMatrix(x, rows, cols, ftest=rowFUN, fsure=rowFUN, na.rm=na.rm, diff=diff, trim=trim) validateIndicesTestMatrix(x, rows, cols, fcolTest=colFUN, fsure=rowFUN, na.rm=na.rm, diff=diff, trim=trim) } } } } } cat(sprintf("%s()...DONE\n", fcn)) } matrixStats/tests/rowRanks_subset.R0000644000175100001440000000150512726216471017304 0ustar hornikuserslibrary("matrixStats") rowRanks_R <- function(x, ties.method="average", ...) { ans <- t(apply(x, MARGIN=1L, FUN=rank, na.last="keep", ties.method=ties.method)) dim(ans) <- dim(x) ans } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) storage.mode(x) <- "integer" for (rows in indexCases) { for (cols in indexCases) { validateIndicesTestMatrix(x, rows, cols, ftest=rowRanks, fsure=rowRanks_R, ties.method="average") validateIndicesTestMatrix(x, rows, cols, ftest=function(x, rows, cols, ...) { t(colRanks(t(x), rows=cols, cols=rows, preserveShape=TRUE, ...)) }, fsure=rowRanks_R, ties.method="average") } } matrixStats/tests/rowVars_subset.R0000644000175100001440000000257412726216471017150 0ustar hornikuserslibrary("matrixStats") rowVars_R <- function(x, na.rm=FALSE) { suppressWarnings({ apply(x, MARGIN=1L, FUN=var, na.rm=na.rm) }) } colVars_R <- function(x, na.rm=FALSE) { suppressWarnings({ apply(x, MARGIN=2L, FUN=var, na.rm=na.rm) }) } rowVars_center <- function(x, rows=NULL, cols=NULL, na.rm=FALSE) { center <- rowWeightedMeans(x, cols=cols, na.rm=na.rm) rowVars(x, rows=rows, cols=cols, center=center, na.rm=na.rm) } colVars_center <- function(x, rows=NULL, cols=NULL, na.rm=FALSE) { center <- colWeightedMeans(x, rows=rows, na.rm=na.rm) colVars(x, rows=rows, cols=cols, center=center, na.rm=na.rm) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) storage.mode(x) <- "integer" for (rows in indexCases) { for (cols in indexCases) { for (na.rm in c(TRUE, FALSE)) { validateIndicesTestMatrix(x, rows, cols, ftest=rowVars, fsure=rowVars_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, ftest=rowVars_center, fsure=rowVars_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, fcolTest=colVars, fsure=rowVars_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, fcolTest=colVars_center, fsure=rowVars_R, na.rm=na.rm) } } } matrixStats/tests/zzz.package-unload.R0000644000175100001440000000227412726216471017624 0ustar hornikusers## These tests need to be last of all tests, otherwise ## covr::package_coverage() gives an error. cat("1. Loading package\n") loadNamespace("matrixStats") stopifnot("matrixStats" %in% loadedNamespaces()) cat("2. Unloading package\n") unloadNamespace("matrixStats") stopifnot(!"matrixStats" %in% loadedNamespaces()) if (FALSE) { ## 'covr' gives "Error in library("matrixStats") : ## there is no package called 'matrixStats'" here, cf. ## https://travis-ci.org/HenrikBengtsson/matrixStats/builds/48015577 cat("3. Attaching package\n") library("matrixStats") stopifnot("package:matrixStats" %in% search()) cat("4. Detaching package\n") detach("package:matrixStats") stopifnot(!"package:matrixStats" %in% search()) stopifnot("matrixStats" %in% loadedNamespaces()) cat("5. Unloading package\n") unloadNamespace("matrixStats") stopifnot(!"matrixStats" %in% loadedNamespaces()) cat("6. Attaching package (again)\n") library("matrixStats") stopifnot("package:matrixStats" %in% search()) cat("7. Detaching package (again)\n") detach("package:matrixStats") stopifnot(!"package:matrixStats" %in% search()) stopifnot("matrixStats" %in% loadedNamespaces()) } cat("7. DONE\n") matrixStats/tests/rowAvgsPerColSet.R0000644000175100001440000000403712726216471017325 0ustar hornikuserslibrary("matrixStats") X <- matrix(rnorm(20*6), nrow=20, ncol=6) rownames(X) <- LETTERS[1:nrow(X)] colnames(X) <- letters[1:ncol(X)] print(X) # - - - - - - - - - - - - - - - - - - - - - - - - - - # Apply rowMeans() for 3 sets of 2 columns # - - - - - - - - - - - - - - - - - - - - - - - - - - nbrOfSets <- 3 S <- matrix(1:ncol(X), ncol=nbrOfSets) colnames(S) <- sprintf("s%d", 1:nbrOfSets) print(S) Z <- rowAvgsPerColSet(X, S=S) print(Z) # Validation Z0 <- cbind(s1=rowMeans(X[,1:2]), s2=rowMeans(X[,3:4]), s3=rowMeans(X[,5:6])) stopifnot(identical(drop(Z), Z0)) # - - - - - - - - - - - - - - - - - - - - - - - - - - # Apply colMeans() for 5 sets of 4 rows # - - - - - - - - - - - - - - - - - - - - - - - - - - nbrOfSets <- 5 S <- matrix(1:nrow(X), ncol=nbrOfSets) colnames(S) <- sprintf("s%d", 1:nbrOfSets) print(S) Z <- colAvgsPerRowSet(X, S=S) print(Z) # Validation Z0 <- rbind(s1=colMeans(X[1:4,]), s2=colMeans(X[5:8,]), s3=colMeans(X[9:12,]), s4=colMeans(X[13:16,]), s5=colMeans(X[17:20,])) stopifnot(identical(drop(Z), Z0)) # - - - - - - - - - - - - - - - - - - - - - - - - - - # When there is only one "complete" set # - - - - - - - - - - - - - - - - - - - - - - - - - - nbrOfSets <- 1 S <- matrix(1:ncol(X), ncol=nbrOfSets) colnames(S) <- sprintf("s%d", 1:nbrOfSets) print(S) Z <- rowAvgsPerColSet(X, S=S, FUN=rowMeans) print(Z) Z0 <- rowMeans(X) stopifnot(identical(drop(Z), Z0)) nbrOfSets <- 1 S <- matrix(1:nrow(X), ncol=nbrOfSets) colnames(S) <- sprintf("s%d", 1:nbrOfSets) print(S) Z <- colAvgsPerRowSet(X, S=S, FUN=colMeans) print(Z) Z0 <- colMeans(X) stopifnot(identical(drop(Z), Z0)) # - - - - - - - - - - - - - - - - - - - - - - - - - - # Use weights # - - - - - - - - - - - - - - - - - - - - - - - - - - nbrOfSets <- 3 S <- matrix(1:ncol(X), ncol=nbrOfSets) colnames(S) <- sprintf("s%d", 1:nbrOfSets) print(S) W <- matrix(runif(length(X)), nrow=nrow(X), ncol=ncol(X)) Z1 <- rowAvgsPerColSet(X, W=W, S=S, FUN=rowWeightedMeans) print(Z1) Z2 <- colAvgsPerRowSet(X, W=W, S=S, FUN=colWeightedMeans) print(Z2) matrixStats/tests/rowCumprods_subset.R0000644000175100001440000000134012726216471020017 0ustar hornikuserslibrary("matrixStats") rowCumprods_R <- function(x) { suppressWarnings({ y <- t(apply(x, MARGIN=1L, FUN=cumprod)) }) dim(y) <- dim(x) y } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-3, max=3), nrow=6, ncol=6) storage.mode(x) <- "integer" for (rows in indexCases) { for (cols in indexCases) { validateIndicesTestMatrix(x, rows, cols, ftest=rowCumprods, fsure=rowCumprods_R) validateIndicesTestMatrix(x, rows, cols, ftest=function(x, rows, cols, ...) { t(colCumprods(t(x), rows=cols, cols=rows)) }, fsure=rowCumprods_R) } } matrixStats/tests/rowRanges_subset.R0000644000175100001440000000252712726216471017452 0ustar hornikuserslibrary("matrixStats") rowMins_R <- function(x, ...) { suppressWarnings({ apply(x, MARGIN=1L, FUN=min, ...) }) } # rowMins_R() rowMaxs_R <- function(x, ...) { suppressWarnings({ apply(x, MARGIN=1L, FUN=max, ...) }) } # rowMaxs_R() rowRanges_R <- function(x, ...) { suppressWarnings({ ans <- t(apply(x, MARGIN=1L, FUN=range, ...)) }) dim(ans) <- c(dim(x)[1], 2) ans } # rowRanges_R() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) storage.mode(x) <- "integer" for (rows in indexCases) { for (cols in indexCases) { for (na.rm in c(TRUE, FALSE)) { validateIndicesTestMatrix(x, rows, cols, ftest=rowRanges, fsure=rowRanges_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, ftest=rowMins, fsure=rowMins_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, ftest=rowMaxs, fsure=rowMaxs_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, fcolTest=colRanges, fsure=rowRanges_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, fcolTest=colMins, fsure=rowMins_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, fcolTest=colMaxs, fsure=rowMaxs_R, na.rm=na.rm) } } } matrixStats/tests/rowWeightedVars.R0000644000175100001440000000530312726216471017235 0ustar hornikuserslibrary("matrixStats") set.seed(1) x <- matrix(rnorm(20), nrow=5L, ncol=4L) print(x) # Weighted row variances (uniform weights - all w=1) # Non-weighted row variances xM0 <- rowVars(x) w <- rep(1, times=ncol(x)) xM1 <- rowWeightedVars(x, w=w) print(xM1) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedVars(t(x), w=w) stopifnot(all.equal(xM2, xM0)) # Weighted row variances (uniform weights - all w=3) x3 <- cbind(x, x, x) xM0 <- rowVars(x3) w <- rep(3, times=ncol(x)) xM1 <- rowWeightedVars(x, w=w) print(xM1) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedVars(t(x), w=w) stopifnot(all.equal(xM2, xM0)) # Weighted row variances (excluding some columns) w <- c(1,1,0,1) xM0 <- rowVars(x[,(w == 1),drop=FALSE]) xM1 <- rowWeightedVars(x, w=w) print(xM1) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedVars(t(x), w=w) stopifnot(all.equal(xM2, xM0)) # Weighted row variances (excluding some columns) w <- c(0,1,0,0) xM0 <- rowVars(x[,(w == 1),drop=FALSE]) xM1 <- rowWeightedVars(x, w=w) #stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedVars(t(x), w=w) stopifnot(all.equal(xM2, xM1)) # Weighted row variances (all zero weights) w <- c(0,0,0,0) xM0 <- rowVars(x[,(w == 1),drop=FALSE]) xM1 <- rowWeightedVars(x, w=w) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedVars(t(x), w=w) stopifnot(all.equal(xM2, xM0)) # Weighted variances by rows and columns w <- 1:4 xM1 <- rowWeightedVars(x, w=w) print(xM1) xM2 <- colWeightedVars(t(x), w=w) stopifnot(all.equal(xM2, xM1)) x[sample(length(x), size=0.3*length(x))] <- NA print(x) # Non-weighted row variances with missing values xM0 <- rowVars(x, na.rm=TRUE) xM1 <- rowWeightedVars(x, w=rep(1, times=ncol(x)), na.rm=TRUE) print(xM1) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedVars(t(x), w=rep(1, times=ncol(x)), na.rm=TRUE) stopifnot(all.equal(xM2, xM0)) # Weighted row variances with missing values xM1 <- rowWeightedVars(x, w=w, na.rm=TRUE) print(xM1) xM2 <- colWeightedVars(t(x), w=w, na.rm=TRUE) stopifnot(all.equal(xM2, xM1)) # Weighted variances by rows and columns w <- 1:4 xM1 <- rowWeightedVars(x, w=w, na.rm=TRUE) xM2 <- colWeightedVars(t(x), w=w, na.rm=TRUE) stopifnot(all.equal(xM2, xM1)) # Weighted row standard deviation (excluding some columns) w <- c(1,1,0,1) ## FIXME: rowVars()/rowSds() needs na.rm=FALSE (wrong default) xM0 <- rowSds(x[,(w == 1),drop=FALSE], na.rm=FALSE) xM1 <- rowWeightedSds(x, w=w) print(xM1) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedSds(t(x), w=w, na.rm=FALSE) stopifnot(all.equal(xM2, xM0)) # Weighted row MADs (excluding some columns) w <- c(1,1,0,1) xM0 <- rowMads(x[,(w == 1),drop=FALSE]) xM1 <- rowWeightedMads(x, w=w) print(xM1) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedMads(t(x), w=w) stopifnot(all.equal(xM2, xM0)) matrixStats/tests/rowIQRs_subset.R0000644000175100001440000000217312726216471017046 0ustar hornikuserslibrary("matrixStats") rowIQRs_R <- function(x, na.rm=FALSE) { quantileNA <- function(x, ..., na.rm=FALSE) { if (!na.rm && anyMissing(x)) return(c(NA_real_, NA_real_)) quantile(x, ..., na.rm=na.rm) } Q <- apply(x, MARGIN=1L, FUN=quantileNA, probs=c(0.25, 0.75), na.rm=na.rm) dim(Q) <- c(2L, nrow(x)) Q[2L,,drop=TRUE] - Q[1L,,drop=TRUE] } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- runif(6, min=-6, max=6) for (idxs in indexCases) { for (na.rm in c(TRUE, FALSE)) { validateIndicesTestVector(x, idxs, ftest=iqr, fsure=function(x, na.rm) { dim(x) <- c(1L, length(x)) rowIQRs_R(x, na.rm=na.rm) }, na.rm=na.rm) } } x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) for (rows in indexCases) { for (cols in indexCases) { for (na.rm in c(TRUE, FALSE)) { validateIndicesTestMatrix(x, rows, cols, ftest=rowIQRs, fsure=rowIQRs_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, fcolTest=colIQRs, fsure=rowIQRs_R, na.rm=na.rm) } } } matrixStats/tests/rowCumsums_subset.R0000644000175100001440000000133212726216471017660 0ustar hornikuserslibrary("matrixStats") rowCumsums_R <- function(x) { suppressWarnings({ y <- t(apply(x, MARGIN=1L, FUN=cumsum)) }) dim(y) <- dim(x) y } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-3, max=3), nrow=6, ncol=6) storage.mode(x) <- "integer" for (rows in indexCases) { for (cols in indexCases) { validateIndicesTestMatrix(x, rows, cols, ftest=rowCumsums, fsure=rowCumsums_R) validateIndicesTestMatrix(x, rows, cols, ftest=function(x, rows, cols, ...) { t(colCumsums(t(x), rows=cols, cols=rows)) }, fsure=rowCumsums_R) } } matrixStats/tests/rowTabulates_subset.R0000644000175100001440000000133012726216471020146 0ustar hornikuserslibrary("matrixStats") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) storage.mode(x) <- "integer" for (rows in indexCases) { for (cols in indexCases) { validateIndicesTestMatrix(x, rows, cols, ftest=rowTabulates, fsure=rowTabulates) validateIndicesTestMatrix(x, rows, cols, ftest=rowTabulates, fsure=rowTabulates, values=1:3) validateIndicesTestMatrix(x, rows, cols, ftest=colTabulates, fsure=colTabulates) validateIndicesTestMatrix(x, rows, cols, ftest=colTabulates, fsure=colTabulates, values=1:3) } } matrixStats/tests/allocMatrix.R0000644000175100001440000000111112726216471016361 0ustar hornikuserslibrary("matrixStats") allocMatrix_R <- function(nrow, ncol, value=NA) { matrix(data=value, nrow=nrow, ncol=ncol) } # allocMatrix_R() values <- list( -1L, 0L, +1L, NA_integer_, .Machine$integer.max, -1, 0, +1, NA_real_, NaN, -Inf, +Inf, .Machine$double.xmin, .Machine$double.xmax, .Machine$double.eps, .Machine$double.neg.eps, FALSE, TRUE, NA ) nrow <- 5L ncol <- 10L for (value in values) { X0 <- allocMatrix_R(nrow, ncol, value=value) X <- allocMatrix(nrow, ncol, value=value) str(list(nrow=nrow, ncol=ncol, value=value, X=X, X0=X0)) stopifnot(identical(X,X0)) } matrixStats/tests/signTabulate_subset.R0000644000175100001440000000156212726216471020123 0ustar hornikuserslibrary("matrixStats") signTabulate0 <- function(x, ...) { nneg <- sum(x < 0, na.rm=TRUE) nzero <- sum(x == 0, na.rm=TRUE) npos <- sum(x > 0, na.rm=TRUE) nna <- sum(is.na(x)) nneginf <- sum(is.infinite(x) & x < 0, na.rm=TRUE) nposinf <- sum(is.infinite(x) & x > 0, na.rm=TRUE) res <- c(nneg, nzero, npos, nna, nneginf, nposinf) res <- as.double(res) names(res) <- c("-1", "0", "+1", "NA", "-Inf", "+Inf") if (is.integer(x)) res <- res[1:4] res } # signTabulate0() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) x[2:3,4:5] <- +Inf x[4:5,1:2] <- -Inf for (idxs in indexCases) { validateIndicesTestVector(x, idxs, ftest=signTabulate, fsure=signTabulate0) } matrixStats/tests/anyMissing_subset.R0000644000175100001440000000073512726216471017623 0ustar hornikuserslibrary("matrixStats") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- runif(6, min=-3, max=3) x[2] <- NA for (mode in c("integer", "numeric")) { storage.mode(x) <- mode for (idxs in indexCases) { validateIndicesTestVector(x, idxs, ftest=anyMissing, fsure=function(x, ...) { anyValue(x, value=NA) }) } } matrixStats/tests/rowLogSumExps_subset.R0000644000175100001440000000132012726216471020267 0ustar hornikuserslibrary("matrixStats") rowLogSumExps_R <- function(x, ...) { apply(x, MARGIN=1L, FUN=function(rx, ...) { log(sum(exp(rx), ...)) }, ...) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) for (rows in indexCases) { for (cols in indexCases) { for (na.rm in c(TRUE, FALSE)) { validateIndicesTestMatrix(x, rows, cols, ftest=rowLogSumExps, fsure=rowLogSumExps_R, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, fcoltest=colLogSumExps, fsure=rowLogSumExps_R, na.rm=na.rm) } } } matrixStats/tests/rowWeightedMedians.R0000644000175100001440000000365512726216471017712 0ustar hornikuserslibrary("matrixStats") set.seed(1) x <- matrix(rnorm(20), nrow=5, ncol=4) print(x) # Non-weighted row medians xM0 <- rowMedians(x) xM1 <- rowWeightedMedians(x) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedMedians(t(x)) stopifnot(all.equal(xM2, xM0)) # Weighted row medians (uniform weights) w <- rep(2.5, ncol(x)) xM1 <- rowWeightedMedians(x, w=w) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedMedians(t(x), w=w) stopifnot(all.equal(xM2, xM0)) # Weighted row medians (excluding some columns) w <- c(1,1,0,1) xM0 <- rowMedians(x[,(w == 1),drop=FALSE]) xM1 <- rowWeightedMedians(x, w=w) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedMedians(t(x), w=w) stopifnot(all.equal(xM2, xM0)) # Weighted row medians (excluding some columns) w <- c(0,1,0,0) xM0 <- rowMedians(x[,(w == 1),drop=FALSE]) xM1 <- rowWeightedMedians(x, w=w) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedMedians(t(x), w=w) stopifnot(all.equal(xM2, xM0)) # Weighted row medians (all zero weights) w <- c(0,0,0,0) xM0 <- rowMedians(x[,(w == 1),drop=FALSE]) xM1 <- rowWeightedMedians(x, w=w) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedMedians(t(x), w=w) stopifnot(all.equal(xM2, xM0)) # Weighted medians by rows and columns w <- 1:4 xM1 <- rowWeightedMedians(x, w=w) xM2 <- colWeightedMedians(t(x), w=w) stopifnot(all.equal(xM2, xM1)) # Weighted row medians with missing values xM0 <- apply(x, MARGIN=1, FUN=weightedMedian, w=w, na.rm=TRUE) print(xM0) xM1 <- rowWeightedMedians(x, w=w, na.rm=TRUE) print(xM1) stopifnot(all.equal(xM1, xM0)) xM2 <- colWeightedMedians(t(x), w=w) stopifnot(all.equal(xM2, xM0)) # Weighted medians by rows and columns w <- 1:4 xM1 <- rowWeightedMedians(x, w=w, na.rm=TRUE) xM2 <- colWeightedMedians(t(x), w=w, na.rm=TRUE) stopifnot(all.equal(xM2, xM1)) # Inf weight x <- matrix(1:2, nrow=1, ncol=2) w <- c(7, Inf) xM1 <- rowWeightedMedians(x, w=w) xM2 <- colWeightedMedians(t(x), w=w) stopifnot(identical(2, xM1)) stopifnot(identical(2, xM2)) matrixStats/tests/rowMedians.R0000644000175100001440000001362112726216471016223 0ustar hornikuserslibrary("matrixStats") rowMedians_R <- function(x, na.rm=FALSE, ...) { apply(x, MARGIN=1L, FUN=median, na.rm=na.rm) } colMedians_R <- function(x, na.rm=FALSE, ...) { apply(x, MARGIN=2L, FUN=median, na.rm=na.rm) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Special case: Non-ties # - - - - - - - - - - - - - - - - - - - - - - - - - - - - cat("Special case: Non-ties:\n") for (mode in c("integer", "double")) { x <- matrix(1:9+0.1, nrow=3, ncol=3) storage.mode(x) <- mode y0 <- rowMedians_R(x, na.rm=FALSE) y1 <- rowMedians(x, na.rm=FALSE) stopifnot(all.equal(y1,y0)) y0 <- colMedians_R(x, na.rm=FALSE) y1 <- colMedians(x, na.rm=FALSE) stopifnot(all.equal(y1,y0)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Special case: Ties # - - - - - - - - - - - - - - - - - - - - - - - - - - - - cat("Special case: Ties:\n") for (mode in c("integer", "double")) { x <- matrix(1:16+0.1, nrow=4, ncol=4) storage.mode(x) <- mode y0 <- rowMedians_R(x, na.rm=FALSE) y1 <- rowMedians(x, na.rm=FALSE) stopifnot(all.equal(y1,y0)) y0 <- colMedians_R(x, na.rm=FALSE) y1 <- colMedians(x, na.rm=FALSE) stopifnot(all.equal(y1,y0)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Special case: Single-element matrix # - - - - - - - - - - - - - - - - - - - - - - - - - - - - cat("Special case: Single-element matrix:\n") for (mode in c("integer", "double")) { x <- matrix(1, nrow=1, ncol=1) storage.mode(x) <- mode y0 <- rowMedians_R(x, na.rm=FALSE) y1 <- rowMedians(x, na.rm=FALSE) stopifnot(all.equal(y1,y0)) y0 <- colMedians_R(x, na.rm=FALSE) y1 <- colMedians(x, na.rm=FALSE) stopifnot(all.equal(y1,y0)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Special case: Empty matrix # - - - - - - - - - - - - - - - - - - - - - - - - - - - - cat("Special case: Empty matrix:\n") for (mode in c("integer", "double")) { x <- matrix(integer(0), nrow=0, ncol=0) storage.mode(x) <- mode y0 <- rowMedians_R(x, na.rm=FALSE) y1 <- rowMedians(x, na.rm=FALSE) stopifnot(all.equal(y1,y0)) y0 <- colMedians_R(x, na.rm=FALSE) y1 <- colMedians(x, na.rm=FALSE) stopifnot(all.equal(y1,y0)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Special case: All NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - cat("Special case: All NAs:\n") for (mode in c("integer", "double")) { x <- matrix(NA_integer_, nrow=3, ncol=3) storage.mode(x) <- mode y0 <- rowMedians_R(x, na.rm=TRUE) y1 <- rowMedians(x, na.rm=TRUE) stopifnot(all.equal(y1,y0)) y0 <- colMedians_R(x, na.rm=TRUE) y1 <- colMedians(x, na.rm=TRUE) stopifnot(all.equal(y1,y0)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Special case: All NaNs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - cat("Special case: All NaNs:\n") x <- matrix(NA_real_, nrow=3, ncol=3) y0 <- rowMedians_R(x, na.rm=TRUE) y1 <- rowMedians(x, na.rm=TRUE) stopifnot(all.equal(y1,y0)) y0 <- colMedians_R(x, na.rm=TRUE) y1 <- colMedians(x, na.rm=TRUE) stopifnot(all.equal(y1,y0)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Special case: All Infs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - cat("Special case: All Infs:\n") x <- matrix(Inf, nrow=3, ncol=3) y0 <- rowMedians_R(x, na.rm=FALSE) y1 <- rowMedians(x, na.rm=FALSE) stopifnot(all.equal(y1,y0)) y0 <- colMedians_R(x, na.rm=FALSE) y1 <- colMedians(x, na.rm=FALSE) stopifnot(all.equal(y1,y0)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Special case: All -Infs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - cat("Special case: All -Infs:\n") x <- matrix(-Inf, nrow=3, ncol=3) y0 <- rowMedians_R(x, na.rm=FALSE) y1 <- rowMedians(x, na.rm=FALSE) stopifnot(all.equal(y1,y0)) y0 <- colMedians_R(x, na.rm=FALSE) y1 <- colMedians(x, na.rm=FALSE) stopifnot(all.equal(y1,y0)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Special case: Infs and -Infs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - cat("Special case: Infs and -Infs:\n") x <- matrix(c(-Inf,+Inf), nrow=4, ncol=4) y0 <- rowMedians_R(x, na.rm=FALSE) y1 <- rowMedians(x, na.rm=FALSE) stopifnot(all.equal(y1,y0)) y0 <- colMedians_R(x, na.rm=FALSE) y1 <- colMedians(x, na.rm=FALSE) stopifnot(all.equal(y1,y0)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Special case: Integer overflow with ties # - - - - - - - - - - - - - - - - - - - - - - - - - - - - cat("Special case: Integer overflow with ties:\n") x <- matrix(.Machine$integer.max, nrow=4, ncol=4) y0 <- rowMedians_R(x, na.rm=FALSE) y1 <- rowMedians(x, na.rm=FALSE) stopifnot(all.equal(y1,y0)) y0 <- colMedians_R(x, na.rm=FALSE) y1 <- colMedians(x, na.rm=FALSE) stopifnot(all.equal(y1,y0)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Consistency checks # - - - - - - - - - - - - - - - - - - - - - - - - - - - - set.seed(1) cat("Consistency checks:\n") K <- if (Sys.getenv("_R_CHECK_FULL_") == "" || Sys.getenv("_R_CHECK_USE_VALGRIND_") != "") 4 else 10 for (kk in seq_len(K)) { cat("Random test #", kk, "\n", sep="") # Simulate data in a matrix of any shape dim <- sample(50:200, size=2) n <- prod(dim) x <- rnorm(n, sd=100) dim(x) <- dim # Add NAs? if ((kk %% 4) %in% c(3,0)) { cat("Adding NAs\n") nna <- sample(n, size=1) naValues <- c(NA_real_, NaN) x[sample(length(x), size=nna)] <- sample(naValues, size=nna, replace=TRUE) } # Integer or double? if ((kk %% 4) %in% c(2,0)) { cat("Coercing to integers\n") storage.mode(x) <- "integer" } na.rm <- sample(c(TRUE,FALSE), size=1) # rowMedians(): y0 <- rowMedians_R(x, na.rm=na.rm) y1 <- rowMedians(x, na.rm=na.rm) stopifnot(all.equal(y1,y0)) y2 <- colMedians(t(x), na.rm=na.rm) stopifnot(all.equal(y2,y0)) # colMedians(): y0 <- colMedians_R(x, na.rm=na.rm) y1 <- colMedians(x, na.rm=na.rm) stopifnot(all.equal(y1,y0)) y2 <- rowMedians(t(x), na.rm=na.rm) stopifnot(all.equal(y2,y0)) } # for (kk ...) matrixStats/tests/rowLogSumExps.R0000644000175100001440000000756412726216471016722 0ustar hornikusers# Test inspired by the harmonic mean example in R-help # thread '[R] Beyond double-precision?' on May 9, 2009. library("matrixStats") library("stats") logSumExp0 <- function(lx) { iMax <- which.max(lx) log1p(sum(exp(lx[-iMax] - lx[iMax]))) + lx[iMax] } # logSumExp0() n <- 1e3 set.seed(1) for (mode in c("integer", "double")) { cat("mode: ", mode, "\n", sep="") X <- matrix(runif(n, min=1.0, max=3.0), nrow=50L) storage.mode(X) <- mode str(X) # The logarithm of the harmonic mean by rows yH <- log(1/rowMeans(1/X)) str(yH) nlX <- -log(X) y0 <- log(ncol(X)) - apply(nlX, MARGIN=1L, FUN=logSumExp0) stopifnot(all.equal(y0,yH)) y1 <- log(ncol(X)) - apply(nlX, MARGIN=1L, FUN=logSumExp) stopifnot(all.equal(y1,y0)) y2 <- log(ncol(X)) - rowLogSumExps(nlX) stopifnot(all.equal(y2,y0)) y3 <- log(ncol(X)) - colLogSumExps(t(nlX)) stopifnot(all.equal(y3,y0)) # The logarithm of the harmonic mean by columns yH <- log(1/colMeans(1/X)) str(yH) y0 <- log(nrow(X)) - apply(nlX, MARGIN=2L, FUN=logSumExp0) stopifnot(all.equal(y0,yH)) y1 <- log(nrow(X)) - apply(nlX, MARGIN=2L, FUN=logSumExp) stopifnot(all.equal(y1,y0)) y2 <- log(nrow(X)) - colLogSumExps(nlX) stopifnot(all.equal(y2,y0)) y3 <- log(nrow(X)) - rowLogSumExps(t(nlX)) stopifnot(all.equal(y3,y0)) # Testing names rownames(nlX) <- seq_len(nrow(X)) colnames(nlX) <- seq_len(ncol(X)) y2 <- rowLogSumExps(nlX) stopifnot(identical(names(y2), rownames(nlX))) y3 <- colLogSumExps(t(nlX)) stopifnot(identical(names(y3), rownames(nlX))) } # for (mode ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Corner cases # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ## Zero-size matrices lX <- matrix(numeric(0L), nrow=0L, ncol=0L) y <- rowLogSumExps(lX) print(y) stopifnot(length(y) == nrow(lX)) y <- colLogSumExps(lX) print(y) stopifnot(length(y) == ncol(lX)) ## Zero-height matrices lX <- matrix(numeric(0L), nrow=0L, ncol=10L) y <- rowLogSumExps(lX) print(y) stopifnot(length(y) == nrow(lX)) y <- colLogSumExps(lX) print(y) stopifnot(length(y) == ncol(lX)) stopifnot(all(y == -Inf)) ## Zero-width matrices lX <- matrix(numeric(0L), nrow=10L, ncol=0L) y <- colLogSumExps(lX) print(y) stopifnot(length(y) == ncol(lX)) y <- rowLogSumExps(lX) print(y) stopifnot(length(y) == nrow(lX)) stopifnot(all(y == -Inf)) ## Matrices with one element lX <- matrix(1.0, nrow=1L, ncol=1L) y <- rowLogSumExps(lX) print(y) stopifnot(length(y) == nrow(lX)) stopifnot(all(y == lX)) y <- colLogSumExps(lX) print(y) stopifnot(length(y) == ncol(lX)) stopifnot(all(y == lX)) ## All missing values lX <- matrix(NA_real_, nrow=1L, ncol=1L) y <- rowLogSumExps(lX, na.rm=TRUE) print(y) stopifnot(length(y) == nrow(lX)) stopifnot(identical(y, -Inf)) lX <- matrix(NA_real_, nrow=1L, ncol=1L) y <- colLogSumExps(lX, na.rm=TRUE) print(y) stopifnot(length(y) == ncol(lX)) stopifnot(identical(y, -Inf)) lX <- matrix(NA_real_, nrow=2L, ncol=2L) y <- rowLogSumExps(lX, na.rm=TRUE) print(y) stopifnot(length(y) == nrow(lX)) stopifnot(all(y == -Inf)) y <- rowLogSumExps(lX, na.rm=FALSE) print(y) stopifnot(length(y) == nrow(lX)) stopifnot(all(is.na(y) & !is.nan(y))) lX <- matrix(NA_real_, nrow=2L, ncol=2L) y <- colLogSumExps(lX, na.rm=TRUE) print(y) stopifnot(length(y) == ncol(lX)) stopifnot(all(y == -Inf)) y <- colLogSumExps(lX, na.rm=FALSE) print(y) stopifnot(length(y) == ncol(lX)) stopifnot(all(is.na(y) & !is.nan(y))) ## +Inf values lX <- matrix(c(1, 2, +Inf), nrow=3L, ncol=2L) y <- colLogSumExps(lX, na.rm=TRUE) print(y) stopifnot(length(y) == ncol(lX)) stopifnot(all(y == +Inf)) ## multiple -Inf values lX <- matrix(c(-Inf, -Inf), nrow=2L, ncol=3L) y <- rowLogSumExps(lX) print(y) stopifnot(length(y) == nrow(lX)) stopifnot(all(y == -Inf)) lX <- matrix(c(-Inf, 5, -Inf), nrow=2L, ncol=3L, byrow=1) y <- rowLogSumExps(lX) print(y) stopifnot(length(y) == nrow(lX)) stopifnot(all(y == 5)) matrixStats/tests/rowVars.R0000644000175100001440000000555612726216471015566 0ustar hornikuserslibrary("matrixStats") rowVars_R <- function(x, na.rm=FALSE) { suppressWarnings({ apply(x, MARGIN=1L, FUN=var, na.rm=na.rm) }) } colVars_R <- function(x, na.rm=FALSE) { suppressWarnings({ apply(x, MARGIN=2L, FUN=var, na.rm=na.rm) }) } rowVars_center <- function(x, rows=NULL, cols=NULL, na.rm=FALSE) { center <- rowWeightedMeans(x, cols=cols, na.rm=na.rm) rowVars(x, rows=rows, cols=cols, center=center, na.rm=na.rm) } colVars_center <- function(x, rows=NULL, cols=NULL, na.rm=FALSE) { center <- colWeightedMeans(x, rows=rows, na.rm=na.rm) colVars(x, rows=rows, cols=cols, center=center, na.rm=na.rm) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # With and without some NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { for (addNA in c(FALSE, TRUE)) { cat("addNA=", addNA, "\n", sep="") x <- matrix(1:100+0.1, nrow=20, ncol=5) if (addNA) { x[13:17,c(2,4)] <- NA_real_ } cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) # Row/column ranges for (na.rm in c(FALSE, TRUE)) { cat("na.rm=", na.rm, "\n", sep="") r0 <- rowVars_R(x, na.rm=na.rm) r1 <- rowVars(x, na.rm=na.rm) r1b <- rowVars_center(x, na.rm=na.rm) r2 <- colVars(t(x), na.rm=na.rm) r2b <- colVars_center(t(x), na.rm=na.rm) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) stopifnot(all.equal(r1b, r1)) stopifnot(all.equal(r2b, r2)) } } # for (addNA ...) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # All NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { x <- matrix(NA_real_, nrow=20, ncol=5) cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) for (na.rm in c(FALSE, TRUE)) { cat("na.rm=", na.rm, "\n", sep="") r0 <- rowVars_R(x, na.rm=na.rm) r1 <- rowVars(x, na.rm=na.rm) r1b <- rowVars_center(x, na.rm=na.rm) r2 <- colVars(t(x), na.rm=na.rm) r2b <- colVars_center(t(x), na.rm=na.rm) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) stopifnot(all.equal(r1b, r1)) stopifnot(all.equal(r2b, r2)) } } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A 1x1 matrix # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(0, nrow=1, ncol=1) for (na.rm in c(FALSE, TRUE)) { cat("na.rm=", na.rm, "\n", sep="") r0 <- rowVars_R(x, na.rm=na.rm) r1 <- rowVars(x, na.rm=na.rm) r1b <- rowVars_center(x, na.rm=na.rm) r2 <- colVars(t(x), na.rm=na.rm) r2b <- colVars_center(t(x), na.rm=na.rm) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) stopifnot(all.equal(r1b, r1)) stopifnot(all.equal(r2b, r2)) } matrixStats/tests/product.R0000644000175100001440000000213612726216471015572 0ustar hornikuserslibrary("matrixStats") for (mode in c("integer", "double")) { cat("mode: ", mode, "\n", sep="") # Empty vector x <- 0 storage.mode(x) <- mode y <- prod(x, na.rm=TRUE) print(y) z <- product(x, na.rm=TRUE) print(z) stopifnot(all.equal(z, y)) # Test negative values x <- c(1,-4,2) storage.mode(x) <- mode y <- prod(x, na.rm=TRUE) print(y) z <- product(x, na.rm=TRUE) print(z) stopifnot(all.equal(z, y)) # Test missing values x <- c(1,NA,NaN,2) storage.mode(x) <- mode y <- prod(x, na.rm=TRUE) print(y) z <- product(x, na.rm=TRUE) print(z) stopifnot(all.equal(z, y)) x <- c(1,NA,NaN,2) storage.mode(x) <- mode y <- prod(x, na.rm=FALSE) print(y) z <- product(x, na.rm=FALSE) print(z) stopifnot(all(is.na(z), is.na(y))) x <- c(1,NaN,2) storage.mode(x) <- mode y <- prod(x, na.rm=FALSE) print(y) stopifnot(is.na(y)) z <- product(x, na.rm=FALSE) print(z) stopifnot(is.na(z)) } # for (mode ...) # NAs following 0s x <- c(0L, NA_integer_) y <- prod(x, na.rm=FALSE) print(y) z <- product(x, na.rm=FALSE) print(z) stopifnot(identical(z, y)) matrixStats/tests/binMeans,binCounts.R0000644000175100001440000000672012726216471017612 0ustar hornikuserslibrary("matrixStats") library("stats") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Naive R implementation of binMeans() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - binMeans0 <- function(y, x, bx, na.rm=TRUE, count=TRUE, right=FALSE) { B <- length(bx)-1L res <- double(B) counts <- rep(NaN, times=B) if (na.rm) { keep <- !is.na(x) & !is.na(y) x <- x[keep] y <- y[keep] } # For each bin... for (kk in seq_len(B)) { if (right) { idxs <- which(bx[kk] < x & x <= bx[kk+1L]) } else { idxs <- which(bx[kk] <= x & x < bx[kk+1L]) } yKK <- y[idxs] muKK <- mean(yKK) res[kk] <- muKK counts[kk] <- length(idxs) } # for (kk ...) if (count) attr(res, "count") <- counts res } # binMeans0() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Case #1 # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- 1:200 nx <- length(x) y <- double(nx) y[1:50] <- 5 y[101:150] <- -5 y <- y + rnorm(nx) # Bins bx <- c(0.5,50.5,100.5,150.5,200.5) yS0 <- binMeans0(y, x=x, bx=bx) yS <- binMeans(y, x=x, bx=bx) nS <- binCounts(x, bx=bx) # Sanity check stopifnot(all.equal(yS, yS0)) stopifnot(all.equal(attr(yS, "count"), nS)) yS0r <- rev(binMeans0(y, x=-x, bx=rev(-bx), count=FALSE, right=TRUE)) ySr <- rev(binMeans(y, x=-x, bx=rev(-bx), count=FALSE, right=TRUE)) # Sanity check stopifnot(all.equal(yS0r, yS0, check.attributes=FALSE)) stopifnot(all.equal(ySr, yS0r)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Case #2 # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - nx <- 1e4 x <- runif(nx) y <- runif(nx) nb <- 20 bx <- do.call(seq, c(as.list(range(x)), length.out=nb)) bx1 <- c(bx[-1], bx[nb] + 1) yS0 <- binMeans0(y, x=x, bx=bx1) yS <- binMeans(y, x=x, bx=bx1) nS <- binCounts(x, bx=bx1) ySr <- rev(binMeans(y, x=-x, bx=rev(-bx1), right=TRUE)) # Sanity check stopifnot(all.equal(yS, yS0)) stopifnot(all.equal(attr(yS, "count"), nS)) stopifnot(all.equal(ySr, yS, check.attributes=FALSE)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Empty bins # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- c(6:8, 16:19) nx <- length(x) y <- runif(nx) bx <- c(0,5,10,15,20,25) yS0 <- binMeans0(y, x=x, bx=bx) yS <- binMeans(y, x=x, bx=bx) nS <- binCounts(x, bx=bx) stopifnot(all.equal(attr(yS, "count"), nS)) stopifnot(all.equal(yS, yS0)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Missing values # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- 1:200 x[100] <- NA_integer_ nx <- length(x) y <- double(nx) y[1:50] <- 5 y[101:150] <- -5 y[123:125] <- NA_real_ y <- y + rnorm(nx) # Bins bx <- c(0.5,50.5,100.5,150.5,200.5) yS0 <- binMeans0(y, x=x, bx=bx) yS <- binMeans(y, x=x, bx=bx) # Sanity check stopifnot(all.equal(yS, yS0)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Exception handling # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Zero bin bounderies (invalid bin definition) bx <- double(0L) res <- try(yS <- binMeans(x=1:10, y=1:10, bx=bx), silent=TRUE) stopifnot(inherits(res, "try-error")) # One bin boundery (invalid bin definition) bx <- double(1L) res <- try(yS <- binMeans(x=1:10, y=1:10, bx=bx), silent=TRUE) stopifnot(inherits(res, "try-error")) matrixStats/tests/allocVector.R0000644000175100001440000000112212726216471016361 0ustar hornikuserslibrary("matrixStats") allocVector_R <- function(length, value=NA) { x <- vector(mode=typeof(value), length=length) if (!is.finite(value) || value != 0) x[] <- value x } # allocVector_R() values <- list( -1L, 0L, +1L, NA_integer_, .Machine$integer.max, -1, 0, +1, NA_real_, NaN, -Inf, +Inf, .Machine$double.xmin, .Machine$double.xmax, .Machine$double.eps, .Machine$double.neg.eps, FALSE, TRUE, NA ) n <- 1e3 for (value in values) { x0 <- allocVector_R(n, value=value) x <- allocVector(n, value=value) str(list(n=n, value=value, x=x, x0=x0)) stopifnot(identical(x,x0)) } matrixStats/tests/weightedMedian.R0000644000175100001440000000450512726216471017032 0ustar hornikuserslibrary("matrixStats") x <- 1:5 y <- weightedMedian(x) y <- weightedMedian(x, w=c(NA,Inf,NA,Inf,NA), na.rm=TRUE) print(y) y <- weightedMedian(x, w=c(NA,Inf,NA,Inf,NA), na.rm=FALSE) print(y) stopifnot(is.na(y)) x <- 1:10 n <- length(x) y1 <- median(x) # 5.5 y2 <- weightedMedian(x) # 5.5 stopifnot(all.equal(y1, y2)) w <- rep(1, n) y1 <- weightedMedian(x, w) # 5.5 (default) y2a <- weightedMedian(x, ties="weighted") # 5.5 (default) y2b <- weightedMedian(x, ties="min") # 5 y2c <- weightedMedian(x, ties="max") # 6 stopifnot(all.equal(y2a,y1)) y3 <- weightedMedian(x, w) # 5.5 (default) # Pull the median towards zero w[1] <- 5 y1 <- weightedMedian(x, w) # 3.5 y <- c(rep(0,w[1]), x[-1]) # Only possible for integer weights y2 <- median(y) # 3.5 stopifnot(all.equal(y1,y2)) # Put even more weight on the zero w[1] <- 8.5 y <- weightedMedian(x, w) # 2 # All weight on the first value w[1] <- Inf y <- weightedMedian(x, w) # 1 # All weight on the last value w[1] <- 1 w[n] <- Inf y <- weightedMedian(x, w) # 10 # All weights set to zero w <- rep(0, n) y <- weightedMedian(x, w) # NA x <- 1:4 w <- rep(1, times=4) for (mode in c("integer", "double")) { storage.mode(x) <- mode for (ties in c("weighted", "mean", "min", "max")) { cat(sprintf("ties=%s\n", ties)) y <- weightedMedian(x, w, ties=ties) } } set.seed(0x42) y <- weightedMedian(x=double(0L)) print(y) stopifnot(length(y) == 1L) stopifnot(is.na(y)) y <- weightedMedian(x=x[1]) print(y) stopifnot(length(y) == 1L) stopifnot(all.equal(y, x[1])) n <- 1e3 x <- runif(n) w <- runif(n, min=0, max=1) for (mode in c("integer", "double")) { storage.mode(x) <- mode for (ties in c("weighted", "mean", "min", "max")) { y <- weightedMedian(x, w, ties=ties) cat(sprintf("mode=%s, ties=%s, result=%g\n", mode, ties, y)) } } # A large vector n <- 1e5 x <- runif(n) w <- runif(n, min=0, max=1) y <- weightedMedian(x, w) y <- weightedMedian(x, w, ties="min") # Single Number xs <- c(1, NA_integer_) ws <- c(1, NA_integer_) for (x in xs) { for (w in ws) { y <- weightedMedian(x=x, w=w) if (is.na(w)) z <- NA_real_ else z <- x[1] stopifnot(all.equal(y, z)) } } matrixStats/tests/sumOver_subset.R0000644000175100001440000000066712726216471017146 0ustar hornikuserslibrary("matrixStats") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- runif(6, min=-6, max=6) storage.mode(x) <- "integer" for (idxs in indexCases) { validateIndicesTestVector(x, idxs, ftest=sumOver, fsure=sum, na.rm=FALSE) validateIndicesTestVector(x, idxs, ftest=sumOver, fsure=sum, na.rm=TRUE) } matrixStats/tests/rowMads.R0000644000175100001440000001256412726216471015534 0ustar hornikuserslibrary("matrixStats") rowMads_R <- function(x, na.rm=FALSE) { suppressWarnings({ apply(x, MARGIN=1L, FUN=mad, na.rm=na.rm) }) } colMads_R <- function(x, na.rm=FALSE) { suppressWarnings({ apply(x, MARGIN=2L, FUN=mad, na.rm=na.rm) }) } rowMads_center <- function(x, rows=NULL, cols=NULL, na.rm=FALSE) { center <- rowMedians(x, cols=cols, na.rm=na.rm) rowMads(x, rows=rows, cols=cols, center=center, na.rm=na.rm) } colMads_center <- function(x, rows=NULL, cols=NULL, na.rm=FALSE) { center <- colMedians(x, rows=rows, na.rm=na.rm) colMads(x, rows=rows, cols=cols, center=center, na.rm=na.rm) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A 3x3 matrix (no ties) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { x <- matrix(c(1,2,3,2,3,4,3,4,5)+0.1, nrow=3, ncol=3) cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) cat("rowMads():\n") r0 <- rowMads_R(x, na.rm=TRUE) r1 <- rowMads(x, na.rm=TRUE) r1b <- rowMads_center(x, na.rm=TRUE) r2 <- colMads(t(x), na.rm=TRUE) r2b <- colMads_center(t(x), na.rm=TRUE) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) stopifnot(all.equal(r1b, r1)) stopifnot(all.equal(r2b, r2)) cat("colMads():\n") r0 <- colMads_R(x, na.rm=TRUE) r1 <- colMads(x, na.rm=TRUE) r1b <- colMads_center(x, na.rm=TRUE) r2 <- rowMads(t(x), na.rm=TRUE) r2b <- rowMads_center(t(x), na.rm=TRUE) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) stopifnot(all.equal(r1b, r1)) stopifnot(all.equal(r2b, r2)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Ties: a 4x4 matrix # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { x <- matrix(c(1:4,2:5,3:6,4:7)+0.1, nrow=4, ncol=4) cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) cat("rowMads():\n") r0 <- rowMads_R(x, na.rm=TRUE) r1 <- rowMads(x, na.rm=TRUE) r2 <- colMads(t(x), na.rm=TRUE) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) cat("colMads():\n") r0 <- colMads_R(x, na.rm=TRUE) r1 <- colMads(x, na.rm=TRUE) r2 <- rowMads(t(x), na.rm=TRUE) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # No ties: a 3x3 matrix with an NA value # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { x <- matrix(c(1,2,3,2,3,4,3,4,5)+0.1, nrow=3, ncol=3) x[2,2] <- NA_real_ cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) cat("rowMads():\n") r0 <- rowMads_R(x, na.rm=TRUE) r1 <- rowMads(x, na.rm=TRUE) r2 <- colMads(t(x), na.rm=TRUE) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) cat("colMads():\n") r0 <- colMads_R(x, na.rm=TRUE) r1 <- colMads(x, na.rm=TRUE) r2 <- rowMads(t(x), na.rm=TRUE) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # With and without some NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (addNA in c(FALSE, TRUE)) { cat("addNA=", addNA, "\n", sep="") x <- matrix(1:100, nrow=20, ncol=5) if (addNA) { x[13:17,c(2,4)] <- NA_real_ } # Row/column ranges for (na.rm in c(FALSE, TRUE)) { cat("na.rm=", na.rm, "\n", sep="") cat("rowMads():\n") r0 <- rowMads_R(x, na.rm=na.rm) r1 <- rowMads(x, na.rm=na.rm) r2 <- colMads(t(x), na.rm=na.rm) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) stopifnot(all.equal(r1, r2)) cat("colMads():\n") r0 <- colMads_R(x, na.rm=na.rm) r1 <- colMads(x, na.rm=na.rm) r2 <- rowMads(t(x), na.rm=na.rm) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) stopifnot(all.equal(r1, r2)) } } # for (addNA ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # All NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(NA_real_, nrow=20, ncol=5) for (na.rm in c(FALSE, TRUE)) { cat("na.rm=", na.rm, "\n", sep="") r0 <- rowMads_R(x, na.rm=na.rm) if (na.rm) r0[is.na(r0)] <- NaN r1 <- rowMads(x, na.rm=na.rm) r2 <- colMads(t(x), na.rm=na.rm) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) stopifnot(all.equal(r1, r2)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A 1x1 matrix # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(0, nrow=1, ncol=1) for (na.rm in c(FALSE, TRUE)) { cat("na.rm=", na.rm, "\n", sep="") r0 <- rowMads_R(x, na.rm=na.rm) r1 <- rowMads(x, na.rm=na.rm) r2 <- colMads(t(x), na.rm=na.rm) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A 0x0 matrix # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(double(0), nrow=0, ncol=0) for (na.rm in c(FALSE, TRUE)) { cat("na.rm=", na.rm, "\n", sep="") r0 <- rowMads_R(x, na.rm=na.rm) r1 <- rowMads(x, na.rm=na.rm) r2 <- colMads(t(x), na.rm=na.rm) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } matrixStats/tests/rowQuantiles_subset.R0000644000175100001440000000232212726216471020171 0ustar hornikuserslibrary("matrixStats") rowQuantiles_R <- function(x, probs, na.rm=FALSE, drop=TRUE, ...) { q <- apply(x, MARGIN=1L, FUN=function(x, probs, na.rm) { if (!na.rm && any(is.na(x))) { naValue <- NA_real_ storage.mode(naValue) <- storage.mode(x) rep(naValue, length(probs)) } else { as.vector(quantile(x, probs=probs, na.rm=na.rm, ...)) } }, probs=probs, na.rm=na.rm) if (!is.null(dim(q))) q <- t(q) else dim(q) <- c(nrow(x), length(probs)) digits <- max(2L, getOption("digits")) colnames(q) <- sprintf("%.*g%%", digits, 100*probs) if (drop) q <- drop(q) q } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) probs <- c(0, 0.25, 0.75, 1) for (rows in indexCases) { for (cols in indexCases) { for (na.rm in c(TRUE, FALSE)) { validateIndicesTestMatrix(x, rows, cols, ftest=rowQuantiles, fsure=rowQuantiles_R, probs=probs, na.rm=na.rm, drop=FALSE) validateIndicesTestMatrix(x, rows, cols, fcolTest=colQuantiles, fsure=rowQuantiles_R, probs=probs, na.rm=na.rm, drop=FALSE) } } } matrixStats/tests/rowCumprods.R0000644000175100001440000000654612726216471016447 0ustar hornikuserslibrary("matrixStats") rowCumprods_R <- function(x) { suppressWarnings({ y <- t(apply(x, MARGIN=1L, FUN=cumprod)) }) dim(y) <- dim(x) y } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # With and without some NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { for (addNA in c(FALSE, TRUE)) { cat("addNA=", addNA, "\n", sep="") x <- matrix(1:100, nrow=20, ncol=5) if (addNA) { x[13:17,c(2,4)] <- NA_real_ } cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) # Row/column ranges r0 <- rowCumprods_R(x) r1 <- rowCumprods(x) r2 <- t(colCumprods(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # for (addNA ...) } # for (mode ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # All NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { x <- matrix(NA_real_, nrow=20, ncol=5) cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) r0 <- rowCumprods_R(x) r1 <- rowCumprods(x) r2 <- t(colCumprods(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # for (mode ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A 1x1 matrix # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { x <- matrix(0, nrow=1, ncol=1) cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) r0 <- rowCumprods_R(x) r1 <- rowCumprods(x) r2 <- t(colCumprods(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # BUG FIX TEST: Assert zeros don't trump NAs in integer matrices # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { x <- matrix(NA_real_, nrow=3, ncol=2) x[1,2] <- 0 x[2,2] <- 1 x[3,1] <- 0 storage.mode(x) <- mode cat("mode: ", mode, "\n", sep="") str(x) r0 <- rowCumprods_R(x) r1 <- rowCumprods(x) r2 <- t(colCumprods(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Corner cases # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { cat("mode: ", mode, "\n", sep="") value <- 0 storage.mode(value) <- mode # A 0x0 matrix x <- matrix(value, nrow=0L, ncol=0L) str(x) r0 <- matrix(value, nrow=nrow(x), ncol=ncol(x)) r1 <- rowCumprods(x) r2 <- t(colCumprods(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) # A 0xK matrix x <- matrix(value, nrow=0L, ncol=5L) str(x) r0 <- matrix(value, nrow=nrow(x), ncol=ncol(x)) r1 <- rowCumprods(x) r2 <- t(colCumprods(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) # A Nx0 matrix x <- matrix(value, nrow=5L, ncol=0L) str(x) r0 <- matrix(value, nrow=nrow(x), ncol=ncol(x)) r1 <- rowCumprods(x) r2 <- t(colCumprods(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # for (mode ...) matrixStats/tests/rowAllAnys_subset.R0000644000175100001440000001250112726216471017567 0ustar hornikuserslibrary("matrixStats") rowAlls_R <- function(x, value=TRUE, na.rm=FALSE, ...) { if (is.na(value)) { apply(is.na(x), MARGIN=1L, FUN=all, na.rm=na.rm) } else { y <- x == value dim(y) <- dim(x) # for 0×N and M×0 cases apply(y, MARGIN=1L, FUN=all, na.rm=na.rm) } } rowAnys_R <- function(x, value=TRUE, na.rm=FALSE, ...) { if (is.na(value)) { apply(is.na(x), MARGIN=1L, FUN=any, na.rm=na.rm) } else { y <- x == value dim(y) <- dim(x) # for 0×N and M×0 cases apply(y, MARGIN=1L, FUN=any, na.rm=na.rm) } } rowAnyMissings_R <- function(x, ...) { apply(x, MARGIN=1L, FUN=anyMissing) } all_R <- function(x, value=TRUE, ...) { if (is.na(value)) { all(is.na(x), ...) } else { all(x == value, ...) } } any_R <- function(x, value=TRUE, ...) { if (is.na(value)) { any(is.na(x), ...) } else { any(x == value, ...) } } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-3, max=3), nrow=6, ncol=6) storage.mode(x) <- "integer" x[2:3,] <- NA_integer_ x[2,1] <- 0L x[4:5,] <- 0L x[4,6] <- NA_integer_ for (rows in indexCases) { for (cols in indexCases) { validateIndicesTestMatrix(x, rows, cols, ftest=rowAlls, fsure=rowAlls_R, value=0, na.rm=TRUE) validateIndicesTestMatrix(x, rows, cols, ftest=rowAlls, fsure=rowAlls_R, value=0, na.rm=FALSE) validateIndicesTestMatrix(x, rows, cols, ftest=rowAlls, fsure=rowAlls_R, value=NA_integer_) validateIndicesTestMatrix(x, rows, cols, fcolTest=colAlls, fsure=rowAlls_R, value=0, na.rm=TRUE) validateIndicesTestMatrix(x, rows, cols, fcolTest=colAlls, fsure=rowAlls_R, value=0, na.rm=FALSE) validateIndicesTestMatrix(x, rows, cols, fcolTest=colAlls, fsure=rowAlls_R, value=NA_integer_) validateIndicesTestMatrix(x, rows, cols, ftest=rowAnys, fsure=rowAnys_R, value=0, na.rm=TRUE) validateIndicesTestMatrix(x, rows, cols, ftest=rowAnys, fsure=rowAnys_R, value=0, na.rm=FALSE) validateIndicesTestMatrix(x, rows, cols, ftest=rowAnys, fsure=rowAnys_R, value=NA_integer_) validateIndicesTestMatrix(x, rows, cols, fcolTest=colAnys, fsure=rowAnys_R, value=0, na.rm=TRUE) validateIndicesTestMatrix(x, rows, cols, fcolTest=colAnys, fsure=rowAnys_R, value=0, na.rm=FALSE) validateIndicesTestMatrix(x, rows, cols, fcolTest=colAnys, fsure=rowAnys_R, value=NA_integer_) validateIndicesTestMatrix(x, rows, cols, ftest=rowAnyMissings, fsure=rowAnyMissings_R) validateIndicesTestMatrix(x, rows, cols, fcolTest=colAnyMissings, fsure=rowAnyMissings_R) } } for (rr in seq_len(nrow(x))) { for (idxs in indexCases) { validateIndicesTestVector(x[rr,], idxs, ftest=allValue, fsure=all_R, value=0, na.rm=TRUE) validateIndicesTestVector(x[rr,], idxs, ftest=allValue, fsure=all_R, value=0, na.rm=FALSE) validateIndicesTestVector(x[rr,], idxs, ftest=allValue, fsure=all_R, value=NA_integer_) validateIndicesTestVector(x[rr,], idxs, ftest=anyValue, fsure=any_R, value=0, na.rm=TRUE) validateIndicesTestVector(x[rr,], idxs, ftest=anyValue, fsure=any_R, value=0, na.rm=FALSE) validateIndicesTestVector(x[rr,], idxs, ftest=anyValue, fsure=any_R, value=NA_integer_) } } storage.mode(x) <- "character" for (rows in indexCases) { for (cols in indexCases) { validateIndicesTestMatrix(x, rows, cols, ftest=rowAlls, fsure=rowAlls_R, value="0", na.rm=TRUE) validateIndicesTestMatrix(x, rows, cols, ftest=rowAlls, fsure=rowAlls_R, value="0", na.rm=FALSE) validateIndicesTestMatrix(x, rows, cols, ftest=rowAlls, fsure=rowAlls_R, value=NA_character_) validateIndicesTestMatrix(x, rows, cols, fcolTest=colAlls, fsure=rowAlls_R, value="0", na.rm=TRUE) validateIndicesTestMatrix(x, rows, cols, fcolTest=colAlls, fsure=rowAlls_R, value="0", na.rm=FALSE) validateIndicesTestMatrix(x, rows, cols, fcolTest=colAlls, fsure=rowAlls_R, value=NA_character_) validateIndicesTestMatrix(x, rows, cols, ftest=rowAnys, fsure=rowAnys_R, value="0", na.rm=TRUE) validateIndicesTestMatrix(x, rows, cols, ftest=rowAnys, fsure=rowAnys_R, value="0", na.rm=FALSE) validateIndicesTestMatrix(x, rows, cols, ftest=rowAnys, fsure=rowAnys_R, value=NA_character_) validateIndicesTestMatrix(x, rows, cols, fcolTest=colAnys, fsure=rowAnys_R, value="0", na.rm=TRUE) validateIndicesTestMatrix(x, rows, cols, fcolTest=colAnys, fsure=rowAnys_R, value="0", na.rm=FALSE) validateIndicesTestMatrix(x, rows, cols, fcolTest=colAnys, fsure=rowAnys_R, value=NA_character_) validateIndicesTestMatrix(x, rows, cols, ftest=rowAnyMissings, fsure=rowAnyMissings_R) validateIndicesTestMatrix(x, rows, cols, fcolTest=colAnyMissings, fsure=rowAnyMissings_R) } } for (rr in seq_len(nrow(x))) { for (idxs in indexCases) { validateIndicesTestVector(x[rr,], idxs, ftest=allValue, fsure=all_R, value="0", na.rm=TRUE) validateIndicesTestVector(x[rr,], idxs, ftest=allValue, fsure=all_R, value="0", na.rm=FALSE) validateIndicesTestVector(x[rr,], idxs, ftest=allValue, fsure=all_R, value=NA_integer_) validateIndicesTestVector(x[rr,], idxs, ftest=anyValue, fsure=any_R, value="0", na.rm=TRUE) validateIndicesTestVector(x[rr,], idxs, ftest=anyValue, fsure=any_R, value="0", na.rm=FALSE) validateIndicesTestVector(x[rr,], idxs, ftest=anyValue, fsure=any_R, value=NA_integer_) } } matrixStats/tests/rowTabulates.R0000644000175100001440000000155112726216471016566 0ustar hornikuserslibrary("matrixStats") N <- 6L K <- 5L J <- 5L data <- matrix(1:J, nrow=N, ncol=K) modes <- c("integer", "raw") for (mode in modes) { cat(sprintf("Mode: %s...\n", mode)) x <- data storage.mode(x) <- mode print(x) y <- rowTabulates(x) print(y) stopifnot(identical(dim(y), c(N,J))) y <- colTabulates(x) print(y) stopifnot(identical(dim(y), c(K,J))) # Count only certain values y <- rowTabulates(x, values=1:3) print(y) stopifnot(identical(dim(y), c(N,3L))) y <- colTabulates(x, values=1:3) print(y) stopifnot(identical(dim(y), c(K,3L))) # Raw y <- rowTabulates(x, values=as.raw(1:3)) print(y) stopifnot(identical(dim(y), c(N,3L))) y2 <- colTabulates(t(x), values=as.raw(1:3)) print(y2) stopifnot(identical(dim(y2), c(N,3L))) stopifnot(identical(y2, y)) cat(sprintf("Mode: %s...done\n", mode)) } # for (mode ...) matrixStats/tests/diff2_subset.R0000644000175100001440000000063512726216471016473 0ustar hornikuserslibrary("matrixStats") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- runif(6, min=-6, max=6) for (l in 1:2) { for (d in 1:2) { for (idxs in indexCases) { validateIndicesTestVector(x, idxs, ftest=diff2, fsure=diff, lag=l, differences=d) } } } matrixStats/tests/weightedMean_subset.R0000644000175100001440000000123012726216471020072 0ustar hornikuserslibrary("matrixStats") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") for (mode in c("numeric", "integer")) { x <- runif(6, min=-6, max=6) w <- runif(6, min=0, max=6) storage.mode(x) <- mode storage.mode(w) <- mode if (mode == "numeric") w[1] <- Inf for (idxs in indexCases) { validateIndicesTestVector_w(x, w, idxs, ftest=weightedMean, fsure=weighted.mean, na.rm=TRUE, refine=TRUE) validateIndicesTestVector_w(x, w, idxs, ftest=weightedMean, fsure=weighted.mean, na.rm=FALSE, refine=TRUE) } } matrixStats/tests/rowCounts.R0000644000175100001440000001002712726216471016113 0ustar hornikuserslibrary("matrixStats") rowCounts_R <- function(x, value=TRUE, na.rm=FALSE, ...) { if (is.na(value)) { counts <- apply(x, MARGIN=1L, FUN=function(x) sum(is.na(x))) } else { counts <- apply(x, MARGIN=1L, FUN=function(x) sum(x == value, na.rm=na.rm)) } as.integer(counts) } # rowCounts_R() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Data type: integer and numeric # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { x <- matrix(runif(20*5, min=-3, max=3), nrow=20, ncol=5) x[sample.int(length(x), size=7)] <- 0 storage.mode(x) <- mode for (na.rm in c(FALSE, TRUE)) { # Count zeros r0 <- rowCounts_R(x, value=0, na.rm=na.rm) r1 <- rowCounts(x, value=0, na.rm=na.rm) r2 <- colCounts(t(x), value=0, na.rm=na.rm) stopifnot(identical(r1, r0)) stopifnot(identical(r2, r0)) # Count NAs r0 <- rowCounts_R(x, value=NA, na.rm=na.rm) r1 <- rowCounts(x, value=NA, na.rm=na.rm) r2 <- colCounts(t(x), value=NA, na.rm=na.rm) stopifnot(identical(r1, r0)) stopifnot(identical(r2, r0)) if (mode == "integer") { ux <- unique(as.vector(x)) r0 <- r1 <- r2 <- integer(nrow(x)) for (value in ux) { r0 <- r0 + rowCounts_R(x, value=value, na.rm=na.rm) r1 <- r1 + rowCounts(x, value=value, na.rm=na.rm) r2 <- r2 + colCounts(t(x), value=value, na.rm=na.rm) stopifnot(identical(r1, r0)) stopifnot(identical(r2, r0)) } stopifnot(all(r0 == ncol(x))) } # if (mode == "integer") } # for (na.rm ...) } # for (mode ...) # All NAs naList <- list(NA_integer_, NA_real_, NaN) for (naValue in naList) { x <- matrix(naValue, nrow=20, ncol=5) for (na.rm in c(FALSE, TRUE)) { r0 <- rowCounts_R(x, na.rm=na.rm) r1 <- rowCounts(x, na.rm=na.rm) r2 <- colCounts(t(x), na.rm=na.rm) stopifnot(identical(r1, r0)) stopifnot(identical(r2, r0)) # Count NAs r0 <- rowCounts_R(x, value=NA, na.rm=na.rm) r1 <- rowCounts(x, value=NA, na.rm=na.rm) r2 <- colCounts(t(x), value=NA, na.rm=na.rm) stopifnot(all(r0 == ncol(x))) stopifnot(identical(r1, r0)) stopifnot(identical(r2, r0)) } } # for (naValue ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Data type: logical # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(FALSE, nrow=20, ncol=5) x[13:17,c(2,4)] <- TRUE x[2:4,] <- TRUE x[,1] <- TRUE x[5,] <- FALSE x[,5] <- FALSE # Row/column counts for (na.rm in c(FALSE, TRUE)) { r0 <- rowCounts_R(x, na.rm=na.rm) r1 <- rowCounts(x, na.rm=na.rm) r2 <- colCounts(t(x), na.rm=na.rm) stopifnot(identical(r1, r0)) stopifnot(identical(r2, r0)) rT <- rowCounts(x, value=TRUE, na.rm=na.rm) rF <- rowCounts(x, value=FALSE, na.rm=na.rm) stopifnot(rT + rF == ncol(x)) cT <- colCounts(x, value=TRUE, na.rm=na.rm) cF <- colCounts(x, value=FALSE, na.rm=na.rm) stopifnot(cT + cF == nrow(x)) # Count NAs r0 <- rowCounts_R(x, value=NA, na.rm=na.rm) r1 <- rowCounts(x, value=NA, na.rm=na.rm) r2 <- colCounts(t(x), value=NA, na.rm=na.rm) stopifnot(identical(r1, r0)) stopifnot(identical(r2, r0)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Data type: character (not sure if this should be supported) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(rep(letters, length.out=20*5), nrow=20, ncol=5) x[2:3,3:4] <- NA_character_ # Row/column counts for (na.rm in c(FALSE, TRUE)) { for (value in c("g", NA_character_)) { r0 <- rowCounts_R(x, value=value, na.rm=na.rm) r1 <- rowCounts(x, value=value, na.rm=na.rm) r2 <- colCounts(t(x), value=value, na.rm=na.rm) stopifnot(identical(r1, r0)) stopifnot(identical(r2, r0)) c <- count(x[1,], value=value, na.rm=na.rm) stopifnot(identical(c,r1[1])) c <- count(x[2,], value=value, na.rm=na.rm) stopifnot(identical(c,r1[2])) } } # NA row x <- matrix(0, nrow=2, ncol=2) x[1,] <- NA_integer_ r0 <- rowCounts(x, value=0) r1 <- rowCounts_R(x, value=0) stopifnot(identical(r0,r1)) matrixStats/tests/psortKM.R0000644000175100001440000000242412726216471015511 0ustar hornikuserslibrary("matrixStats") library("utils") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Local functions # - - - - - - - - - - - - - - - - - - - - - - - - - - - - psortKM_R <- function(x, k, m) { x <- sort(x) x[(k-m+1):k] } # psortKM_R() psortKM_R2 <- function(x, k, m) { partial <- (k-m+1):k x <- sort.int(x, partial=partial) x[partial] } # psortKM_R2() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Consistency checks # - - - - - - - - - - - - - - - - - - - - - - - - - - - - set.seed(1) cat("Consistency checks:\n") x <- 1:500 x[298:300] <- 300 y <- sample(x) cat("x:\n") str(x) cat("sample(x):\n") str(y) for (k in c(1, 2, 300, 301, length(x))) { for (m in 1:min(5,k)) { px0 <- psortKM_R(x, k=k, m=m) px0b <- psortKM_R2(x, k=k, m=m) stopifnot(identical(px0b, px0)) px1 <- matrixStats:::.psortKM(x, k=k, m=m) cat(sprintf(".psortKM(x, k=%d, m=%d):\n", k, m)) print(px1) stopifnot(identical(px1, px0)) py0 <- psortKM_R(y, k=k, m=m) py0b <- psortKM_R2(y, k=k, m=m) stopifnot(identical(py0b, py0)) py1 <- matrixStats:::.psortKM(y, k=k, m=m) cat(sprintf(".psortKM(y, k=%d, m=%d):\n", k, m)) print(py1) stopifnot(identical(py1, py0)) stopifnot(identical(py1, px1)) } # for (m ...) } # for (k ...) matrixStats/tests/binCounts_subset.R0000644000175100001440000000147212726216471017445 0ustar hornikuserslibrary("matrixStats") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Local functions # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - binCounts_hist <- function(x, bx, right=FALSE, ...) { n0 <- graphics::hist(x, breaks=bx, right=right, include.lowest=TRUE, plot=FALSE)$counts } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- runif(6, min=-6, max=6) storage.mode(x) <- "integer" bx <- c(-6, 0, 3, 4, 10) for (idxs in indexCases) { validateIndicesTestVector(x, idxs, ftest=binCounts, fsure=binCounts_hist, bx=bx, right=FALSE) validateIndicesTestVector(x, idxs, ftest=binCounts, fsure=binCounts_hist, bx=bx, right=TRUE) } matrixStats/tests/rowProds_subset.R0000644000175100001440000000143212726216471017314 0ustar hornikuserslibrary("matrixStats") rowProds_R <- function(x, FUN=prod, na.rm=FALSE, ...) { y <- apply(x, MARGIN=1L, FUN=FUN, na.rm=na.rm) y } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) storage.mode(x) <- "integer" method <- "expSumLog" FUN <- product for (rows in indexCases) { for (cols in indexCases) { for (na.rm in c(TRUE, FALSE)) { validateIndicesTestMatrix(x, rows, cols, ftest=rowProds, fsure=rowProds_R, method=method, FUN=FUN, na.rm=na.rm) validateIndicesTestMatrix(x, rows, cols, fcolTest=colProds, fsure=rowProds_R, method=method, FUN=FUN, na.rm=na.rm) } } } matrixStats/tests/rowDiffs.R0000644000175100001440000000414412726216471015676 0ustar hornikuserslibrary("matrixStats") rowDiffs_R <- function(x, lag=1L, differences=1L, ...) { ncol2 <- ncol(x) - lag*differences if (ncol2 <= 0) { return(matrix(x[integer(0L)], nrow=nrow(x), ncol=0L)) } suppressWarnings({ y <- apply(x, MARGIN=1L, FUN=diff, lag=lag, differences=differences) }) y <- t(y) dim(y) <- c(nrow(x), ncol2) y } set.seed(0x42) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # With and without some NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { cat("mode: ", mode, "\n", sep="") for (addNA in c(FALSE, TRUE)) { cat("addNA=", addNA, "\n", sep="") x <- matrix(sample(20*8)+0.1, nrow=20, ncol=8) if (addNA) { x[13:17,c(2,4)] <- NA_real_ } storage.mode(x) <- mode str(x) for (lag in 1:4) { for (differences in 1:3) { cat(sprintf("mode: %s, lag=%d, differences=%d\n", mode, lag, differences)) # Row/column ranges r0 <- rowDiffs_R(x, lag=lag, differences=differences) r1 <- rowDiffs(x, lag=lag, differences=differences) r2 <- t(colDiffs(t(x), lag=lag, differences=differences)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) stopifnot(all.equal(r1, r2)) } } } # for (addNA ...) } # for (mode ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # All NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { cat("mode: ", mode, "\n", sep="") x <- matrix(NA_real_, nrow=20, ncol=5) storage.mode(x) <- mode str(x) r0 <- rowDiffs_R(x) r1 <- rowDiffs(x) r2 <- t(colDiffs(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) } # for (mode ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A 1x1 matrix # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(0, nrow=1, ncol=1) r0 <- rowDiffs_R(x) r1 <- rowDiffs(x) r2 <- t(colDiffs(t(x))) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) matrixStats/tests/rowVarDiffs_mad,iqr_subset.R0000644000175100001440000000213212726216471021340 0ustar hornikuserslibrary("matrixStats") FUNs <- list( madDiff=c(rowMadDiffs, colMadDiffs), iqrDiff=c(rowIQRDiffs, colIQRDiffs) ) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") for (fcn in names(FUNs)) { cat(sprintf("subsetted tests on %s()...\n", fcn)) rowFUN <- FUNs[[fcn]][[1L]] colFUN <- FUNs[[fcn]][[2L]] for (mode in c("numeric", "integer")) { x <- matrix(runif(6*6, min=-6, max=6), nrow=6L, ncol=6L) storage.mode(x) <- mode trim <- runif(1, min=0, max=0.5) if (mode == "numeric") x[1:2,3:4] <- Inf for (diff in 1:2) { for (rows in indexCases) { for (cols in indexCases) { for (na.rm in c(TRUE, FALSE)) { validateIndicesTestMatrix(x, rows, cols, ftest=rowFUN, fsure=rowFUN, na.rm=na.rm, diff=diff, trim=trim) validateIndicesTestMatrix(x, rows, cols, fcolTest=colFUN, fsure=rowFUN, na.rm=na.rm, diff=diff, trim=trim) } } } } } cat(sprintf("%s()...DONE\n", fcn)) } matrixStats/tests/logSumExp.R0000644000175100001440000000612112726216471016033 0ustar hornikuserslibrary("matrixStats") library("stats") logSumExp_R <- function(lx, na.rm=FALSE) { log(sum(exp(lx), na.rm=na.rm)) } ## R-help thread \emph{'[R] Beyond double-precision?'} on May 9, 2009. for (mode in c("integer", "double")) { cat("mode: ", mode, "\n", sep="") set.seed(1) x <- runif(20, min=1.0, max=3.0) storage.mode(x) <- mode str(x) ## The logarithm of the harmonic mean y0 <- log(1/mean(1/x)) print(y0) ## -1.600885 lx <- log(x) y1 <- log(length(x)) - logSumExp(-lx) print(y1) ## [1] -1.600885 # Sanity check stopifnot(all.equal(y1, y0)) y2 <- log(length(x)) - logSumExp_R(-lx) # Sanity check stopifnot(all.equal(y2, y0)) } # for (mode ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Missing values # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ## NA values lx <- c(1,2,3) lx[2] <- NA_real_ y0 <- logSumExp_R(lx, na.rm=FALSE) y <- logSumExp(lx, na.rm=FALSE) print(y) stopifnot(identical(y, NA_real_)) stopifnot(all.equal(y, y0)) y0 <- logSumExp_R(lx, na.rm=TRUE) y <- logSumExp(lx, na.rm=TRUE) print(y) stopifnot(all.equal(y, y0)) ## NaN values lx <- c(1,2,3) lx[2] <- NaN y0 <- logSumExp_R(lx, na.rm=FALSE) y <- logSumExp(lx, na.rm=FALSE) print(y) stopifnot(identical(y, NA_real_)) stopifnot(all.equal(y, y0)) y0 <- logSumExp_R(lx, na.rm=TRUE) y <- logSumExp(lx, na.rm=TRUE) print(y) stopifnot(all.equal(y, y0)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Corner cases # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ## Zero-length vectors lx <- numeric(0L) y0 <- logSumExp_R(lx) y <- logSumExp(lx) print(y) stopifnot(identical(y, -Inf)) stopifnot(all.equal(y, y0)) ## Vectors of length one lx <- 1.0 y0 <- logSumExp_R(lx) y <- logSumExp(lx) print(y) stopifnot(identical(y, lx)) stopifnot(all.equal(y, y0)) lx <- NA_real_ y0 <- logSumExp_R(lx, na.rm=TRUE) y <- logSumExp(lx, na.rm=TRUE) print(y) stopifnot(identical(y, -Inf)) stopifnot(all.equal(y, y0)) ## All missing values lx <- c(NA_real_, NA_real_) y0 <- logSumExp_R(lx, na.rm=TRUE) y <- logSumExp(lx, na.rm=TRUE) print(y) stopifnot(identical(y, -Inf)) stopifnot(all.equal(y, y0)) lx <- c(NA_real_, NA_real_) y0 <- logSumExp_R(lx, na.rm=FALSE) y <- logSumExp(lx, na.rm=FALSE) print(y) stopifnot(identical(y, NA_real_)) stopifnot(all.equal(y, y0)) ## +Inf values lx <- c(1, 2, +Inf) y0 <- logSumExp_R(lx) y <- logSumExp(lx) print(y) stopifnot(identical(y, +Inf)) stopifnot(all.equal(y, y0)) ## First element is a missing value, cf. PR #33 lx <- c(NA_real_, 1) y0 <- logSumExp_R(lx) print(y0) y <- logSumExp(lx, na.rm=FALSE) print(y) stopifnot(identical(y, NA_real_)) stopifnot(all.equal(y, y0)) y0 <- logSumExp_R(lx, na.rm=TRUE) print(y0) y <- logSumExp(lx, na.rm=TRUE) print(y) stopifnot(identical(y, 1)) stopifnot(all.equal(y, y0)) ## Multiple -Inf values, cf. issue #84 lx <- c(-Inf, -Inf) y0 <- logSumExp_R(lx) y <- logSumExp(lx) print(y) stopifnot(identical(y, -Inf)) stopifnot(all.equal(y, y0)) lx <- c(-Inf, 5, -Inf) y0 <- logSumExp_R(lx) y <- logSumExp(lx) print(y) stopifnot(identical(y, 5)) stopifnot(all.equal(y, y0)) matrixStats/tests/logSumExp_subset.R0000644000175100001440000000102612726216471017417 0ustar hornikuserslibrary("matrixStats") logSumExp_R <- function(lx, na.rm=FALSE) { log(sum(exp(lx), na.rm=na.rm)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- runif(6, min=-6, max=6) for (idxs in indexCases) { validateIndicesTestVector(x, idxs, ftest=logSumExp, fsure=logSumExp_R, na.rm=FALSE) validateIndicesTestVector(x, idxs, ftest=logSumExp, fsure=logSumExp_R, na.rm=TRUE) } matrixStats/tests/rowWeightedMedians_subset.R0000644000175100001440000000163512726216471021273 0ustar hornikuserslibrary("matrixStats") rowWeightedMedians_R <- function(x, w, na.rm=FALSE, ...) { apply(x, MARGIN=1L, FUN=weightedMedian, w=w, na.rm=na.rm, ...) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") for (mode in c("numeric", "integer")) { x <- matrix(runif(6*6, min=-6, max=6), nrow=6, ncol=6) w <- runif(6, min=0, max=6) storage.mode(x) <- mode storage.mode(w) <- mode if (mode == "numeric") w[1] <- Inf for (rows in indexCases) { for (cols in indexCases) { for (na.rm in c(TRUE, FALSE)) { validateIndicesTestMatrix_w(x, w, rows, cols, ftest=rowWeightedMedians, fsure=rowWeightedMedians_R, na.rm=na.rm) validateIndicesTestMatrix_w(x, w, rows, cols, fcolTest=colWeightedMedians, fsure=rowWeightedMedians_R, na.rm=na.rm) } } } } matrixStats/tests/weightedMedian_subset.R0000644000175100001440000000164612726216471020422 0ustar hornikuserslibrary("matrixStats") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") for (mode in c("numeric", "integer")) { x <- runif(6, min=-6, max=6) w <- runif(6, min=0, max=6) storage.mode(x) <- mode storage.mode(w) <- mode if (mode == "numeric") w[1] <- Inf for (idxs in indexCases) { validateIndicesTestVector_w(x, w, idxs, ftest=weightedMedian, fsure=weightedMedian, na.rm=TRUE) validateIndicesTestVector_w(x, w, idxs, ftest=weightedMedian, fsure=weightedMedian, na.rm=FALSE) for (ties in c("weighted", "mean", "min", "max")) { validateIndicesTestVector_w(x, w, idxs, ftest=weightedMedian, fsure=weightedMedian, na.rm=TRUE, ties=ties) validateIndicesTestVector_w(x, w, idxs, ftest=weightedMedian, fsure=weightedMedian, na.rm=FALSE, ties=ties) } } } matrixStats/tests/weightedVar.R0000644000175100001440000000427412726216471016370 0ustar hornikuserslibrary("matrixStats") weightedVar_R <- function(x, w) { sum(w * (x - mu)^2) / (sum(w) - 1) } n <- 10 x <- as.double(1:n) message("*** weightedVar() ...") message("- Zero elements") m0 <- var(integer(0)) m1 <- weightedVar(integer(0), w=integer(0)) str(list(m0=m0, m1=m1)) stopifnot(all.equal(m1, m0)) message("- One elements") m0 <- var(1) m1 <- weightedVar(1) str(list(m0=m0, m1=m1)) stopifnot(all.equal(m1, m0)) message("- Uniform weights (all w=1)") m0 <- var(x) w <- rep(1, times=n) m1 <- weightedVar(x, w=w) str(list(m0=m0, m1=m1)) stopifnot(all.equal(m1, m0)) message("- Uniform weights (all w=3)") m0 <- var(rep(x, each=3)) w <- rep(3, times=n) m1 <- weightedVar(x, w=w) str(list(m0=m0, m1=m1)) stopifnot(all.equal(m1, m0)) message("- Uniform weights on the first five elements") idxs <- 1:5 m0 <- var(x[1:5]) w <- rep(0, times=n); w[idxs] <- 1 m1 <- weightedVar(x, w=w) str(list(m0=m0, m1=m1)) stopifnot(all.equal(m1, m0)) message("- Uniform weights on every second elements") idxs <- seq(from=1, to=n, by=2) m0 <- var(x[idxs]) w <- rep(0, times=n); w[idxs] <- 1 m1 <- weightedVar(x, w=w) str(list(m0=m0, m1=m1)) stopifnot(all.equal(m1, m0)) message("- All weights are zero") idxs <- integer(0L) m0 <- var(x[idxs]) w <- rep(0, times=n); w[idxs] <- 1 m1 <- weightedVar(x, w=w) str(list(m0=m0, m1=m1)) stopifnot(all.equal(m1, m0)) message("- Infinite weights on first element") idxs <- 1L m0 <- var(x[idxs]) w <- rep(0, times=n); w[idxs] <- Inf m1 <- weightedVar(x, w=w) str(list(m0=m0, m1=m1)) stopifnot(all.equal(m1, m0)) message("- Frequency weights") ## From https://en.wikipedia.org/wiki/Weighted_arithmetic_mean y <- c(2, 2, 4, 5, 5, 5) x <- unique(y) w <- table(y) stopifnot(names(w) == x) m0 <- weightedVar(x, w=w) m1 <- var(y) stopifnot(all.equal(m1, m0)) m2 <- weightedVar(x, w=w) str(list(m0=m0, m1=m1, m2=m2)) stopifnot(all.equal(m2, m0)) ## From https://github.com/HenrikBengtsson/matrixStats/issues/72 large <- c(21, 8, 26, 1, 15, 33, 12, 25, 0, 84) years <- c(41706, 9301, 33678, 3082, 27040, 44188, 10049, 30591, 2275, 109831) m0 <- weightedVar(large, w=years) m1 <- weightedVar(large, w=years) str(list(m0=m0, m1=m1)) stopifnot(all.equal(m1, m0)) message("*** weightedVar() ... DONE") matrixStats/tests/varDiff_etal_subset.R0000644000175100001440000000160612726216471020066 0ustar hornikuserslibrary("matrixStats") FUNs <- list( varDiff=varDiff, sdDiff=sdDiff, madDiff=madDiff, iqrDiff=iqrDiff ) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") for (fcn in names(FUNs)) { cat(sprintf("subsetted tests on %s()...\n", fcn)) FUN <- FUNs[[fcn]] for (mode in c("numeric", "integer")) { x <- runif(6, min=-6, max=6) storage.mode(x) <- mode trim <- runif(1, min=0, max=0.5) if (mode == "numeric") x[1] <- Inf for (diff in 1:2) { for (idxs in indexCases) { validateIndicesTestVector(x, idxs, ftest=FUN, fsure=FUN, na.rm=TRUE, diff=diff, trim=trim) validateIndicesTestVector(x, idxs, ftest=FUN, fsure=FUN, na.rm=FALSE, diff=diff, trim=trim) } } } cat(sprintf("%s()...DONE\n", fcn)) } matrixStats/tests/product_subset.R0000644000175100001440000000067112726216471017161 0ustar hornikuserslibrary("matrixStats") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") x <- runif(6, min=-6, max=6) storage.mode(x) <- "integer" for (idxs in indexCases) { validateIndicesTestVector(x, idxs, ftest=product, fsure=prod, na.rm=TRUE) validateIndicesTestVector(x, idxs, ftest=product, fsure=prod, na.rm=FALSE) } matrixStats/tests/weightedVar_etal_subset.R0000644000175100001440000000153212726216471020754 0ustar hornikuserslibrary("matrixStats") FUNs <- list( weightedVar=weightedVar, weightedSd=weightedSd, weightedMad=weightedMad ) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetted tests # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source("utils/validateIndicesFramework.R") for (fcn in names(FUNs)) { cat(sprintf("subsetted tests on %s()...\n", fcn)) FUN <- FUNs[[fcn]] for (mode in c("numeric", "integer")) { x <- runif(6, min=-6, max=6) w <- runif(6, min=0, max=6) storage.mode(x) <- mode storage.mode(w) <- mode if (mode == "numeric") w[1] <- Inf for (idxs in indexCases) { validateIndicesTestVector_w(x, w, idxs, ftest=FUN, fsure=FUN, na.rm=TRUE) validateIndicesTestVector_w(x, w, idxs, ftest=FUN, fsure=FUN, na.rm=FALSE) } } cat(sprintf("%s()...DONE\n", fcn)) } matrixStats/tests/rowSds.R0000644000175100001440000000552712726216471015402 0ustar hornikuserslibrary("matrixStats") rowSds_R <- function(x, na.rm=FALSE) { suppressWarnings({ apply(x, MARGIN=1L, FUN=sd, na.rm=na.rm) }) } colSds_R <- function(x, na.rm=FALSE) { suppressWarnings({ apply(x, MARGIN=2L, FUN=sd, na.rm=na.rm) }) } rowSds_center <- function(x, rows=NULL, cols=NULL, na.rm=FALSE) { center <- rowWeightedMeans(x, cols=cols, na.rm=na.rm) rowSds(x, rows=rows, cols=cols, center=center, na.rm=na.rm) } colSds_center <- function(x, rows=NULL, cols=NULL, na.rm=FALSE) { center <- colWeightedMeans(x, rows=rows, na.rm=na.rm) colSds(x, rows=rows, cols=cols, center=center, na.rm=na.rm) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # With and without some NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { for (addNA in c(FALSE, TRUE)) { cat("addNA=", addNA, "\n", sep="") x <- matrix(1:100+0.1, nrow=20, ncol=5) if (addNA) { x[13:17,c(2,4)] <- NA_real_ } cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) # Row/column ranges for (na.rm in c(FALSE, TRUE)) { cat("na.rm=", na.rm, "\n", sep="") r0 <- rowSds_R(x, na.rm=na.rm) r1 <- rowSds(x, na.rm=na.rm) r1b <- rowSds_center(x, na.rm=na.rm) r2 <- colSds(t(x), na.rm=na.rm) r2b <- colSds_center(t(x), na.rm=na.rm) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) stopifnot(all.equal(r1b, r1)) stopifnot(all.equal(r2b, r2)) } } # for (addNA ...) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # All NAs # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { x <- matrix(NA_real_, nrow=20, ncol=5) cat("mode: ", mode, "\n", sep="") storage.mode(x) <- mode str(x) for (na.rm in c(FALSE, TRUE)) { cat("na.rm=", na.rm, "\n", sep="") r0 <- rowSds_R(x, na.rm=na.rm) r1 <- rowSds(x, na.rm=na.rm) r1b <- rowSds_center(x, na.rm=na.rm) r2 <- colSds(t(x), na.rm=na.rm) r2b <- colSds_center(t(x), na.rm=na.rm) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) stopifnot(all.equal(r1b, r1)) stopifnot(all.equal(r2b, r2)) } } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A 1x1 matrix # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(0, nrow=1, ncol=1) for (na.rm in c(FALSE, TRUE)) { cat("na.rm=", na.rm, "\n", sep="") r0 <- rowSds_R(x, na.rm=na.rm) r1 <- rowSds(x, na.rm=na.rm) r1b <- rowSds_center(x, na.rm=na.rm) r2 <- colSds(t(x), na.rm=na.rm) r2b <- colSds_center(t(x), na.rm=na.rm) stopifnot(all.equal(r1, r2)) stopifnot(all.equal(r1, r0)) stopifnot(all.equal(r2, r0)) stopifnot(all.equal(r1b, r1)) stopifnot(all.equal(r2b, r2)) } matrixStats/tests/anyMissing.R0000644000175100001440000000505112726216471016232 0ustar hornikuserslibrary("matrixStats") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Setup # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - data <- data.frame( logical=c(TRUE, FALSE, TRUE, FALSE), integer=1:4, double=seq(from=1.0, to=4.0, by=1.0), complex=seq(from=1.0, to=4.0, by=1.0) + 1.0i, character=I(letters[1:4]) ) modes <- names(data) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Special cases # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - cat("NULL...\n") stopifnot(identical(anyMissing(NULL), FALSE)) cat("NULL...done\n") cat("raw...\n") stopifnot(identical(anyMissing(as.raw(0:3)), FALSE)) cat("raw...done\n") cat("list(NULL)...\n") stopifnot(identical(anyMissing(list(NULL)), FALSE)) cat("list(NULL)...done\n") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Scalars, vectors, and matrices of various modes # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in modes) { cat(sprintf("Mode: %s...\n", mode)) values <- data[[mode]] # Scalars cat(" scalar\n") x <- values[1L] print(x) stopifnot(identical(anyMissing(x), FALSE)) is.na(x) <- TRUE print(x) stopifnot(identical(anyMissing(x), TRUE)) # Vectors cat(" vector\n") x <- values print(x) stopifnot(identical(anyMissing(x), FALSE)) is.na(x)[2L] <- TRUE print(x) stopifnot(identical(anyMissing(x), TRUE)) # Matrices cat(" matrix\n") x <- matrix(c(values, values), ncol=2L) print(x) stopifnot(identical(anyMissing(x), FALSE)) is.na(x)[2L] <- TRUE print(x) stopifnot(identical(anyMissing(x), TRUE)) cat(sprintf("Mode: %s...done\n", mode)) } # for (mode ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Data frames # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - cat("data.frame...\n") x <- data stopifnot(identical(anyMissing(x), FALSE)) for (mode in modes) { x <- data is.na(x[[mode]])[2L] <- TRUE print(x) stopifnot(identical(anyMissing(x), TRUE)) } # for (mode ...) cat("data.frame...done\n") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Lists # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - cat("list...\n") x <- as.list(data) stopifnot(identical(anyMissing(x), FALSE)) for (mode in modes) { x <- as.list(data) is.na(x[[mode]])[2L] <- TRUE print(x) stopifnot(identical(anyMissing(x), TRUE)) } # for (mode ...) cat("list...done\n") matrixStats/tests/weightedVar_etal.R0000644000175100001440000000337412726216471017375 0ustar hornikuserslibrary("matrixStats") FUNs <- list( weightedVar=weightedVar, weightedSd=weightedSd, weightedMad=weightedMad ) for (fcn in names(FUNs)) { cat(sprintf("%s()...\n", fcn)) FUN <- FUNs[[fcn]] for (mode in c("integer", "double")) { cat("mode: ", mode, "\n", sep="") n <- 15L x <- runif(n, min=-5, max=5) storage.mode(x) <- mode str(x) for (addNA in c(FALSE, TRUE)) { cat("addNA: ", addNA, "\n", sep="") if (addNA) { x[c(5,7)] <- NA } str(x) for (na.rm in c(FALSE, TRUE)) { cat("na.rm: ", na.rm, "\n", sep="") cat("Weights are not specified (all are 1)\n") m1 <- FUN(x, na.rm=na.rm) str(list(m1=m1)) cat("All weights are 1\n") w <- rep(1, times=n) m1 <- FUN(x, w, na.rm=na.rm) str(list(m1=m1)) cat("First weight is 5\n") # Pull the mean towards zero w[1] <- 5 str(w) m1 <- FUN(x, w, na.rm=na.rm) str(list(m1=m1)) cat("All weights are 0\n") # All weights set to zero w <- rep(0, times=n) m1 <- FUN(x, w, na.rm=na.rm) str(list(m1=m1)) cat("First weight is 8.5\n") # Put even more weight on the zero w[1] <- 8.5 m1 <- FUN(x, w, na.rm=na.rm) str(list(m1=m1)) cat("First weight is Inf\n") # All weight on the first value w[1] <- Inf m1 <- FUN(x, w, na.rm=na.rm) str(list(m1=m1)) cat("Last weight is Inf\n") # All weight on the last value w[1] <- 1 w[n] <- Inf m1 <- FUN(x, w, na.rm=na.rm) str(list(m1=m1)) } # for (na.rm ...) } # for (addNA ...) } # for (mode ...) cat(sprintf("%s()...DONE\n", fcn)) } # for (fcn ...) matrixStats/tests/utils/0000755000175100001440000000000012726216471015125 5ustar hornikusersmatrixStats/tests/utils/validateIndicesFramework.R0000644000175100001440000001151212726216471022216 0ustar hornikuserslibrary("matrixStats") validateIndicesTestVector <- function(x, idxs, ftest, fsure, debug=FALSE, ...) { if (debug) cat(sprintf("idxs=%s, type=%s\n", toString(idxs), toString(typeof(idxs)))) suppressWarnings({ actual <- tryCatch(ftest(x,idxs=idxs,...), error=function(c) "error") expect <- tryCatch({ if (!is.null(idxs)) x <- x[idxs] fsure(x,...) }, error=function(c) "error") }) if (debug) cat(sprintf("actual=%s\nexpect=%s\n", toString(actual), toString(expect))) stopifnot(all.equal(actual, expect)) } validateIndicesTestVector_w <- function(x, w, idxs, ftest, fsure, debug=FALSE, ...) { if (debug) cat(sprintf("idxs=%s, type=%s\n", toString(idxs), toString(typeof(idxs)))) suppressWarnings({ actual <- tryCatch(ftest(x,w,idxs=idxs,...), error=function(c) "error") expect <- tryCatch({ if (!is.null(idxs)) { x <- x[idxs] w <- w[idxs] } fsure(x,w,...) }, error=function(c) "error") }) if (debug) cat(sprintf("actual=%s\nexpect=%s\n", toString(actual), toString(expect))) stopifnot(all.equal(actual, expect)) } validateIndicesTestMatrix <- function(x, rows, cols, ftest, fcolTest, fsure, debug=FALSE, ...) { if (debug) { cat(sprintf("rows=%s; type=%s\n", toString(rows), toString(typeof(rows)))) cat(sprintf("cols=%s; type=%s\n", toString(cols), toString(typeof(cols)))) } suppressWarnings({ if (missing(fcolTest)) { actual <- tryCatch(ftest(x,rows=rows,cols=cols,...), error=function(c) "error") } else { actual <- tryCatch(fcolTest(t(x),rows=cols,cols=rows,...), error=function(c) "error") } expect <- tryCatch({ if (!is.null(rows) && !is.null(cols)) { x <- x[rows,cols,drop=FALSE] } else if (!is.null(rows)) { x <- x[rows,,drop=FALSE] } else if (!is.null(cols)) { x <- x[,cols,drop=FALSE] } fsure(x,...) }, error=function(c) "error") }) if (debug) cat(sprintf("actual=%s\nexpect=%s\n", toString(actual), toString(expect))) stopifnot(all.equal(actual, expect)) } validateIndicesTestMatrix_w <- function(x, w, rows, cols, ftest, fcolTest, fsure, debug=FALSE, ...) { if (debug) { cat(sprintf("rows=%s; type=%s\n", toString(rows), toString(typeof(rows)))) cat(sprintf("cols=%s; type=%s\n", toString(cols), toString(typeof(cols)))) } suppressWarnings({ if (missing(fcolTest)) { actual <- tryCatch(ftest(x,w,rows=rows,cols=cols,...), error=function(c) "error") } else { actual <- tryCatch(fcolTest(t(x),w,rows=cols,cols=rows,...), error=function(c) "error") } expect <- tryCatch({ if (!is.null(rows) && !is.null(cols)) { x <- x[rows,cols,drop=FALSE] w <- w[cols] } else if (!is.null(rows)) { x <- x[rows,,drop=FALSE] } else if (!is.null(cols)) { x <- x[,cols,drop=FALSE] w <- w[cols] } fsure(x, w, ...) }, error=function(c) "error") }) if (debug) cat(sprintf("actual=%s\nexpect=%s\n", toString(actual), toString(expect))) stopifnot(all.equal(actual, expect)) } indexCases <- list() # negative indices with duplicates indexCases[[length(indexCases)+1]] <- c(-4, 0, 0, -3, -1, -3, -1) # positive indices indexCases[[length(indexCases)+1]] <- c(3, 5, 1) # positive indices with duplicates indexCases[[length(indexCases)+1]] <- c(3, 0, 0, 5, 1, 5, 5) # positive indices out of ranges indexCases[[length(indexCases)+1]] <- 4:9 # negative out of ranges: just ignore indexCases[[length(indexCases)+1]] <- c(-5, 0, -3, -1, -9) # negative indices exclude all indexCases[[length(indexCases)+1]] <- -1:-6 # idxs is single number indexCases[[length(indexCases)+1]] <- 4 indexCases[[length(indexCases)+1]] <- -4 indexCases[[length(indexCases)+1]] <- 0 # idxs is empty indexCases[[length(indexCases)+1]] <- integer() # NA in idxs indexCases[[length(indexCases)+1]] <- c(NA_real_, 0, 2) # Inf in idxs indexCases[[length(indexCases)+1]] <- c(-Inf, -1) indexCases[[length(indexCases)+1]] <- c(NA_real_, 0, 2, Inf) # single logical indexCases[[length(indexCases)+1]] <- NA indexCases[[length(indexCases)+1]] <- TRUE indexCases[[length(indexCases)+1]] <- FALSE # full logical idxs indexCases[[length(indexCases)+1]] <- c(FALSE, TRUE, FALSE, TRUE, TRUE, FALSE) # too many logical idxs indexCases[[length(indexCases)+1]] <- c(FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, FALSE, TRUE) # insufficient idxs indexCases[[length(indexCases)+1]] <- c(FALSE, TRUE) indexCases[[length(indexCases)+1]] <- c(FALSE, TRUE, NA) indexCases[[length(indexCases)+1]] <- c(FALSE, TRUE, NA, FALSE) # NULL indexCases[length(indexCases)+1] <- list(NULL) indexErrorCases <- list() # mixed positive and negative indices indexErrorCases[[length(indexCases)+1]] <- 1:-1 # mixed positive, negative and zero indices indexErrorCases[[length(indexCases)+1]] <- c(-4, 0, 0, 1) # NA in idxs indexErrorCases[[length(indexCases)+1]] <- c(NA_real_, -2) matrixStats/tests/rowIQRs.R0000644000175100001440000000354012726216471015460 0ustar hornikuserslibrary("matrixStats") rowIQRs_R <- function(x, na.rm=FALSE) { quantileNA <- function(x, ..., na.rm=FALSE) { if (!na.rm && anyMissing(x)) return(c(NA_real_, NA_real_)) quantile(x, ..., na.rm=na.rm) } Q <- apply(x, MARGIN=1L, FUN=quantileNA, probs=c(0.25, 0.75), na.rm=na.rm) dim(Q) <- c(2L, nrow(x)) Q[2L,,drop=TRUE] - Q[1L,,drop=TRUE] } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Test with multiple quantiles # - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { cat("mode: ", mode, "\n", sep="") x <- matrix(1:100+0.1, nrow=10, ncol=10) storage.mode(x) <- mode str(x) for (addNA in c(FALSE, TRUE)) { if (addNA) { x[3:5,6:9] <- NA } for (na.rm in c(FALSE, TRUE)) { probs <- c(0,0.5,1) q0 <- rowIQRs_R(x, na.rm=na.rm) print(q0) q1 <- rowIQRs(x, na.rm=na.rm) print(q1) stopifnot(all.equal(q1, q0)) q2 <- colIQRs(t(x), na.rm=na.rm) stopifnot(all.equal(q2, q0)) q <- iqr(x[3,], na.rm=na.rm) print(q) } # for (na.rm ...) } # for (addNA ...) } # for (mode ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Test corner cases # - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { cat("mode: ", mode, "\n", sep="") # Empty vectors x <- integer(0L) storage.mode(x) <- mode str(x) q <- iqr(x) print(q) stopifnot(identical(q, NA_real_)) # Scalar x <- 1L storage.mode(x) <- mode str(x) q <- iqr(x) str(q) stopifnot(identical(q, 0)) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Single row matrices # - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(1, nrow=1L, ncol=2L) q <- rowIQRs(x) stopifnot(identical(q, 0)) x <- matrix(1, nrow=2L, ncol=1L) q <- colIQRs(x) stopifnot(identical(q, 0)) matrixStats/tests/rowAllAnys.R0000644000175100001440000001134412726216471016206 0ustar hornikuserslibrary("matrixStats") rowAlls_R <- function(x, value=TRUE, na.rm=FALSE, ...) { if (is.na(value)) { apply(is.na(x), MARGIN=1L, FUN=all, na.rm=na.rm) } else { y <- x == value dim(y) <- dim(x) # for 0×N and M×0 cases apply(y, MARGIN=1L, FUN=all, na.rm=na.rm) } } rowAnys_R <- function(x, value=TRUE, na.rm=FALSE, ...) { if (is.na(value)) { apply(is.na(x), MARGIN=1L, FUN=any, na.rm=na.rm) } else { y <- x == value dim(y) <- dim(x) # for 0×N and M×0 cases apply(y, MARGIN=1L, FUN=any, na.rm=na.rm) } } rowAnyMissings_R <- function(x, ...) { apply(x, MARGIN=1L, FUN=anyMissing) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Data type: logical # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(FALSE, nrow=20, ncol=5) x[13:17,c(2,4)] <- TRUE x[2:4,] <- TRUE x[,1] <- TRUE x[5,] <- FALSE x[,5] <- FALSE x[3,] <- FALSE x[4,] <- TRUE for (kk in 1:3) { if (kk == 2) { x[2,2] <- NA } else if (kk == 3) { x[,2] <- NA x[2,] <- NA } # Row/column all for (na.rm in c(FALSE, TRUE)) { m0 <- rowAlls_R(x, na.rm=na.rm) m1 <- rowAlls(x, na.rm=na.rm) m2 <- colAlls(t(x), na.rm=na.rm) str(list("all()", m0=m0, m1=m1, m2=m2)) stopifnot(identical(m1, m0)) stopifnot(identical(m2, m0)) } # Row/column any for (na.rm in c(FALSE, TRUE)) { m0 <- rowAnys_R(x, na.rm=na.rm) m1 <- rowAnys(x, na.rm=na.rm) m2 <- colAnys(t(x), na.rm=na.rm) str(list("any()", m0=m0, m1=m1, m2=m2)) stopifnot(identical(m1, m0)) stopifnot(identical(m2, m0)) m0 <- rowAnyMissings_R(x) m1 <- rowAnyMissings(x) m2 <- colAnyMissings(t(x)) str(list("anyMissing()", m0=m0, m1=m1, m2=m2)) stopifnot(identical(m1, m0)) stopifnot(identical(m2, m0)) } } # for (kk ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Data type: integer # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(rep(1:28, length.out=20*5), nrow=20, ncol=5) x[2,] <- 7L x[3,1] <- 7L x[2:3,3:4] <- NA_integer_ # Row/column counts value <- 7L for (na.rm in c(FALSE, TRUE)) { ## All r0 <- rowAlls_R(x, value=value, na.rm=na.rm) r1 <- rowAlls(x, value=value, na.rm=na.rm) r2 <- colAlls(t(x), value=value, na.rm=na.rm) stopifnot(identical(r1, r0)) stopifnot(identical(r2, r1)) for (rr in seq_len(nrow(x))) { c <- allValue(x[rr,], value=value, na.rm=na.rm) stopifnot(identical(c,r1[rr])) c <- allValue(x[rr,], value=value, na.rm=na.rm) stopifnot(identical(c,r1[rr])) } ## Any r0 <- rowAnys_R(x, value=value, na.rm=na.rm) r1 <- rowAnys(x, value=value, na.rm=na.rm) r2 <- colAnys(t(x), value=value, na.rm=na.rm) stopifnot(identical(r1, r0)) stopifnot(identical(r2, r1)) for (rr in seq_len(nrow(x))) { c <- anyValue(x[rr,], value=value, na.rm=na.rm) stopifnot(identical(c,r1[rr])) c <- anyValue(x[rr,], value=value, na.rm=na.rm) stopifnot(identical(c,r1[rr])) } } all_R <- function(x, value=TRUE, ...) { if (is.na(value)) { all(is.na(x), ...) } else { all(x == value, ...) } } any_R <- function(x, value=TRUE, ...) { if (is.na(value)) { any(is.na(x), ...) } else { any(x == value, ...) } } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Data type: character (not sure if this should be supported) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(rep(letters, length.out=20*5), nrow=20, ncol=5) x[2,] <- "g" x[2:4,3:4] <- NA_character_ # Row/column counts for (value in c("g", NA_character_)) { for (na.rm in c(FALSE, TRUE)) { ## All r0 <- rowAlls_R(x, value=value, na.rm=na.rm) r1 <- rowAlls(x, value=value, na.rm=na.rm) r2 <- colAlls(t(x), value=value, na.rm=na.rm) stopifnot(identical(r1, r0)) stopifnot(identical(r2, r1)) for (rr in seq_len(nrow(x))) { c0 <- all_R(x[rr,], value, na.rm=na.rm) c <- allValue(x[rr,], value=value, na.rm=na.rm) stopifnot(identical(c,r1[rr])) stopifnot(identical(c,c0)) } ## Any r0 <- rowAnys_R(x, value=value, na.rm=na.rm) r1 <- rowAnys(x, value=value, na.rm=na.rm) r2 <- colAnys(t(x), value=value, na.rm=na.rm) stopifnot(identical(r1, r0)) stopifnot(identical(r2, r1)) for (rr in seq_len(nrow(x))) { c0 <- any_R(x[rr,], value, na.rm=na.rm) c <- anyValue(x[rr,], value=value, na.rm=na.rm) stopifnot(identical(c,c0)) stopifnot(identical(c,r1[rr])) } } } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # NA 0 test # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(0, nrow=3, ncol=3) x[1,] <- c(NA_real_, NA_real_, 0) x[3,] <- c(1, 0, 1) r0 <- rowAnys_R(x, value=0) r1 <- rowAnys(x, value=0) stopifnot(identical(r0, r1)) matrixStats/tests/rowQuantiles.R0000644000175100001440000000727212726216471016615 0ustar hornikuserslibrary("matrixStats") rowQuantiles_R <- function(x, probs, na.rm=FALSE, drop=TRUE, ...) { q <- apply(x, MARGIN=1L, FUN=function(x, probs, na.rm) { if (!na.rm && any(is.na(x))) { naValue <- NA_real_ storage.mode(naValue) <- storage.mode(x) rep(naValue, length(probs)) } else { as.vector(quantile(x, probs=probs, na.rm=na.rm, ...)) } }, probs=probs, na.rm=na.rm) if (!is.null(dim(q))) q <- t(q) else dim(q) <- c(nrow(x), length(probs)) digits <- max(2L, getOption("digits")) colnames(q) <- sprintf("%.*g%%", digits, 100*probs) if (drop) q <- drop(q) q } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Test with multiple quantiles # - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { cat("mode: ", mode, "\n", sep="") x <- matrix(1:40+0.1, nrow=8, ncol=5) storage.mode(x) <- mode str(x) probs <- c(0,0.5,1) q0 <- rowQuantiles_R(x, probs=probs) print(q0) q1 <- rowQuantiles(x, probs=probs) print(q1) stopifnot(all.equal(q1, q0)) q2 <- colQuantiles(t(x), probs=probs) stopifnot(all.equal(q2, q0)) } # for (mode ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Test with a single quantile # - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (mode in c("integer", "double")) { cat("mode: ", mode, "\n", sep="") x <- matrix(1:40, nrow=8, ncol=5) storage.mode(x) <- mode str(x) probs <- c(0.5) q0 <- rowQuantiles_R(x, probs=probs) print(q0) q1 <- rowQuantiles(x, probs=probs) print(q1) stopifnot(all.equal(q1, q0)) q2 <- colQuantiles(t(x), probs=probs) stopifnot(all.equal(q2, q0)) } # for (mode ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Consistency checks # - - - - - - - - - - - - - - - - - - - - - - - - - - - - set.seed(1) probs <- seq(from=0, to=1, by=0.25) cat("Consistency checks:\n") K <- if (Sys.getenv("_R_CHECK_FULL_") == "" || Sys.getenv("_R_CHECK_USE_VALGRIND_") != "") 4 else 20 for (kk in seq_len(K)) { cat("Random test #", kk, "\n", sep="") # Simulate data in a matrix of any shape dim <- sample(20:60, size=2L) n <- prod(dim) x <- rnorm(n, sd=100) dim(x) <- dim # Add NAs? hasNA <- (kk %% 4) %in% c(3,0); if (hasNA) { cat("Adding NAs\n") nna <- sample(n, size=1) naValues <- c(NA_real_, NaN) x[sample(length(x), size=nna)] <- sample(naValues, size=nna, replace=TRUE) } # Integer or double? if ((kk %% 4) %in% c(2,0)) { cat("Coercing to integers\n") storage.mode(x) <- "integer" } str(x) # rowQuantiles(): q0 <- rowQuantiles_R(x, probs=probs, na.rm=hasNA) q1 <- rowQuantiles(x, probs=probs, na.rm=hasNA) stopifnot(all.equal(q1, q0)) q2 <- colQuantiles(t(x), probs=probs, na.rm=hasNA) stopifnot(all.equal(q2, q0)) } # for (kk ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Empty matrices # - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(NA_real_, nrow=0L, ncol=0L) probs <- c(0, 0.25, 0.75, 1) q <- rowQuantiles(x, probs=probs) stopifnot(identical(dim(q), c(nrow(x), length(probs)))) q <- colQuantiles(x, probs=probs) stopifnot(identical(dim(q), c(ncol(x), length(probs)))) x <- matrix(NA_real_, nrow=2L, ncol=0L) q <- rowQuantiles(x, probs=probs) stopifnot(identical(dim(q), c(nrow(x), length(probs)))) x <- matrix(NA_real_, nrow=0L, ncol=2L) q <- colQuantiles(x, probs=probs) stopifnot(identical(dim(q), c(ncol(x), length(probs)))) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Single column matrices # - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- matrix(1, nrow=2L, ncol=1L) q <- rowQuantiles(x, probs=probs) print(q) x <- matrix(1, nrow=1L, ncol=2L) q <- colQuantiles(x, probs=probs) print(q) matrixStats/src/0000755000175100001440000000000012776336765013430 5ustar hornikusersmatrixStats/src/validateIndices_TYPE-template.h0000644000175100001440000001116512776336765021347 0ustar hornikusers/*********************************************************************** TEMPLATE: void validateIndices_(X_C_TYPE *idxs, R_xlen_t nidxs, R_xlen_t maxIdx, int allowOutOfBound, R_xlen_t *ansNidxs, int *subsettedType, int *hasna) Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD_NAME: the name of the resulting function - X_TYPE: 'i', 'r' ***********************************************************************/ #include #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, [METHOD_NAME]) */ #include "templates-types.h" #undef IntegerFromIndex_TYPE #undef RealFromIndex_TYPE #if X_TYPE == 'i' #define IntegerFromIndex_TYPE CONCAT_MACROS(IntegerFromIndex, Integer) #define RealFromIndex_TYPE CONCAT_MACROS(RealFromIndex, Integer) #elif X_TYPE == 'r' #define IntegerFromIndex_TYPE CONCAT_MACROS(IntegerFromIndex, Real) #define RealFromIndex_TYPE CONCAT_MACROS(RealFromIndex, Real) #endif static R_INLINE int IntegerFromIndex_TYPE(X_C_TYPE x, R_xlen_t maxIdx) { if (X_ISNAN(x)) return NA_INTEGER; #if X_TYPE == 'r' if (x > R_INT_MAX || x < R_INT_MIN) return NA_INTEGER; // including the cases of Inf #endif if (x > maxIdx) return NA_INTEGER; return x; } static R_INLINE int RealFromIndex_TYPE(X_C_TYPE x, R_xlen_t maxIdx) { if (X_ISNAN(x)) return NA_REAL; #if X_TYPE == 'r' if (IS_INF(x)) return NA_REAL; #endif if (x > maxIdx) return NA_REAL; return x; } /** idxs must not be NULL, which should be checked before calling this function. **/ void* METHOD_NAME(X_C_TYPE *idxs, R_xlen_t nidxs, R_xlen_t maxIdx, int allowOutOfBound, R_xlen_t *ansNidxs, int *subsettedType, int *hasna) { // set default as no NA. *hasna = FALSE; // For a un-full positive legal idxs array, we should use SUBSETTED_INTEGER as default. *subsettedType = SUBSETTED_INTEGER; R_xlen_t ii, jj; int state = 0; R_xlen_t count = 0; Rboolean needReAlloc = FALSE; // figure out whether idxs are all positive or all negative. for (ii = 0; ii < nidxs; ++ ii) { X_C_TYPE idx = idxs[ii]; if (idx > 0 || X_ISNAN(idx) #if X_TYPE == 'r' || IS_INF(idx) #endif ) { if (state < 0) error("only 0's may be mixed with negative subscripts"); #if X_TYPE == 'r' if (IS_INF(idx)) { needReAlloc = TRUE; // need to realloc indices array to set inf to NA } else #endif if (!X_ISNAN(idx)) { if (idx > maxIdx) { if (!allowOutOfBound) error("subscript out of bounds"); *hasna = TRUE; // out-of-bound index is NA needReAlloc = TRUE; } #if X_TYPE == 'r' if (idx > R_INT_MAX) *subsettedType = SUBSETTED_REAL; #endif } else { *hasna = TRUE; } state = 1; ++ count; } else if (idx < 0) { if (state > 0) error("only 0's may be mixed with negative subscripts"); state = -1; needReAlloc = TRUE; } else { // idx == 0, need to realloc indices array needReAlloc = TRUE; } } if (state >= 0) *ansNidxs = count; if (!needReAlloc) { // must have: state >= 0 *subsettedType = SUBSETTED_DEFAULT; return idxs; } // fill positive idxs into ans if (state >= 0) { if (*subsettedType == SUBSETTED_INTEGER) { // NOTE: braces is needed here, because of macro-defined function RETURN_VALIDATED_ANS(int, nidxs, idxs[ii], IntegerFromIndex_TYPE(idxs[ii],maxIdx),); } // *subsettedType == SUBSETTED_REAL RETURN_VALIDATED_ANS(double, nidxs, idxs[ii], RealFromIndex_TYPE(idxs[ii],maxIdx),); } // state < 0 // use filter as bitset to find out all required idxs Rboolean *filter = Calloc(maxIdx, Rboolean); count = maxIdx; memset(filter, 0, maxIdx*sizeof(Rboolean)); // set to FALSE for (ii = 0; ii < nidxs; ++ ii) { R_xlen_t idx = -idxs[ii]; if (idx > 0 && idx <= maxIdx) { if (filter[idx-1] == 0) { -- count; filter[idx-1] = TRUE; } } } *ansNidxs = count; if (count == 0) { Free(filter); return NULL; } // find the biggest number 'upperBound' R_xlen_t upperBound; for (upperBound = maxIdx-1; upperBound >= 0; -- upperBound) { if (!filter[upperBound]) break; } ++ upperBound; if (upperBound > R_INT_MAX) *subsettedType = SUBSETTED_REAL; // fill required idxs into ans if (*subsettedType == SUBSETTED_INTEGER) { // NOTE: braces is needed here, because of macro-defined function RETURN_VALIDATED_ANS(int, upperBound, !filter[ii], ii + 1, Free(filter);); } // *subsettedType == SUBSETTED_REAL RETURN_VALIDATED_ANS(double, upperBound, !filter[ii], ii + 1, Free(filter);); } #include "templates-types_undef.h" matrixStats/src/productExpSumLog.c0000644000175100001440000000334612776336765017066 0ustar hornikusers/*************************************************************************** Public methods: SEXP productExpSumLog(SEXP x, SEXP idxs, SEXP naRm, SEXP hasNA) Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD productExpSumLog #define RETURN_TYPE double #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nx, void *idxs, R_xlen_t nidxs, int narm, int hasna #define X_TYPE 'i' #include "templates-gen-vector.h" #define X_TYPE 'r' #include "templates-gen-vector.h" SEXP productExpSumLog(SEXP x, SEXP idxs, SEXP naRm, SEXP hasNA) { SEXP ans = NILSXP; R_xlen_t nx; double res = NA_REAL; int narm, hasna; /* Argument 'x': */ assertArgVector(x, (R_TYPE_INT | R_TYPE_REAL), "x"); nx = xlength(x); /* Argument 'naRm': */ narm = asLogicalNoNA(naRm, "na.rm"); /* Argument 'hasNA': */ hasna = asLogicalNoNA(hasNA, "hasNA"); /* Argument 'idxs': */ R_xlen_t nidxs; int idxsType; void *cidxs = validateIndices(idxs, nx, 1, &nidxs, &idxsType); /* Double matrices are more common to use. */ if (isReal(x)) { res = productExpSumLog_Real[idxsType](REAL(x), nx, cidxs, nidxs, narm, hasna); } else if (isInteger(x)) { res = productExpSumLog_Integer[idxsType](INTEGER(x), nx, cidxs, nidxs, narm, hasna); } /* Return results */ PROTECT(ans = allocVector(REALSXP, 1)); REAL(ans)[0] = res; UNPROTECT(1); return(ans); } // productExpSumLog() /*************************************************************************** HISTORY: 2015-07-04 [DJ] o Supported subsetted computation. 2014-06-04 [HB] o Created. **************************************************************************/ matrixStats/src/weightedMean.c0000644000175100001440000000372312776336765016202 0ustar hornikusers/*************************************************************************** Public methods: SEXP weightedMean(SEXP x, SEXP w, SEXP idxs, SEXP naRm, SEXP refine) Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #include #define METHOD weightedMean #define RETURN_TYPE double #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nx, double *w, void *idxs, R_xlen_t nidxs, int narm, int refine #define X_TYPE 'i' #include "templates-gen-vector.h" #define X_TYPE 'r' #include "templates-gen-vector.h" SEXP weightedMean(SEXP x, SEXP w, SEXP idxs, SEXP naRm, SEXP refine) { SEXP ans; int narm, refine2; double avg = NA_REAL; R_xlen_t nx, nw; /* Argument 'x': */ assertArgVector(x, (R_TYPE_INT | R_TYPE_REAL), "x"); nx = xlength(x); /* Argument 'x': */ assertArgVector(w, (R_TYPE_REAL), "w"); nw = xlength(w); if (nx != nw) { error("Argument 'x' and 'w' are of different lengths: %d != %d", nx, nw); } /* Argument 'naRm': */ narm = asLogicalNoNA(naRm, "na.rm"); /* Argument 'refine': */ refine2 = asLogicalNoNA(refine, "refine"); /* Argument 'idxs': */ R_xlen_t nidxs; int idxsType; void *cidxs = validateIndices(idxs, nx, 1, &nidxs, &idxsType); /* Double matrices are more common to use. */ if (isReal(x)) { avg = weightedMean_Real[idxsType](REAL(x), nx, REAL(w), cidxs, nidxs, narm, refine2); } else if (isInteger(x)) { avg = weightedMean_Integer[idxsType](INTEGER(x), nx, REAL(w), cidxs, nidxs, narm, refine2); } /* Return results */ PROTECT(ans = allocVector(REALSXP, 1)); REAL(ans)[0] = avg; UNPROTECT(1); return(ans); } // weightedMean() /*************************************************************************** HISTORY: 2015-06-07 [DJ] o Supported subsetted computation. 2014-12-08 [HB] o Created. **************************************************************************/ matrixStats/src/rowCumprods_TYPE-template.h0000644000175100001440000001027512776336765020604 0ustar hornikusers/*********************************************************************** TEMPLATE: void rowCumprods_[rowsType][colsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int byrow, ANS_C_TYPE *ans Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD: the name of the resulting function - X_TYPE: 'i' or 'r' Authors: Henrik Bengtsson. Copyright: Henrik Bengtsson, 2014 ***********************************************************************/ #include #include #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, X_ISNAN, [METHOD_NAME]) */ #include "templates-types.h" RETURN_TYPE METHOD_NAME_ROWS_COLS(ARGUMENTS_LIST) { R_xlen_t ii, jj, kk, kk_prev, idx; R_xlen_t colBegin; X_C_TYPE xvalue; LDOUBLE value; #ifdef ROWS_TYPE ROWS_C_TYPE *crows = (ROWS_C_TYPE*) rows; #endif #ifdef COLS_TYPE COLS_C_TYPE *ccols = (COLS_C_TYPE*) cols; #endif #if ANS_TYPE == 'i' double R_INT_MIN_d = (double)R_INT_MIN, R_INT_MAX_d = (double)R_INT_MAX; /* OK, i.e. no integer overflow yet? */ int warn = 0, ok, *oks = NULL; #endif if (ncols == 0 || nrows == 0) return; if (byrow) { #if ANS_TYPE == 'i' oks = (int *) R_alloc(nrows, sizeof(int)); #endif colBegin = R_INDEX_OP(COL_INDEX(ccols,0), *, nrow); for (kk=0; kk < nrows; kk++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,kk)); xvalue = R_INDEX_GET(x, idx, X_NA); ans[kk] = (ANS_C_TYPE) xvalue; #if ANS_TYPE == 'i' oks[kk] = !X_ISNA(xvalue); #endif } kk_prev = 0; for (jj=1; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); for (ii=0; ii < nrows; ii++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); #if ANS_TYPE == 'i' if (oks[ii]) { if (X_ISNA(xvalue)) { oks[ii] = 0; ans[kk] = ANS_NA; } else { value = (LDOUBLE) ans[kk_prev] * (LDOUBLE) xvalue; /* Integer overflow? */ if (value < R_INT_MIN_d || value > R_INT_MAX_d) { oks[ii] = 0; warn = 1; ans[kk] = ANS_NA; } else { ans[kk] = (ANS_C_TYPE) value; } } } else { ans[kk] = ANS_NA; } #else ans[kk] = (ANS_C_TYPE) ((LDOUBLE) ans[kk_prev] * (LDOUBLE) xvalue); #endif kk++; kk_prev++; R_CHECK_USER_INTERRUPT(kk); } /* for (ii ...) */ } /* for (jj ...) */ } else { kk = 0; for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); value = 1; #if ANS_TYPE == 'i' ok = 1; #endif for (ii=0; ii < nrows; ii++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); #if ANS_TYPE == 'i' if (ok) { if (X_ISNA(xvalue)) { ok = 0; ans[kk] = ANS_NA; } else { value *= (LDOUBLE) xvalue; /* Integer overflow? */ if (value < R_INT_MIN_d || value > R_INT_MAX_d) { ok = 0; warn = 1; ans[kk] = ANS_NA; } else { ans[kk] = (ANS_C_TYPE) value; } } } else { ans[kk] = ANS_NA; } #else value *= xvalue; ans[kk] = (ANS_C_TYPE) value; #endif kk++; R_CHECK_USER_INTERRUPT(kk); } /* for (ii ...) */ } /* for (jj ...) */ } /* if (byrow) */ #if ANS_TYPE == 'i' /* Warn on integer overflow? */ if (warn) { warning("Integer overflow. Detected one or more elements whose absolute values were out of the range [%d,%d] that can be used to for integers. Such values are set to NA_integer_.", R_INT_MIN, R_INT_MAX); } #endif } /*************************************************************************** HISTORY: 2015-06-07 [DJ] o Supported subsetted computation. 2014-11-26 [HB] o Created from rowVars_TYPE-template.h. **************************************************************************/ matrixStats/src/x_OP_y.c0000644000175100001440000002104612776336765014774 0ustar hornikusers#include #include "types.h" #include "utils.h" #define METHOD_TEMPLATE_H "x_OP_y_TYPE-template.h" #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, \ Y_C_TYPE *y, R_xlen_t ny, \ void *xrows, R_xlen_t nxrows, void *xcols, R_xlen_t nxcols, \ void *yidxs, R_xlen_t nyidxs, \ int byrow, int commute, \ int narm, int hasna, \ ANS_C_TYPE *ans, R_xlen_t n /* Addition */ #define METHOD x_OP_y_Add #define X_TYPE 'i' #define Y_TYPE 'i' #define ANS_TYPE 'i' #define OP '+' #include "templates-gen-matrix-vector.h" #define X_TYPE 'i' #define Y_TYPE 'r' #define ANS_TYPE 'r' #define OP '+' #include "templates-gen-matrix-vector.h" #define X_TYPE 'r' #define Y_TYPE 'i' #define ANS_TYPE 'r' #define OP '+' #include "templates-gen-matrix-vector.h" #define X_TYPE 'r' #define Y_TYPE 'r' #define ANS_TYPE 'r' #define OP '+' #include "templates-gen-matrix-vector.h" #undef METHOD /* Subtraction */ #define METHOD x_OP_y_Sub #define X_TYPE 'i' #define Y_TYPE 'i' #define ANS_TYPE 'i' #define OP '-' #include "templates-gen-matrix-vector.h" #define X_TYPE 'i' #define Y_TYPE 'r' #define ANS_TYPE 'r' #define OP '-' #include "templates-gen-matrix-vector.h" #define X_TYPE 'r' #define Y_TYPE 'i' #define ANS_TYPE 'r' #define OP '-' #include "templates-gen-matrix-vector.h" #define X_TYPE 'r' #define Y_TYPE 'r' #define ANS_TYPE 'r' #define OP '-' #include "templates-gen-matrix-vector.h" #undef METHOD /* Multiplication */ #define METHOD x_OP_y_Mul #define X_TYPE 'i' #define Y_TYPE 'i' #define ANS_TYPE 'i' #define OP '*' #include "templates-gen-matrix-vector.h" #define X_TYPE 'i' #define Y_TYPE 'r' #define ANS_TYPE 'r' #define OP '*' #include "templates-gen-matrix-vector.h" #define X_TYPE 'r' #define Y_TYPE 'i' #define ANS_TYPE 'r' #define OP '*' #include "templates-gen-matrix-vector.h" #define X_TYPE 'r' #define Y_TYPE 'r' #define ANS_TYPE 'r' #define OP '*' #include "templates-gen-matrix-vector.h" #undef METHOD /* Division */ #define METHOD x_OP_y_Div #define X_TYPE 'i' #define Y_TYPE 'i' #define ANS_TYPE 'r' #define OP '/' #include "templates-gen-matrix-vector.h" #define X_TYPE 'i' #define Y_TYPE 'r' #define ANS_TYPE 'r' #define OP '/' #include "templates-gen-matrix-vector.h" #define X_TYPE 'r' #define Y_TYPE 'i' #define ANS_TYPE 'r' #define OP '/' #include "templates-gen-matrix-vector.h" #define X_TYPE 'r' #define Y_TYPE 'r' #define ANS_TYPE 'r' #define OP '/' #include "templates-gen-matrix-vector.h" #undef METHOD SEXP x_OP_y(SEXP x, SEXP y, SEXP dim, SEXP operator, SEXP xrows, SEXP xcols, SEXP yidxs, SEXP commute, SEXP naRm, SEXP hasNA, SEXP byRow) { SEXP ans = NILSXP; int narm, hasna, byrow, commute2; int op; R_xlen_t nrow, ncol, ny; /* Argument 'x' and 'dim': */ assertArgMatrix(x, dim, (R_TYPE_INT | R_TYPE_REAL), "x"); nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'y': */ assertArgVector(y, (R_TYPE_INT | R_TYPE_REAL), "y"); ny = xlength(y); /* Argument 'byRow': */ byrow = asLogicalNoNA(byRow, "byrow"); /* Argument 'commute2': */ commute2 = asLogicalNoNA(commute, "commute"); /* Argument 'naRm': */ narm = asLogicalNoNA(naRm, "na.rm"); /* Argument 'hasNA': */ hasna = asLogicalNoNA(hasNA, "hasNA"); /* Argument 'xrows', 'xcols' and 'yidxs': */ R_xlen_t nxrows, nxcols, nyidxs; int xrowsType, xcolsType, yidxsType; void *cxrows = validateIndices(xrows, nrow, 0, &nxrows, &xrowsType); void *cxcols = validateIndices(xcols, ncol, 0, &nxcols, &xcolsType); void *cyidxs = validateIndices(yidxs, ny, 1, &nyidxs, &yidxsType); /* Argument 'operator': */ op = asInteger(operator); if (op == 1) { /* Addition */ if (isReal(x) || isReal(y)) { PROTECT(ans = allocMatrix(REALSXP, nxrows, nxcols)); if (isReal(x) && isReal(y)) { x_OP_y_Add_Real_Real[xrowsType][xcolsType][yidxsType]( REAL(x), nrow, ncol, REAL(y), ny, cxrows, nxrows, cxcols, nxcols, cyidxs, nyidxs, byrow, commute2, narm, hasna, REAL(ans), xlength(ans)); } else if (isReal(x) && isInteger(y)) { x_OP_y_Add_Real_Integer[xrowsType][xcolsType][yidxsType]( REAL(x), nrow, ncol, INTEGER(y), ny, cxrows, nxrows, cxcols, nxcols, cyidxs, nyidxs, byrow, commute2, narm, hasna, REAL(ans), xlength(ans)); } else if (isInteger(x) && isReal(y)) { x_OP_y_Add_Integer_Real[xrowsType][xcolsType][yidxsType]( INTEGER(x), nrow, ncol, REAL(y), ny, cxrows, nxrows, cxcols, nxcols, cyidxs, nyidxs, byrow, commute2, narm, hasna, REAL(ans), xlength(ans)); } UNPROTECT(1); } else { PROTECT(ans = allocMatrix(INTSXP, nxrows, nxcols)); x_OP_y_Add_Integer_Integer[xrowsType][xcolsType][yidxsType]( INTEGER(x), nrow, ncol, INTEGER(y), ny, cxrows, nxrows, cxcols, nxcols, cyidxs, nyidxs, byrow, commute2, narm, hasna, INTEGER(ans), xlength(ans)); UNPROTECT(1); } } if (op == 2) { /* Subtraction */ if (isReal(x) || isReal(y)) { PROTECT(ans = allocMatrix(REALSXP, nxrows, nxcols)); if (isReal(x) && isReal(y)) { x_OP_y_Sub_Real_Real[xrowsType][xcolsType][yidxsType]( REAL(x), nrow, ncol, REAL(y), ny, cxrows, nxrows, cxcols, nxcols, cyidxs, nyidxs, byrow, commute2, narm, hasna, REAL(ans), xlength(ans)); } else if (isReal(x) && isInteger(y)) { x_OP_y_Sub_Real_Integer[xrowsType][xcolsType][yidxsType]( REAL(x), nrow, ncol, INTEGER(y), ny, cxrows, nxrows, cxcols, nxcols, cyidxs, nyidxs, byrow, commute2, narm, hasna, REAL(ans), xlength(ans)); } else if (isInteger(x) && isReal(y)) { x_OP_y_Sub_Integer_Real[xrowsType][xcolsType][yidxsType]( INTEGER(x), nrow, ncol, REAL(y), ny, cxrows, nxrows, cxcols, nxcols, cyidxs, nyidxs, byrow, commute2, narm, hasna, REAL(ans), xlength(ans)); } UNPROTECT(1); } else { PROTECT(ans = allocMatrix(INTSXP, nxrows, nxcols)); x_OP_y_Sub_Integer_Integer[xrowsType][xcolsType][yidxsType]( INTEGER(x), nrow, ncol, INTEGER(y), ny, cxrows, nxrows, cxcols, nxcols, cyidxs, nyidxs, byrow, commute2, narm, hasna, INTEGER(ans), xlength(ans)); UNPROTECT(1); } } else if (op == 3) { /* Multiplication */ if (isReal(x) || isReal(y)) { PROTECT(ans = allocMatrix(REALSXP, nxrows, nxcols)); if (isReal(x) && isReal(y)) { x_OP_y_Mul_Real_Real[xrowsType][xcolsType][yidxsType]( REAL(x), nrow, ncol, REAL(y), ny, cxrows, nxrows, cxcols, nxcols, cyidxs, nyidxs, byrow, commute2, narm, hasna, REAL(ans), xlength(ans)); } else if (isReal(x) && isInteger(y)) { x_OP_y_Mul_Real_Integer[xrowsType][xcolsType][yidxsType]( REAL(x), nrow, ncol, INTEGER(y), ny, cxrows, nxrows, cxcols, nxcols, cyidxs, nyidxs, byrow, commute2, narm, hasna, REAL(ans), xlength(ans)); } else if (isInteger(x) && isReal(y)) { x_OP_y_Mul_Integer_Real[xrowsType][xcolsType][yidxsType]( INTEGER(x), nrow, ncol, REAL(y), ny, cxrows, nxrows, cxcols, nxcols, cyidxs, nyidxs, byrow, commute2, narm, hasna, REAL(ans), xlength(ans)); } UNPROTECT(1); } else { PROTECT(ans = allocMatrix(INTSXP, nxrows, nxcols)); x_OP_y_Mul_Integer_Integer[xrowsType][xcolsType][yidxsType]( INTEGER(x), nrow, ncol, INTEGER(y), ny, cxrows, nxrows, cxcols, nxcols, cyidxs, nyidxs, byrow, commute2, narm, hasna, INTEGER(ans), xlength(ans)); UNPROTECT(1); } } else if (op == 4) { /* Division */ PROTECT(ans = allocMatrix(REALSXP, nxrows, nxcols)); if (isReal(x) && isReal(y)) { x_OP_y_Div_Real_Real[xrowsType][xcolsType][yidxsType]( REAL(x), nrow, ncol, REAL(y), ny, cxrows, nxrows, cxcols, nxcols, cyidxs, nyidxs, byrow, commute2, narm, hasna, REAL(ans), xlength(ans)); } else if (isReal(x) && isInteger(y)) { x_OP_y_Div_Real_Integer[xrowsType][xcolsType][yidxsType]( REAL(x), nrow, ncol, INTEGER(y), ny, cxrows, nxrows, cxcols, nxcols, cyidxs, nyidxs, byrow, commute2, narm, hasna, REAL(ans), xlength(ans)); } else if (isInteger(x) && isReal(y)) { x_OP_y_Div_Integer_Real[xrowsType][xcolsType][yidxsType]( INTEGER(x), nrow, ncol, REAL(y), ny, cxrows, nxrows, cxcols, nxcols, cyidxs, nyidxs, byrow, commute2, narm, hasna, REAL(ans), xlength(ans)); } else if (isInteger(x) && isInteger(y)) { x_OP_y_Div_Integer_Integer[xrowsType][xcolsType][yidxsType]( INTEGER(x), nrow, ncol, INTEGER(y), ny, cxrows, nxrows, cxcols, nxcols, cyidxs, nyidxs, byrow, commute2, narm, hasna, REAL(ans), xlength(ans)); } UNPROTECT(1); } return(ans); } /* x_OP_y() */ matrixStats/src/rowOrderStats.c0000644000175100001440000000617512776336765016427 0ustar hornikusers/*************************************************************************** Public methods: SEXP rowOrderStats(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP which) Authors: Henrik Bengtsson. Adopted from rowQ() by R. Gentleman. To do: Add support for missing values. Copyright Henrik Bengtsson, 2007-2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD rowOrderStats #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, R_xlen_t qq, X_C_TYPE *ans #define X_TYPE 'i' #include "templates-gen-matrix.h" #define X_TYPE 'r' #include "templates-gen-matrix.h" SEXP rowOrderStats(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP which) { SEXP ans = NILSXP; R_xlen_t nrow, ncol, qq; /* Argument 'x' and 'dim': */ assertArgMatrix(x, dim, (R_TYPE_INT | R_TYPE_REAL), "x"); nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'which': */ if (length(which) != 1) error("Argument 'which' must be a single number."); if (!isNumeric(which)) error("Argument 'which' must be a numeric number."); /* Argument 'rows' and 'cols': */ R_xlen_t nrows, ncols; int rowsType, colsType; int rowsHasna, colsHasna; void *crows = validateIndicesCheckNA(rows, nrow, 0, &nrows, &rowsType, &rowsHasna); void *ccols = validateIndicesCheckNA(cols, ncol, 0, &ncols, &colsType, &colsHasna); // Check missing rows if (rowsHasna && ncols > 0) { error("Argument 'rows' must not contain missing value"); } // Check missing cols if (colsHasna && nrows > 0) { error("Argument 'cols' must not contain missing value"); } /* Subtract one here, since rPsort does zero based addressing */ qq = asInteger(which) - 1; /* Assert that 'qq' is a valid index */ if (qq < 0 || qq >= ncols) { error("Argument 'which' is out of range."); } /* Double matrices are more common to use. */ if (isReal(x)) { PROTECT(ans = allocVector(REALSXP, nrows)); rowOrderStats_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, qq, REAL(ans)); UNPROTECT(1); } else if (isInteger(x)) { PROTECT(ans = allocVector(INTSXP, nrows)); rowOrderStats_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, qq, INTEGER(ans)); UNPROTECT(1); } return(ans); } // rowOrderStats() /*************************************************************************** HISTORY: 2015-07-11 [DJ] o Supported subsetted computation. 2009-02-04 [HB] o BUG FIX: For some errors in rowOrderStats(), the stack would not become UNPROTECTED before calling error. 2008-03-25 [HB] o Renamed from 'rowQuantiles' to 'rowOrderStats'. 2007-08-10 [HB] o Removed arguments for NAs since rowOrderStats() still don't support it. 2005-11-24 [HB] o Cool, it works and compiles nicely. o Preallocate colOffset to speed up things even more. o Added more comments and error checking. o Adopted from rowQ() in Biobase of Bioconductor. **************************************************************************/ matrixStats/src/binMeans.c0000644000175100001440000000551712776336765015340 0ustar hornikusers/*************************************************************************** Public methods: binMeans(SEXP y, SEXP x, SEXP bx, SEXP retCount, SEXP right) Copyright Henrik Bengtsson, 2012-2013 **************************************************************************/ #include #include "types.h" #include "utils.h" #include #define BIN_BY 'L' #include "binMeans-BINBY-template.h" #define BIN_BY 'R' #include "binMeans-BINBY-template.h" SEXP binMeans(SEXP y, SEXP x, SEXP bx, SEXP retCount, SEXP right) { SEXP ans = NILSXP, count = NILSXP; R_xlen_t nx, ny, nbins; int closedRight, retcount; int *count_ptr = NULL; /* Argument 'y': */ assertArgVector(y, (R_TYPE_REAL), "y"); ny = xlength(y); /* Argument 'x': */ assertArgVector(x, (R_TYPE_REAL), "x"); nx = xlength(x); if (nx != ny) { error("Argument 'y' and 'x' are of different lengths: %d != %d", ny, nx); } /* Argument 'bx': */ assertArgVector(bx, (R_TYPE_REAL), "bx"); nbins = xlength(bx)-1; if (nbins <= 0) { error("Argument 'bx' must specify at least two bin boundaries (= one bin): %d", xlength(bx)); } /* Argument 'right': */ closedRight = asLogicalNoNA(right, "right"); /* Argument 'retCount': */ retcount = asLogicalNoNA(retCount, "retCount"); PROTECT(ans = allocVector(REALSXP, nbins)); if (retcount) { PROTECT(count = allocVector(INTSXP, nbins)); count_ptr = INTEGER(count); } if (closedRight) { binMeans_R(REAL(y), ny, REAL(x), nx, REAL(bx), nbins, REAL(ans), count_ptr); } else { binMeans_L(REAL(y), ny, REAL(x), nx, REAL(bx), nbins, REAL(ans), count_ptr); } if (retcount) { setAttrib(ans, install("count"), count); UNPROTECT(1); // 'count' } UNPROTECT(1); // 'ans' return ans; return(ans); } // binMeans() /*************************************************************************** HISTORY: 2015-05-30 [HB] o Added protected against 'bx' too short. 2014-10-06 [HB] o CLEANUP: All argument validation is now done by the high-level C API. 2014-06-02 [HB] o CLEANUP: Removed unused variable in binMeans(). 2013-10-08 [HB] o Now binCounts() calls binCounts_(). 2013-05-10 [HB] o SPEEDUP: binMeans() no longer tests in every iteration (=for every data point) whether the last bin has been reached or not. 2012-10-10 [HB] o BUG FIX: binMeans() would return random/garbage means/counts for bins that were beyond the last data point. o BUG FIX: In some cases binMeans() could try to go past the last bin. 2012-10-03 [HB] o Created binMeans(), which was adopted from from code proposed by Martin Morgan (Fred Hutchinson Cancer Research Center, Seattle) as a reply to HB's R-devel thread 'Fastest non-overlapping binning mean function out there?' on Oct 3, 2012. **************************************************************************/ matrixStats/src/sumOver.c0000644000175100001440000000507512776336765015243 0ustar hornikusers/*************************************************************************** Public methods: SEXP sumOver(SEXP x, SEXP idxs, SEXP naRm, SEXP mode) Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include #include "types.h" #include "utils.h" #define METHOD sumOver #define RETURN_TYPE double #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nx, int *idxs, R_xlen_t nidxs, int narm, int mode #define X_TYPE 'i' #include "templates-gen-vector.h" #define X_TYPE 'r' #include "templates-gen-vector.h" SEXP sumOver(SEXP x, SEXP idxs, SEXP naRm, SEXP mode) { SEXP ans = NILSXP; R_xlen_t nx; int narm, mode2; double sum; /* Argument 'x': */ assertArgVector(x, (R_TYPE_INT | R_TYPE_REAL), "x"); nx = xlength(x); /* Argument 'naRm': */ narm = asLogicalNoNA(naRm, "na.rm"); /* Argument 'mode': */ if (!isInteger(mode)) { error("Argument 'mode' must be a single integer."); } mode2 = asInteger(mode); /* Argument 'idxs': */ R_xlen_t nidxs; int idxsType; void *cidxs = validateIndices(idxs, nx, 1, &nidxs, &idxsType); /* Dispatch to low-level C function */ if (isReal(x)) { sum = sumOver_Real[idxsType](REAL(x), nx, cidxs, nidxs, narm, mode2); } else if (isInteger(x)) { sum = sumOver_Integer[idxsType](INTEGER(x), nx, cidxs, nidxs, narm, mode2); } else { error("Argument 'x' must be numeric."); } /* Return results */ switch (mode2) { case 1: /* integer */ PROTECT(ans = allocVector(INTSXP, 1)); if (ISNAN(sum)) { INTEGER(ans)[0] = NA_INTEGER; } else if (sum > R_INT_MAX || sum < R_INT_MIN) { Rf_warning("Integer overflow. Use sumOver(..., mode=\"numeric\") to avoid this."); INTEGER(ans)[0] = NA_INTEGER; } else { INTEGER(ans)[0] = (int)sum; } UNPROTECT(1); break; case 2: /* numeric */ PROTECT(ans = allocVector(REALSXP, 1)); if (sum > DOUBLE_XMAX) { REAL(ans)[0] = R_PosInf; } else if (sum < -DOUBLE_XMAX) { REAL(ans)[0] = R_NegInf; } else { REAL(ans)[0] = sum; } UNPROTECT(1); break; default: /* To please compiler */ ans = NILSXP; break; } return(ans); } // sumOver() /*************************************************************************** HISTORY: 2015-07-11 [DJ] o Supported subsetted computation. 2014-11-06 [HB] o Moved validation of arguments and construction of return object to this function. 2014-11-02 [HB] o Created. **************************************************************************/ matrixStats/src/allocMatrix2.c0000644000175100001440000001061512776336765016140 0ustar hornikusers#include #include "types.h" #include /* Checks whether setting bytes of an int/double to all zeroes corresponds to assigning a zero value. Note that the bit representation of int's and double's may not be the same on all architectures. */ int memset_zero_ok_int() { int t = 1; memset(&t, 0, sizeof(t)); return (t == 0); } int memset_zero_ok_double() { double t = 1; memset(&t, 0, sizeof(t)); return (t == 0); } /* For debugging purposes */ /* SEXP memsetZeroable() { SEXP ans; PROTECT(ans = allocVector(LGLSXP, 2)); LOGICAL(ans)[1] = memset_zero_ok_int(); LOGICAL(ans)[2] = memset_zero_ok_double(); UNPROTECT(1); return(ans); } */ void fillWithValue(SEXP ans, SEXP value) { R_xlen_t i, n; SEXPTYPE type; double *ans_ptr_d, value_d; int *ans_ptr_i, value_i; int *ans_ptr_l, value_l; /* Argument 'ans': */ if (!isVectorAtomic(ans)) { error("Argument 'ans' must be a vector."); } n = xlength(ans); /* Argument 'value': */ if (!isVectorAtomic(value) || xlength(value) != 1) { error("Argument 'value' must be a scalar."); } type = TYPEOF(value); switch (type) { case INTSXP: value_i = asInteger(value); ans_ptr_i = INTEGER(ans); if (value_i == 0 && memset_zero_ok_int()) { memset(ans_ptr_i, 0, n*sizeof(value_i)); } else { for (i=0; i < n; i++) ans_ptr_i[i] = value_i; } break; case REALSXP: value_d = asReal(value); ans_ptr_d = REAL(ans); if (value_d == 0 && memset_zero_ok_double()) { memset(ans_ptr_d, 0, n*sizeof(value_d)); } else { for (i=0; i < n; i++) ans_ptr_d[i] = value_d; } break; case LGLSXP: value_l = asLogical(value); ans_ptr_l = LOGICAL(ans); if (value_l == 0 && memset_zero_ok_int()) { memset(ans_ptr_l, 0, n*sizeof(value_l)); } else { for (i=0; i < n; i++) ans_ptr_l[i] = value_l; } break; default: error("Argument 'value' must be either of type integer, numeric or logical."); break; } } /* fillWithValue() */ SEXP allocVector2(SEXP length, SEXP value) { SEXP ans; SEXPTYPE type; R_xlen_t n = 0; /* Argument 'length': */ if (isInteger(length) && xlength(length) == 1) { n = (R_xlen_t)asInteger(length); } else if (isReal(length) && xlength(length) == 1) { n = (R_xlen_t)asReal(length); } else { error("Argument 'length' must be a single numeric."); } if (n < 0) error("Argument 'length' is negative."); /* Argument 'value': */ if (!isVectorAtomic(value) || xlength(value) != 1) { error("Argument 'value' must be a scalar."); } type = TYPEOF(value); PROTECT(ans = allocVector(type, n)); fillWithValue(ans, value); UNPROTECT(1); return(ans); } /* allocVector2() */ SEXP allocMatrix2(SEXP nrow, SEXP ncol, SEXP value) { SEXP ans; SEXPTYPE type; int nc, nr; /* Argument 'nrow' & 'ncol': */ if (!isInteger(nrow) || xlength(nrow) != 1) { error("Argument 'nrow' must be a single integer."); } if (!isInteger(ncol) || xlength(ncol) != 1) { error("Argument 'ncol' must be a single integer."); } nr = asInteger(nrow); nc = asInteger(ncol); if (nr < 0) error("Argument 'nrow' is negative."); if (nr < 0) error("Argument 'ncol' is negative."); /* Argument 'value': */ if (!isVectorAtomic(value) || xlength(value) != 1) { error("Argument 'value' must be a scalar."); } type = TYPEOF(value); PROTECT(ans = allocMatrix(type, nr, nc)); fillWithValue(ans, value); UNPROTECT(1); return(ans); } /* allocMatrix2() */ SEXP allocArray2(SEXP dim, SEXP value) { SEXP ans; SEXPTYPE type; int i, d; double nd = 1.0; R_xlen_t n; /* Argument 'dim': */ if (!isInteger(dim) || xlength(dim) == 0) { error("Argument 'dim' must be an integer vector of at least length one."); } for (i = 0; i < xlength(dim); i++) { d = INTEGER(dim)[i]; nd *= d; #ifndef LONG_VECTOR_SUPPORT if (nd > R_INT_MAX) { error("Argument 'dim' specifies too many elements: %.g > %d", nd, R_INT_MAX); } #endif } n = (R_xlen_t)nd; /* Argument 'value': */ if (!isVectorAtomic(value) || xlength(value) != 1) { error("Argument 'value' must be a scalar."); } type = TYPEOF(value); PROTECT(dim = duplicate(dim)); PROTECT(ans = allocVector(type, n)); fillWithValue(ans, value); setAttrib(ans, R_DimSymbol, dim); UNPROTECT(2); return(ans); } /* allocArray2() */ matrixStats/src/rowDiffs.c0000644000175100001440000000524612776336765015366 0ustar hornikusers/*************************************************************************** Public methods: SEXP rowDiffs(SEXP x, ...) Authors: Henrik Bengtsson Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD rowDiffs #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int byrow, R_xlen_t lag, R_xlen_t differences, X_C_TYPE *ans, R_xlen_t nrow_ans, R_xlen_t ncol_ans #define X_TYPE 'i' #include "templates-gen-matrix.h" #define X_TYPE 'r' #include "templates-gen-matrix.h" SEXP rowDiffs(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP lag, SEXP differences, SEXP byRow) { int byrow; SEXP ans = NILSXP; R_xlen_t lagg, diff; R_xlen_t nrow, ncol; R_xlen_t nrow_ans, ncol_ans; /* Argument 'x' and 'dim': */ assertArgMatrix(x, dim, (R_TYPE_INT | R_TYPE_REAL), "x"); nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'lag': */ lagg = asInteger(lag); if (lagg < 1) { error("Argument 'lag' must be a positive integer."); } /* Argument 'differences': */ diff = asInteger(differences); if (diff < 1) { error("Argument 'differences' must be a positive integer."); } /* Argument 'rows' and 'cols': */ R_xlen_t nrows, ncols; int rowsType, colsType; void *crows = validateIndices(rows, nrow, 0, &nrows, &rowsType); void *ccols = validateIndices(cols, ncol, 0, &ncols, &colsType); /* Argument 'byRow': */ byrow = asLogical(byRow); /* Dimension of result matrix */ if (byrow) { nrow_ans = nrows; ncol_ans = (R_xlen_t)((double)ncols - ((double)diff*(double)lagg)); if (ncol_ans < 0) ncol_ans = 0; } else { nrow_ans = (R_xlen_t)((double)nrows - ((double)diff*(double)lagg)); if (nrow_ans < 0) nrow_ans = 0; ncol_ans = ncols; } if (isReal(x)) { PROTECT(ans = allocMatrix(REALSXP, nrow_ans, ncol_ans)); rowDiffs_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, byrow, lagg, diff, REAL(ans), nrow_ans, ncol_ans); UNPROTECT(1); } else if (isInteger(x)) { PROTECT(ans = allocMatrix(INTSXP, nrow_ans, ncol_ans)); rowDiffs_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, byrow, lagg, diff, INTEGER(ans), nrow_ans, ncol_ans); UNPROTECT(1); } return(ans); } /* rowDiffs() */ /*************************************************************************** HISTORY: 2015-06-13 [DJ] o Supported subsetted computation. 2014-12-29 [HB] o Created. **************************************************************************/ matrixStats/src/rowVars_TYPE-template.h0000644000175100001440000000607212776336765017723 0ustar hornikusers/*********************************************************************** TEMPLATE: void rowVars_(ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int narm, int hasna, int byrow, double *ans Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD: the name of the resulting function - X_TYPE: 'i' or 'r' Authors: Adopted from rowQuantiles.c by R. Gentleman. Template by Henrik Bengtsson. Copyright: Henrik Bengtsson, 2007-2013 ***********************************************************************/ #include #include #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, X_ISNAN, [METHOD_NAME]) */ #include "templates-types.h" RETURN_TYPE METHOD_NAME_ROWS_COLS(ARGUMENTS_LIST) { R_xlen_t ii, jj, kk, idx; R_xlen_t *colOffset; X_C_TYPE *values, value; double value_d, mu_d, sigma2_d; #ifdef ROWS_TYPE ROWS_C_TYPE *crows = (ROWS_C_TYPE*) rows; #endif #ifdef COLS_TYPE COLS_C_TYPE *ccols = (COLS_C_TYPE*) cols; #endif /* R allocate memory for the 'values'. This will be taken care of by the R garbage collector later on. */ values = (X_C_TYPE *) R_alloc(ncols, sizeof(X_C_TYPE)); /* If there are no missing values, don't try to remove them. */ if (hasna == FALSE) narm = FALSE; /* Pre-calculate the column offsets */ colOffset = (R_xlen_t *) R_alloc(ncols, sizeof(R_xlen_t)); if (byrow) { for (jj=0; jj < ncols; jj++) colOffset[jj] = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); } else { for (jj=0; jj < ncols; jj++) colOffset[jj] = COL_INDEX(ccols,jj); } for (ii=0; ii < nrows; ii++) { R_xlen_t rowIdx = byrow ? ROW_INDEX(crows,ii) : R_INDEX_OP(ROW_INDEX(crows,ii), *, ncol); //HJ kk = 0; for (jj=0; jj < ncols; jj++) { idx = R_INDEX_OP(rowIdx, +, colOffset[jj]); value = R_INDEX_GET(x, idx, X_NA); //HJ if (X_ISNAN(value)) { if (narm == FALSE) { kk = -1; break; } } else { values[kk] = value; kk = kk + 1; } } /* for (jj ...) */ /* Note that 'values' will never contain NA/NaNs */ if (kk <= 1) { ans[ii] = NA_REAL; } else { /* (a) Calculate mu = sum(x)/length(x) */ mu_d = 0; for (jj=0; jj < kk; jj++) { mu_d += (double)values[jj]; } mu_d /= (double)kk; /* (b) Calculate sigma^2 */ sigma2_d = 0; for (jj=0; jj < kk; jj++) { value_d = ((double)values[jj] - mu_d); value_d *= value_d; sigma2_d += value_d; } sigma2_d /= (double)(kk-1); ans[ii] = sigma2_d; } /* if (kk <= 1) */ R_CHECK_USER_INTERRUPT(ii); } /* for (ii ...) */ } /*************************************************************************** HISTORY: 2015-06-13 [DJ] o Supported subsetted computation. 2014-11-18 [HB] o Created from rowMads_TYPE-template.h. **************************************************************************/ matrixStats/src/rowVars.c0000644000175100001440000000453512776336765015246 0ustar hornikusers/*************************************************************************** Public methods: SEXP rowVars(SEXP x, SEXP naRm, SEXP hasNA) SEXP colVars(SEXP x, SEXP naRm, SEXP hasNA) Authors: Henrik Bengtsson Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD rowVars #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int narm, int hasna, int byrow, double *ans #define X_TYPE 'i' #include "templates-gen-matrix.h" #define X_TYPE 'r' #include "templates-gen-matrix.h" #undef METHOD SEXP rowVars(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP naRm, SEXP hasNA, SEXP byRow) { int narm, hasna, byrow; SEXP ans; R_xlen_t nrow, ncol; /* Argument 'x' and 'dim': */ assertArgMatrix(x, dim, (R_TYPE_INT | R_TYPE_REAL), "x"); nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'naRm': */ narm = asLogicalNoNA(naRm, "na.rm"); /* Argument 'hasNA': */ hasna = asLogicalNoNA(hasNA, "hasNA"); /* Argument 'rows' and 'cols': */ R_xlen_t nrows, ncols; int rowsType, colsType; void *crows = validateIndices(rows, nrow, 0, &nrows, &rowsType); void *ccols = validateIndices(cols, ncol, 0, &ncols, &colsType); /* Argument 'byRow': */ byrow = asLogical(byRow); if (!byrow) { SWAP(R_xlen_t, nrow, ncol); SWAP(void*, crows, ccols); SWAP(R_xlen_t, nrows, ncols); SWAP(int, rowsType, colsType); } /* R allocate a double vector of length 'nrow' Note that 'nrow' means 'ncol' if byrow=FALSE. */ PROTECT(ans = allocVector(REALSXP, nrows)); /* Double matrices are more common to use. */ if (isReal(x)) { rowVars_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, narm, hasna, byrow, REAL(ans)); } else if (isInteger(x)) { rowVars_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, narm, hasna, byrow, REAL(ans)); } UNPROTECT(1); return(ans); } /* rowVars() */ /*************************************************************************** HISTORY: 2015-06-13 [DJ] o Supported subsetted computation. 2014-11-18 [HB] o Created from rowMads.c. **************************************************************************/ matrixStats/src/psortKM.c0000644000175100001440000000534512776336765015202 0ustar hornikusers/*************************************************************************** Public methods: SEXP psortKM(SEXP x, SEXP k, SEXP nk) Arguments: x: numeric vector k: integer scalar in [1,length(x)] m: integer scalar in [1,k] and not too large if k is large. Authors: Henrik Bengtsson Copyright Henrik Bengtsson, 2012 **************************************************************************/ #include #include #include "types.h" #include "utils.h" void psortKM_C(double *x, R_xlen_t nx, R_xlen_t k, R_xlen_t m, double *ans) { R_xlen_t ii, ll; double *xx; /* R allocate memory for the 'xx'. This will be taken care of by the R garbage collector later on. */ xx = (double *) R_alloc(nx, sizeof(double)); /* Create a local copy 'xx' of 'x'. */ for (ii=0; ii < nx; ii++) { xx[ii] = x[ii]; } /* Permute xx[0:partial] so that xx[partial+1] is in the correct place with smaller values to the left, ... Example: psortKM(x, k=50, m=2) with length(x) = 1000 rPsort(xx, 1000, 50); We know x[50] and that x[1:49] <= x[50] rPsort(xx, 50, 49); x[49] and that x[1:48] <= x[49] rPsort(xx, 49, 48); x[48] and that x[1:47] <= x[48] */ ll = nx; for (ii=0; ii < m; ii++) { rPsort(xx, ll, k-1-ii); ll = (k-1)-ii; } for (ii=0; ii < m; ii++) { ans[ii] = xx[(k-m)+ii]; } } /* psortKM_C() */ SEXP psortKM(SEXP x, SEXP k, SEXP m) { SEXP ans; R_xlen_t nx, kk, mm; /* Argument 'x': */ assertArgVector(x, (R_TYPE_REAL), "x"); nx = xlength(x); if (nx == 0) { error("Argument 'x' must not be empty."); } /* Argument 'k': */ if (!isInteger(k)) { error("Argument 'k' must be an integer."); } if (length(k) != 1) { error("Argument 'k' must be a single integer."); } kk = asInteger(k); if (kk <= 0) { error("Argument 'k' must be a positive integer."); } if (kk > nx) { error("Argument 'k' must not be greater than number of elements in 'x'."); } /* Argument 'm': */ if (!isInteger(m)) { error("Argument 'm' must be an integer."); } if (length(m) != 1) { error("Argument 'm' must be a single integer."); } mm = asInteger(m); if (mm <= 0) { error("Argument 'm' must be a positive integer."); } else if (mm > kk) { error("Argument 'm' must not be greater than argument 'k'."); } /* R allocate a double vector of length 'partial' */ PROTECT(ans = allocVector(REALSXP, mm)); psortKM_C(REAL(x), nx, kk, mm, REAL(ans)); UNPROTECT(1); return(ans); } /* psortKM() */ /*************************************************************************** HISTORY: 2012-09-10 [HB] o Added psortKM(). o Created. **************************************************************************/ matrixStats/src/validateIndices.c0000644000175100001440000002030512776336765016664 0ustar hornikusers/*************************************************************************** Public methods: SEXP validate(SEXP idxs, SEXP maxIdx, SEXP allowOutOfBound) **************************************************************************/ #include #include "utils.h" #define METHOD validateIndices #define RETURN_VALIDATED_ANS(type, n, cond, item, poststmt) \ type *ans = (type*) R_alloc(count, sizeof(type)); \ jj = 0; \ for (ii = 0; ii < n; ++ ii) { \ if (cond) ans[jj ++] = item; \ } \ poststmt \ return ans #define FILL_VALIDATED_ANS(n, cond, item) \ jj = 0; \ for (ii = 0; ii < n; ++ ii) { \ if (cond) ans[jj ++] = item; \ } #define X_TYPE 'i' #define SUBSETTED_DEFAULT SUBSETTED_INTEGER #include "validateIndices_TYPE-template.h" #undef SUBSETTED_DEFAULT #define X_TYPE 'r' #define SUBSETTED_DEFAULT SUBSETTED_REAL #include "validateIndices_TYPE-template.h" #undef SUBSETTED_DEFAULT /** idxs must not be NULL, which should be checked before calling this function. **/ void* validateIndices_Logical(int *idxs, R_xlen_t nidxs, R_xlen_t maxIdx, int allowOutOfBound, R_xlen_t *ansNidxs, int *subsettedType, int *hasna) { R_xlen_t ii, jj, kk; R_xlen_t count1 = 0, count2 = 0; // set default as no NA. *hasna = FALSE; // set default type as SUBSETTED_INTEGER *subsettedType = SUBSETTED_INTEGER; if (nidxs == 0) { *ansNidxs = 0; return NULL; } if (nidxs > maxIdx) { if (!allowOutOfBound) { error("logical subscript too long"); } *hasna = TRUE; // out-of-bound index is NA // count how many idx items for (ii = 0; ii < maxIdx; ++ ii) { if (idxs[ii]) { // TRUE or NA ++ count1; if (ii + 1 > R_INT_MAX) *subsettedType = SUBSETTED_REAL; } } for (; ii < nidxs; ++ ii) { if (idxs[ii]) { // TRUE or NA ++ count2; } } *ansNidxs = count1 + count2; if (*subsettedType == SUBSETTED_INTEGER) { int *ans = (int*) R_alloc(*ansNidxs, sizeof(int)); FILL_VALIDATED_ANS(maxIdx, idxs[ii], idxs[ii] == NA_LOGICAL ? NA_INTEGER : ii + 1); for (ii = count1; ii < *ansNidxs; ++ ii) { ans[ii] = NA_INTEGER; } return ans; } // *subsettedType == SUBSETTED_REAL double *ans = (double*) R_alloc(*ansNidxs, sizeof(double)); FILL_VALIDATED_ANS(maxIdx, idxs[ii], idxs[ii] == NA_LOGICAL ? NA_REAL : ii + 1); for (ii = count1; ii < *ansNidxs; ++ ii) { ans[ii] = NA_REAL; } return ans; } // nidxs <= maxIdx R_xlen_t naCount = 0; R_xlen_t lastIndex = 0; R_xlen_t lastPartNum = maxIdx % nidxs; for (ii = 0; ii < lastPartNum; ++ ii) { if (idxs[ii]) { // TRUE or NA if (idxs[ii] == NA_LOGICAL) ++ naCount; else lastIndex = ii + 1; ++ count1; } } if (lastIndex > 0 && maxIdx - lastPartNum + lastIndex > R_INT_MAX) *subsettedType = SUBSETTED_REAL; lastIndex = 0; for (; ii < nidxs; ++ ii) { if (idxs[ii]) { // TRUE or NA if (idxs[ii] == NA_LOGICAL) ++ naCount; else lastIndex = ii + 1; ++ count2; } } R_xlen_t count = count1 + count2; if (lastIndex > 0 && maxIdx - lastPartNum - count + lastIndex > R_INT_MAX) *subsettedType = SUBSETTED_REAL; if (naCount == 0 && count == nidxs) { // All True *ansNidxs = maxIdx; *subsettedType = SUBSETTED_ALL; return NULL; } if (naCount) *hasna = TRUE; *ansNidxs = maxIdx / nidxs * count + count1; if (*subsettedType == SUBSETTED_INTEGER) { int *ans = (int*) R_alloc(*ansNidxs, sizeof(int)); FILL_VALIDATED_ANS(nidxs, idxs[ii], idxs[ii] == NA_LOGICAL ? NA_INTEGER : ii + 1); for (ii = count, kk = nidxs; kk+nidxs <= maxIdx; kk += nidxs, ii += count) { for (jj = 0; jj < count; ++ jj) { ans[ii+jj] = ans[jj] == NA_INTEGER ? NA_INTEGER : ans[jj] + kk; } } for (jj = 0; jj < count1; ++ jj) { ans[ii+jj] = ans[jj] == NA_INTEGER ? NA_INTEGER : ans[jj] + kk; } return ans; } // *subsettedType == SUBSETTED_REAL double *ans = (double*) R_alloc(*ansNidxs, sizeof(double)); FILL_VALIDATED_ANS(nidxs, idxs[ii], idxs[ii] == NA_LOGICAL ? NA_REAL : ii + 1); for (ii = count, kk = nidxs; kk+nidxs <= maxIdx; kk += nidxs, ii += count) { for (jj = 0; jj < count; ++ jj) { ans[ii+jj] = ISNAN(ans[jj]) ? NA_REAL : ans[jj] + kk; } } for (jj = 0; jj < count1; ++ jj) { ans[ii+jj] = ISNAN(ans[jj]) ? NA_REAL : ans[jj] + kk; } return ans; } /************************************************************* * The most important function which is widely called. * If `idxs` is NULL, NULL will be returned, which indicates selecting. * the whole to-be-computed vector(matrix). * `maxIdx` is the to-be-computed vector(matrix)'s length (rows/cols). * `allowOutOfBound` indicates whether to allow positve out of bound indexing. * `ansNidxs` is used for returning the new idxs array's length. * `subsettedType` is used for returning the new idxs array's datatype. * `hasna` is TRUE, if NA is included in returned result. ************************************************************/ void *validateIndices(SEXP idxs, R_xlen_t maxIdx, int allowOutOfBound, R_xlen_t *ansNidxs, int *subsettedType) { int hasna; return validateIndicesCheckNA(idxs, maxIdx, allowOutOfBound, ansNidxs, subsettedType, &hasna); } void *validateIndicesCheckNA(SEXP idxs, R_xlen_t maxIdx, int allowOutOfBound, R_xlen_t *ansNidxs, int *subsettedType, int *hasna) { R_xlen_t nidxs = xlength(idxs); int mode = TYPEOF(idxs); // Set no NA as default. *hasna = FALSE; switch (mode) { case INTSXP: return validateIndices_Integer(INTEGER(idxs), nidxs, maxIdx, allowOutOfBound, ansNidxs, subsettedType, hasna); case REALSXP: return validateIndices_Real(REAL(idxs), nidxs, maxIdx, allowOutOfBound, ansNidxs, subsettedType, hasna); case LGLSXP: return validateIndices_Logical(LOGICAL(idxs), nidxs, maxIdx, allowOutOfBound, ansNidxs, subsettedType, hasna); case NILSXP: *subsettedType = SUBSETTED_ALL; *ansNidxs = maxIdx; return NULL; default: error("idxs can only be integer, numeric, or logical."); } return NULL; // useless sentence. won't be executed. } /************************************************************* * This function can be called by R. * If `idxs` is NULL, NULL will be returned, which indicates selecting. * the whole to-be-computed vector(matrix). * `maxIdx` is the to-be-computed vector(matrix)'s length (rows/cols). * `allowOutOfBound` indicates whether to allow positve out of bound indexing. ************************************************************/ SEXP validate(SEXP idxs, SEXP maxIdx, SEXP allowOutOfBound) { SEXP ans; R_xlen_t ansNidxs; int subsettedType; R_xlen_t cmaxIdx = asR_xlen_t(maxIdx, 0); R_xlen_t nidxs = xlength(idxs); int callowOutOfBound = asLogicalNoNA(allowOutOfBound, "allowOutOfBound"); void *cidxs; // Set no NA as default. int hasna = FALSE; int mode = TYPEOF(idxs); switch (mode) { case INTSXP: cidxs = validateIndices_Integer(INTEGER(idxs), nidxs, cmaxIdx, callowOutOfBound, &ansNidxs, &subsettedType, &hasna); break; case REALSXP: cidxs = validateIndices_Real(REAL(idxs), nidxs, cmaxIdx, callowOutOfBound, &ansNidxs, &subsettedType, &hasna); break; case LGLSXP: cidxs = validateIndices_Logical(LOGICAL(idxs), nidxs, cmaxIdx, callowOutOfBound, &ansNidxs, &subsettedType, &hasna); break; case NILSXP: return R_NilValue; default: error("idxs can only be integer, numeric, or logical."); } if (subsettedType == SUBSETTED_ALL) { return R_NilValue; } if (subsettedType == SUBSETTED_INTEGER) { ans = PROTECT(allocVector(INTSXP, ansNidxs)); if (cidxs && ansNidxs > 0) { memcpy(INTEGER(ans), cidxs, ansNidxs*sizeof(int)); } UNPROTECT(1); return ans; } // else: subsettedType == SUBSETTED_REAL ans = PROTECT(allocVector(REALSXP, ansNidxs)); if (cidxs && ansNidxs > 0) { memcpy(REAL(ans), cidxs, ansNidxs*sizeof(double)); } UNPROTECT(1); return ans; } matrixStats/src/templates-gen-matrix-vector.h0000644000175100001440000000560012776336765021151 0ustar hornikusers#include "macros.h" #include METHOD_TEMPLATE_H #define COLS_TYPE 'i' #include METHOD_TEMPLATE_H #undef COLS_TYPE #define COLS_TYPE 'r' #include METHOD_TEMPLATE_H #undef COLS_TYPE #define ROWS_TYPE 'i' #include METHOD_TEMPLATE_H #define COLS_TYPE 'i' #include METHOD_TEMPLATE_H #undef COLS_TYPE #define COLS_TYPE 'r' #include METHOD_TEMPLATE_H #undef COLS_TYPE #undef ROWS_TYPE #define ROWS_TYPE 'r' #include METHOD_TEMPLATE_H #define COLS_TYPE 'i' #include METHOD_TEMPLATE_H #undef COLS_TYPE #define COLS_TYPE 'r' #include METHOD_TEMPLATE_H #undef COLS_TYPE #undef ROWS_TYPE #define IDXS_TYPE 'i' #include METHOD_TEMPLATE_H #define COLS_TYPE 'i' #include METHOD_TEMPLATE_H #undef COLS_TYPE #define COLS_TYPE 'r' #include METHOD_TEMPLATE_H #undef COLS_TYPE #define ROWS_TYPE 'i' #include METHOD_TEMPLATE_H #define COLS_TYPE 'i' #include METHOD_TEMPLATE_H #undef COLS_TYPE #define COLS_TYPE 'r' #include METHOD_TEMPLATE_H #undef COLS_TYPE #undef ROWS_TYPE #define ROWS_TYPE 'r' #include METHOD_TEMPLATE_H #define COLS_TYPE 'i' #include METHOD_TEMPLATE_H #undef COLS_TYPE #define COLS_TYPE 'r' #include METHOD_TEMPLATE_H #undef COLS_TYPE #undef ROWS_TYPE #undef IDXS_TYPE #define IDXS_TYPE 'r' #include METHOD_TEMPLATE_H #define COLS_TYPE 'i' #include METHOD_TEMPLATE_H #undef COLS_TYPE #define COLS_TYPE 'r' #include METHOD_TEMPLATE_H #undef COLS_TYPE #define ROWS_TYPE 'i' #include METHOD_TEMPLATE_H #define COLS_TYPE 'i' #include METHOD_TEMPLATE_H #undef COLS_TYPE #define COLS_TYPE 'r' #include METHOD_TEMPLATE_H #undef COLS_TYPE #undef ROWS_TYPE #define ROWS_TYPE 'r' #include METHOD_TEMPLATE_H #define COLS_TYPE 'i' #include METHOD_TEMPLATE_H #undef COLS_TYPE #define COLS_TYPE 'r' #include METHOD_TEMPLATE_H #undef COLS_TYPE #undef ROWS_TYPE #undef IDXS_TYPE RETURN_TYPE (*METHOD_NAME[3][3][3])(ARGUMENTS_LIST) = {{ {METHOD_NAME_noRows_noCols_noIdxs, METHOD_NAME_noRows_noCols_intIdxs, METHOD_NAME_noRows_noCols_realIdxs}, {METHOD_NAME_noRows_intCols_noIdxs, METHOD_NAME_noRows_intCols_intIdxs, METHOD_NAME_noRows_intCols_realIdxs}, {METHOD_NAME_noRows_realCols_noIdxs, METHOD_NAME_noRows_realCols_intIdxs, METHOD_NAME_noRows_realCols_realIdxs}, }, { {METHOD_NAME_intRows_noCols_noIdxs, METHOD_NAME_intRows_noCols_intIdxs, METHOD_NAME_intRows_noCols_realIdxs}, {METHOD_NAME_intRows_intCols_noIdxs, METHOD_NAME_intRows_intCols_intIdxs, METHOD_NAME_intRows_intCols_realIdxs}, {METHOD_NAME_intRows_realCols_noIdxs, METHOD_NAME_intRows_realCols_intIdxs, METHOD_NAME_intRows_realCols_realIdxs}, }, { {METHOD_NAME_realRows_noCols_noIdxs, METHOD_NAME_realRows_noCols_intIdxs, METHOD_NAME_realRows_noCols_realIdxs}, {METHOD_NAME_realRows_intCols_noIdxs, METHOD_NAME_realRows_intCols_intIdxs, METHOD_NAME_realRows_intCols_realIdxs}, {METHOD_NAME_realRows_realCols_noIdxs, METHOD_NAME_realRows_realCols_intIdxs, METHOD_NAME_realRows_realCols_realIdxs}, } }; #include "templates-types_undef.h" matrixStats/src/rowMedians.c0000644000175100001440000000647412776336765015717 0ustar hornikusers/*************************************************************************** Public methods: SEXP rowMedians(SEXP x, ...) Authors: Adopted from rowQuantiles.c by R. Gentleman. Copyright Henrik Bengtsson, 2007 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD rowMedians #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int narm, int hasna, int byrow, double *ans #define X_TYPE 'i' #include "templates-gen-matrix.h" #define X_TYPE 'r' #include "templates-gen-matrix.h" SEXP rowMedians(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP naRm, SEXP hasNA, SEXP byRow) { int narm, hasna, byrow; SEXP ans; R_xlen_t nrow, ncol; /* Argument 'x' and 'dim': */ assertArgMatrix(x, dim, (R_TYPE_INT | R_TYPE_REAL), "x"); /* Get dimensions of 'x'. */ nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'naRm': */ narm = asLogicalNoNA(naRm, "na.rm"); /* Argument 'hasNA': */ hasna = asLogicalNoNA(hasNA, "hasNA"); /* Argument 'rows' and 'cols': */ R_xlen_t nrows, ncols; int rowsType, colsType; void *crows = validateIndices(rows, nrow, 0, &nrows, &rowsType); void *ccols = validateIndices(cols, ncol, 0, &ncols, &colsType); /* Argument 'byRow': */ byrow = asLogical(byRow); if (!byrow) { SWAP(R_xlen_t, nrow, ncol); SWAP(void*, crows, ccols); SWAP(R_xlen_t, nrows, ncols); SWAP(int, rowsType, colsType); } /* R allocate a double vector of length 'nrows' Note that 'nrows' means 'ncols' if byrow=FALSE. */ PROTECT(ans = allocVector(REALSXP, nrows)); /* Double matrices are more common to use. */ if (isReal(x)) { rowMedians_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, narm, hasna, byrow, REAL(ans)); } else if (isInteger(x)) { rowMedians_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, narm, hasna, byrow, REAL(ans)); } UNPROTECT(1); return(ans); } /* rowMedians() */ /*************************************************************************** HISTORY: 2015-06-07 [DJ] o Supported subsetted computation. 2013-01-13 [HB] o Added argument 'byRow' to rowMedians() and dropped colMedians(). o Using internal arguments 'by_row' instead of 'by_column'. 2011-12-11 [HB] o BUG FIX: rowMediansReal(..., na.rm=TRUE) did not handle NaN:s, only NA:s. Note that NaN:s does not exist for integers. 2011-10-12 [HJ] o Added colMedians(). o Now rowMediansInteger/Real() can operate also by columns, cf. argument 'by_column'. 2007-08-14 [HB] o Added checks for user interrupts every 1000 line. o Added argument 'hasNA' to rowMedians(). 2005-12-07 [HB] o BUG FIX: When calculating the median of an even number (non-NA) values, the length of the second sort was one element too short, which made the method to freeze, i.e. rPsort(rowData, qq, qq) is now (...qq+1, qq). 2005-11-24 [HB] o By implementing a special version for integers, there is no need to coerce to double in R, which would take up twice the amount of memory. o rowMedians() now handles NAs too. o Adopted from rowQuantiles.c in Biobase of Bioconductor. **************************************************************************/ matrixStats/src/rowLogSumExp.c0000644000175100001440000000442512776336765016214 0ustar hornikusers/*************************************************************************** Public methods: SEXP rowLogSumExps(SEXP lx, SEXP dim, SEXP rows, SEXP cols, SEXP naRm, SEXP hasNA, SEXP byRow) Authors: Henrik Bengtsson Copyright Henrik Bengtsson, 2013-2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD rowLogSumExp #define METHOD_NAME rowLogSumExps_double #define RETURN_TYPE void #define ARGUMENTS_LIST double *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, int rowsType, void *cols, R_xlen_t ncols, int colsType, int narm, int hasna, R_xlen_t byrow, double *ans #include "templates-gen-vector.h" SEXP rowLogSumExps(SEXP lx, SEXP dim, SEXP rows, SEXP cols, SEXP naRm, SEXP hasNA, SEXP byRow) { SEXP ans; int narm, hasna, byrow; R_xlen_t nrow, ncol; /* Argument 'lx' and 'dim': */ assertArgMatrix(lx, dim, (R_TYPE_REAL), "lx"); nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'naRm': */ narm = asLogicalNoNA(naRm, "na.rm"); /* Argument 'hasNA': */ hasna = asLogicalNoNA(hasNA, "hasNA"); /* Argument 'rows' and 'cols': */ R_xlen_t nrows, ncols; int rowsType, colsType; void *crows = validateIndices(rows, nrow, 0, &nrows, &rowsType); void *ccols = validateIndices(cols, ncol, 0, &ncols, &colsType); /* Argument 'byRow': */ byrow = asLogical(byRow); if (byrow) { ans = PROTECT(allocVector(REALSXP, nrows)); rowLogSumExps_double[rowsType](REAL(lx), nrow, ncol, crows, nrows, rowsType, ccols, ncols, colsType, narm, hasna, 1, REAL(ans)); } else { ans = PROTECT(allocVector(REALSXP, ncols)); rowLogSumExps_double[colsType](REAL(lx), nrow, ncol, crows, nrows, rowsType, ccols, ncols, colsType, narm, hasna, 0, REAL(ans)); } UNPROTECT(1); /* ans = PROTECT(...) */ return(ans); } /* rowLogSumExps() */ /*************************************************************************** HISTORY: 2015-06-12 [DJ] o Supported subsetted computation. 2013-05-02 [HB] o BUG FIX: Incorrectly used ISNAN() on an int variable as caught by the 'cc' compiler on Solaris. Reported by Brian Ripley upon CRAN submission. 2013-04-30 [HB] o Created. **************************************************************************/ matrixStats/src/colCounts.c0000644000175100001440000000777112776336765015561 0ustar hornikusers/*************************************************************************** Public methods: SEXP colCounts(SEXP x, ...) Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD colCounts #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, X_C_TYPE value, int what, int narm, int hasna, int *ans #define X_TYPE 'i' #include "templates-gen-matrix.h" #define X_TYPE 'r' #include "templates-gen-matrix.h" #define X_TYPE 'l' #include "templates-gen-matrix.h" SEXP colCounts(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP value, SEXP what, SEXP naRm, SEXP hasNA) { SEXP ans; int narm, hasna, what2; R_xlen_t nrow, ncol; /* Argument 'x' and 'dim': */ assertArgMatrix(x, dim, (R_TYPE_LGL | R_TYPE_INT | R_TYPE_REAL), "x"); nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'value': */ if (length(value) != 1) error("Argument 'value' must be a single value."); if (!isNumeric(value)) error("Argument 'value' must be a numeric value."); /* Argument 'what': */ what2 = asInteger(what); if (what2 < 0 || what2 > 2) error("INTERNAL ERROR: Unknown value of 'what' for rowCounts: %d", what2); /* Argument 'naRm': */ narm = asLogicalNoNA(naRm, "na.rm"); /* Argument 'hasNA': */ hasna = asLogicalNoNA(hasNA, "hasNA"); /* Argument 'rows' and 'cols': */ R_xlen_t nrows, ncols; int rowsType, colsType; void *crows = validateIndices(rows, nrow, 0, &nrows, &rowsType); void *ccols = validateIndices(cols, ncol, 0, &ncols, &colsType); /* R allocate an integer vector of length 'ncol' */ PROTECT(ans = allocVector(INTSXP, ncols)); if (isReal(x)) { colCounts_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, asReal(value), what2, narm, hasna, INTEGER(ans)); } else if (isInteger(x)) { colCounts_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, asInteger(value), what2, narm, hasna, INTEGER(ans)); } else if (isLogical(x)) { colCounts_Logical[rowsType][colsType](LOGICAL(x), nrow, ncol, crows, nrows, ccols, ncols, asLogical(value), what2, narm, hasna, INTEGER(ans)); } UNPROTECT(1); return(ans); } // colCounts() SEXP count(SEXP x, SEXP idxs, SEXP value, SEXP what, SEXP naRm, SEXP hasNA) { SEXP ans; int narm, hasna, what2; R_xlen_t nx; /* Argument 'x' and 'dim': */ assertArgVector(x, (R_TYPE_LGL | R_TYPE_INT | R_TYPE_REAL), "x"); nx = xlength(x); /* Argument 'value': */ if (length(value) != 1) error("Argument 'value' must be a single value."); if (!isNumeric(value)) error("Argument 'value' must be a numeric value."); /* Argument 'what': */ what2 = asInteger(what); /* Argument 'naRm': */ narm = asLogicalNoNA(naRm, "na.rm"); /* Argument 'hasNA': */ hasna = asLogicalNoNA(hasNA, "hasNA"); /* Argument 'idxs': */ R_xlen_t nrows, ncols = 1; int rowsType, colsType = SUBSETTED_ALL; void *crows = validateIndices(idxs, nx, 1, &nrows, &rowsType); void *ccols = NULL; /* R allocate a integer scalar */ PROTECT(ans = allocVector(INTSXP, 1)); if (isReal(x)) { colCounts_Real[rowsType][colsType](REAL(x), nx, 1, crows, nrows, ccols, ncols, asReal(value), what2, narm, hasna, INTEGER(ans)); } else if (isInteger(x)) { colCounts_Integer[rowsType][colsType](INTEGER(x), nx, 1, crows, nrows, ccols, ncols, asInteger(value), what2, narm, hasna, INTEGER(ans)); } else if (isLogical(x)) { colCounts_Logical[rowsType][colsType](LOGICAL(x), nx, 1, crows, nrows, ccols, ncols, asLogical(value), what2, narm, hasna, INTEGER(ans)); } UNPROTECT(1); return(ans); } // count() /*************************************************************************** HISTORY: 2015-04-21 [DJ] o Supported subsetted computation. 2014-11-14 [HB] o Created from rowCounts.c. **************************************************************************/ matrixStats/src/rowLogSumExp_TYPE-template.h0000644000175100001440000000377312776336765020700 0ustar hornikusers/*********************************************************************** TEMPLATE: double rowLogSumExp_double[idxsType](ARGUMENTS_LIST) ARGUMENTS_LIST: double *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, int rowsType, void *cols, R_xlen_t ncols, int colsType, int narm, int hasna, R_xlen_t byrow, double *ans ***********************************************************************/ #include "types.h" #include "templates-types.h" /* extern 1-D function 'logSumExp' */ extern double (*logSumExp_double[3])(double *x, void *idxs, R_xlen_t nidxs, int narm, int hasna, int by, double *xx); RETURN_TYPE METHOD_NAME_IDXS(ARGUMENTS_LIST) { R_xlen_t ii, idx; double navalue; double (*logsumexp)(double *x, void *idxs, R_xlen_t nidxs, int narm, int hasna, int by, double *xx); #ifdef IDXS_TYPE IDXS_C_TYPE *crows = (IDXS_C_TYPE*) rows; IDXS_C_TYPE *ccols = (IDXS_C_TYPE*) cols; #endif if (byrow) { /* R allocate memory for row-vector 'xx' of length 'ncol'. This will be taken care of by the R garbage collector later on. */ double *xx = (double *) R_alloc(ncols, sizeof(double)); navalue = (narm || ncols == 0) ? R_NegInf : NA_REAL; logsumexp = logSumExp_double[colsType]; for (ii=0; ii < nrows; ++ii) { idx = IDX_INDEX(crows,ii); if (idx == NA_R_XLEN_T) { ans[ii] = navalue; } else { ans[ii] = logsumexp(x+idx, cols, ncols, narm, hasna, nrow, xx); } } } else { navalue = (narm || nrows == 0) ? R_NegInf : NA_REAL; logsumexp = logSumExp_double[rowsType]; for (ii=0; ii < ncols; ++ii) { idx = R_INDEX_OP(IDX_INDEX(ccols,ii), *, nrow); if (idx == NA_R_XLEN_T) { ans[ii] = navalue; } else { ans[ii] = logsumexp(x+idx, rows, nrows, narm, hasna, 0, NULL); } } } /* if (byrow) */ } /*************************************************************************** HISTORY: 2013-06-12 [DH] o Created. **************************************************************************/ matrixStats/src/rowMedians_TYPE-template.h0000644000175100001440000001362412776336765020371 0ustar hornikusers/*********************************************************************** TEMPLATE: void rowMedians_[rowsType][colsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int narm, int hasna, int byrow, double *ans Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD: the name of the resulting function - X_TYPE: 'i' or 'r' Authors: Adopted from rowQuantiles.c by R. Gentleman. Template by Henrik Bengtsson. Copyright: Henrik Bengtsson, 2007-2013 ***********************************************************************/ #include #include #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, X_ISNAN, [METHOD_NAME]) */ #include "templates-types.h" RETURN_TYPE METHOD_NAME_ROWS_COLS(ARGUMENTS_LIST) { int isOdd; R_xlen_t ii, jj, kk, qq, idx; R_xlen_t *colOffset; X_C_TYPE *values, value; #ifdef ROWS_TYPE ROWS_C_TYPE *crows = (ROWS_C_TYPE*) rows; #endif #ifdef COLS_TYPE COLS_C_TYPE *ccols = (COLS_C_TYPE*) cols; #endif /* R allocate memory for the 'values'. This will be taken care of by the R garbage collector later on. */ values = (X_C_TYPE *) R_alloc(ncols, sizeof(X_C_TYPE)); /* If there are no missing values, don't try to remove them. */ if (hasna == FALSE) narm = FALSE; /* When narm == FALSE, isOdd and qq are the same for all rows */ if (narm == FALSE) { isOdd = (ncols % 2 == 1); qq = (R_xlen_t)(ncols/2) - 1; } else { isOdd = FALSE; qq = 0; } value = 0; /* Pre-calculate the column offsets */ colOffset = (R_xlen_t *) R_alloc(ncols, sizeof(R_xlen_t)); // HJ begin if (byrow) { for (jj=0; jj < ncols; jj++) colOffset[jj] = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); } else { for (jj=0; jj < ncols; jj++) colOffset[jj] = COL_INDEX(ccols,jj); } // HJ end if (hasna == TRUE) { for (ii=0; ii < nrows; ii++) { R_xlen_t rowIdx = byrow ? ROW_INDEX(crows,ii) : R_INDEX_OP(ROW_INDEX(crows,ii), *, ncol); //HJ kk = 0; /* The index of the last non-NA value detected */ for (jj=0; jj < ncols; jj++) { idx = R_INDEX_OP(rowIdx, +, colOffset[jj]); value = R_INDEX_GET(x, idx, X_NA); //HJ if (X_ISNAN(value)) { if (narm == FALSE) { kk = -1; break; } } else { values[kk] = value; kk = kk + 1; } } /* Note that 'values' will never contain NA/NaNs */ if (kk == 0) { ans[ii] = R_NaN; } else if (kk == -1) { ans[ii] = R_NaReal; } else { /* When narm == TRUE, isOdd and qq may change with row */ if (narm == TRUE) { isOdd = (kk % 2 == 1); qq = (R_xlen_t)(kk/2) - 1; } /* Permute x[0:kk-1] so that x[qq] is in the correct place with smaller values to the left, ... */ X_PSORT(values, kk, qq+1); value = values[qq+1]; if (isOdd == TRUE) { ans[ii] = (double)value; } else { /* Permute x[0:qq-2] so that x[qq-1] is in the correct place with smaller values to the left, ... */ X_PSORT(values, qq+1, qq); ans[ii] = ((double)values[qq] + (double)value)/2; } } R_CHECK_USER_INTERRUPT(ii); } /* for (ii ...) */ } else { for (ii=0; ii < nrows; ii++) { R_xlen_t rowIdx = byrow ? ROW_INDEX_NONA(crows,ii) : ROW_INDEX_NONA(crows,ii) * ncol; //HJ for (jj=0; jj < ncols; jj++) values[jj] = x[rowIdx+colOffset[jj]]; //HJ /* Permute x[0:ncols-1] so that x[qq] is in the correct place with smaller values to the left, ... */ X_PSORT(values, ncols, qq+1); value = values[qq+1]; if (isOdd == TRUE) { ans[ii] = (double)value; } else { /* Permute x[0:qq-2] so that x[qq-1] is in the correct place with smaller values to the left, ... */ X_PSORT(values, qq+1, qq); ans[ii] = ((double)values[qq] + (double)value)/2; } R_CHECK_USER_INTERRUPT(ii); } /* for (ii ...) */ } /* if (hasna ...) */ } /*************************************************************************** HISTORY: 2015-06-07 [DJ] o Supported subsetted computation. 2014-11-06 [HB] o CLEANUP: Moving away from R data types in low-level C functions. 2014-11-01 [HB] o SPEEDUP: Now using 'ansp = REAL(ans)' once and then assigning to 'ansp' instead of to 'REAL(ans)'. 2013-04-23 [HB] o BUG FIX: The integer template of rowMedians_() would not handle ties properly. This was because ties were calculated as '(double)((rowData[qq] + value)/2)' instead of '((double)(rowData[qq] + value))/2'. 2013-01-13 [HB] o Merged rowMedians_Integer() and rowMedians_Read() into template rowMedians_(). 2013-01-13 [HB] o Using internal arguments 'by_row' instead of 'by_column'. 2011-12-11 [HB] o BUG FIX: rowMediansReal(..., na.rm=TRUE) did not handle NaN:s, only NA:s. Note that NaN:s does not exist for integers. 2011-10-12 [HJ] o Added colMedians(). o Now rowMediansInteger/Real() can operate also by columns, cf. argument 'by_column'. 2007-08-14 [HB] o Added checks for user interrupts every 1000 line. o Added argument 'hasNA' to rowMedians(). 2005-12-07 [HB] o BUG FIX: When calculating the median of an even number (non-NA) values, the length of the second sort was one element too short, which made the method to freeze, i.e. rPsort(rowData, qq, qq) is now (...qq+1, qq). 2005-11-24 [HB] o By implementing a special version for integers, there is no need to coerce to double in R, which would take up twice the amount of memory. o rowMedians() now handles NAs too. o Adopted from rowQuantiles.c in Biobase of Bioconductor. **************************************************************************/ matrixStats/src/rowDiffs_TYPE-template.h0000644000175100001440000001342112776336765020037 0ustar hornikusers/*********************************************************************** TEMPLATE: void rowDiffs_(ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int byrow, R_xlen_t lag, R_xlen_t differences, X_C_TYPE *ans, R_xlen_t nrow_ans, R_xlen_t ncol_ans Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD_NAME: the name of the resulting function - X_TYPE: 'i' or 'r' Copyright: Henrik Bengtsson, 2014 ***********************************************************************/ #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, [METHOD_NAME]) */ #include "templates-types.h" #include #undef X_DIFF #undef DIFF_X_MATRIX #if X_TYPE == 'i' #ifndef diff_int static R_INLINE int diff_int(int a, int b) { if (X_ISNA(a) || X_ISNA(b)) return(NA_INTEGER); return a-b; } #define diff_int diff_int #endif #define X_DIFF diff_int #define DIFF_X_MATRIX diff_matrix_int #elif X_TYPE == 'r' #define X_DIFF(a,b) a-b #define DIFF_X_MATRIX diff_matrix_double #endif #if (X_TYPE == 'i' && !defined(diff_matrix_int)) || (X_TYPE == 'r' && !defined(diff_matrix_double)) static R_INLINE void DIFF_X_MATRIX(X_C_TYPE *x, R_xlen_t nrow_x, R_xlen_t ncol_x, int byrow, R_xlen_t lag, X_C_TYPE *y, R_xlen_t nrow_y, R_xlen_t ncol_y) { R_xlen_t ii, jj, ss, tt, uu; if (byrow) { uu = lag * nrow_x; tt = 0; ss = 0; for (jj=0; jj < ncol_y; jj++) { for (ii=0; ii < nrow_y; ii++) { y[ss++] = X_DIFF(x[uu++], x[tt++]); } } } else { uu = lag; tt = 0; ss = 0; for (jj=0; jj < ncol_y; jj++) { for (ii=0; ii < nrow_y; ii++) { /* Rprintf("y[%d] = x[%d] - x[%d] = %g - %g = %g\n", ss, uu, tt, (double)x[uu], (double)x[tt], (double)X_DIFF(x[uu], x[tt])); */ y[ss++] = X_DIFF(x[uu++], x[tt++]); } tt += lag; uu += lag; } } } #if X_TYPE == 'i' #define diff_matrix_int diff_matrix_int #elif X_TYPE == 'r' #define diff_matrix_double diff_matrix_double #endif #endif #undef DIFF_X_MATRIX_ROWS #ifdef ROWS_TYPE #if ROWS_TYPE == 'i' #define DIFF_X_MATRIX_ROWS CONCAT_MACROS(DIFF_X_MATRIX, intRows) #elif ROWS_TYPE == 'r' #define DIFF_X_MATRIX_ROWS CONCAT_MACROS(DIFF_X_MATRIX, realRows) #endif #else #define DIFF_X_MATRIX_ROWS CONCAT_MACROS(DIFF_X_MATRIX, noRows) #endif #undef DIFF_X_MATRIX_ROWS_COLS #ifdef COLS_TYPE #if COLS_TYPE == 'i' #define DIFF_X_MATRIX_ROWS_COLS CONCAT_MACROS(DIFF_X_MATRIX_ROWS, intCols) #elif COLS_TYPE == 'r' #define DIFF_X_MATRIX_ROWS_COLS CONCAT_MACROS(DIFF_X_MATRIX_ROWS, realCols) #endif #else #define DIFF_X_MATRIX_ROWS_COLS CONCAT_MACROS(DIFF_X_MATRIX_ROWS, noCols) #endif static R_INLINE void DIFF_X_MATRIX_ROWS_COLS(X_C_TYPE *x, R_xlen_t nrow, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int byrow, R_xlen_t lag, X_C_TYPE *ans, R_xlen_t nrow_ans, R_xlen_t ncol_ans) { R_xlen_t ii, jj, ss; R_xlen_t idx, colBegin1, colBegin2; X_C_TYPE xvalue1, xvalue2; #ifdef ROWS_TYPE ROWS_C_TYPE *crows = (ROWS_C_TYPE*) rows; #endif #ifdef COLS_TYPE COLS_C_TYPE *ccols = (COLS_C_TYPE*) cols; #endif ss = 0; if (byrow) { for (jj=0; jj < ncol_ans; jj++) { colBegin1 = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); colBegin2 = R_INDEX_OP(COL_INDEX(ccols,(jj+lag)), *, nrow); for (ii=0; ii < nrow_ans; ii++) { idx = R_INDEX_OP(colBegin1, +, ROW_INDEX(crows,ii)); xvalue1 = R_INDEX_GET(x, idx, X_NA); idx = R_INDEX_OP(colBegin2, +, ROW_INDEX(crows,ii)); xvalue2 = R_INDEX_GET(x, idx, X_NA); ans[ss++] = X_DIFF(xvalue2, xvalue1); } } } else { for (jj=0; jj < ncol_ans; jj++) { colBegin1 = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); for (ii=0; ii < nrow_ans; ii++) { idx = R_INDEX_OP(colBegin1, +, ROW_INDEX(crows,ii)); xvalue1 = R_INDEX_GET(x, idx, X_NA); idx = R_INDEX_OP(colBegin1, +, ROW_INDEX(crows,ii+lag)); xvalue2 = R_INDEX_GET(x, idx, X_NA); ans[ss++] = X_DIFF(xvalue2, xvalue1); } } } } RETURN_TYPE METHOD_NAME_ROWS_COLS(ARGUMENTS_LIST) { R_xlen_t nrow_tmp, ncol_tmp; X_C_TYPE *tmp = NULL; /* Nothing to do? */ if (ncol_ans <= 0 || nrow_ans <= 0) return; /* Special case (difference == 1) */ if (differences == 1) { DIFF_X_MATRIX_ROWS_COLS(x, nrow, rows, nrows, cols, ncols, byrow, lag, ans, nrow_ans, ncol_ans); } else { /* Allocate temporary work matrix (to hold intermediate differences) */ if (byrow) { nrow_tmp = nrows; ncol_tmp = ncols - lag; } else { nrow_tmp = nrows - lag; ncol_tmp = ncols; } tmp = Calloc(nrow_tmp*ncol_tmp, X_C_TYPE); /* (a) First order of differences */ DIFF_X_MATRIX_ROWS_COLS(x, nrow, rows, nrows, cols, ncols, byrow, lag, tmp, nrow_tmp, ncol_tmp); if (byrow) { ncol_tmp = ncol_tmp - lag; } else { nrow_tmp = nrow_tmp - lag; } /* (a) Intermediate orders of differences */ while (--differences > 1) { DIFF_X_MATRIX(tmp, nrow_tmp, ncol_tmp, byrow, lag, tmp, nrow_tmp, ncol_tmp); if (byrow) { ncol_tmp = ncol_tmp - lag; } else { nrow_tmp = nrow_tmp - lag; } } /* (c) Last order of differences */ DIFF_X_MATRIX(tmp, nrow_tmp, ncol_tmp, byrow, lag, ans, nrow_ans, ncol_ans); /* Deallocate temporary work matrix */ Free(tmp); } /* if (differences ...) */ } /*************************************************************************** HISTORY: 2015-06-13 [DJ] o Supported subsetted computation. 2014-12-29 [HB] o Created. **************************************************************************/ matrixStats/src/indexByRow.c0000644000175100001440000000431212776336765015666 0ustar hornikusers/*************************************************************************** Public methods: SEXP indexByRow(SEXP dim, SEXP idxs) Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include "types.h" SEXP indexByRow(SEXP dim, SEXP idxs) { SEXP ans; int nr, nc; int *ans_ptr, *idxs_ptr; R_xlen_t i, idx, n, nidxs; int col, row; int d; double nd = 1.0; /* Argument 'dim': */ if (!isInteger(dim) || xlength(dim) != 2) { error("Argument 'dim' must be an integer vector of length two."); } for (i = 0; i < xlength(dim); i++) { d = INTEGER(dim)[i]; if (d < 0) { error("Argument 'dim' specifies a negative value: %d", d); } nd *= d; #ifndef LONG_VECTOR_SUPPORT if (nd > R_INT_MAX) { error("Argument 'dim' specifies too many elements: %.g > %d", nd, R_INT_MAX); } #endif } n = (R_xlen_t)nd; /* Argument 'idxs': */ if (isNull(idxs)) { idxs_ptr = NULL; nidxs = 0; } else if (isVectorAtomic(idxs)) { idxs_ptr = INTEGER(idxs); nidxs = xlength(idxs); } else { /* To please compiler */ idxs_ptr = NULL; nidxs = 0; error("Argument 'idxs' must be NULL or a vector."); } nr = INTEGER(dim)[0]; nc = INTEGER(dim)[1]; if (idxs_ptr == NULL) { PROTECT(ans = allocVector(INTSXP, n)); ans_ptr = INTEGER(ans); row = 1; col = 0; for (i = 0; i < n; i++) { ans_ptr[i] = row + col*nr; col++; if (col == nc) { row++; col = 0; } } UNPROTECT(1); } else { PROTECT(ans = allocVector(INTSXP, nidxs)); ans_ptr = INTEGER(ans); for (i = 0; i < nidxs; i++) { // idxs <- idxs - 1 // cols <- idxs %/% dim[2L] // rows <- idxs %% dim[2L] // cols + dim[1L]*rows + 1L idx = idxs_ptr[i] - 1; col = idx / nc; row = idx - nc*col; row = idx % nc; idx = col + nr*row + 1; ans_ptr[i] = idx; } UNPROTECT(1); } return(ans); } // indexByRow() /*************************************************************************** HISTORY: 2014-11-09 [HB] o Created. **************************************************************************/ matrixStats/src/rowCounts.c0000644000175100001440000000517012776336765015602 0ustar hornikusers/*************************************************************************** Public methods: SEXP rowCounts(SEXP x, ...) Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD rowCounts #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, X_C_TYPE value, int what, int narm, int hasna, int *ans #define X_TYPE 'i' #include "templates-gen-matrix.h" #define X_TYPE 'r' #include "templates-gen-matrix.h" #define X_TYPE 'l' #include "templates-gen-matrix.h" SEXP rowCounts(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP value, SEXP what, SEXP naRm, SEXP hasNA) { SEXP ans; int narm, hasna, what2; R_xlen_t nrow, ncol; /* Argument 'x' & 'dim': */ assertArgMatrix(x, dim, (R_TYPE_LGL | R_TYPE_INT | R_TYPE_REAL), "x"); nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'value': */ if (length(value) != 1) error("Argument 'value' must be a single value."); if (!isNumeric(value)) error("Argument 'value' must be a numeric value."); /* Argument 'what': */ what2 = asInteger(what); if (what2 < 0 || what2 > 2) error("INTERNAL ERROR: Unknown value of 'what' for rowCounts: %d", what2); /* Argument 'naRm': */ narm = asLogicalNoNA(naRm, "na.rm"); /* Argument 'hasNA': */ hasna = asLogicalNoNA(hasNA, "hasNA"); /* Argument 'rows' and 'cols': */ R_xlen_t nrows, ncols; int rowsType, colsType; void *crows = validateIndices(rows, nrow, 0, &nrows, &rowsType); void *ccols = validateIndices(cols, ncol, 0, &ncols, &colsType); /* R allocate a double vector of length 'nrow' */ PROTECT(ans = allocVector(INTSXP, nrows)); /* Double matrices are more common to use. */ if (isReal(x)) { rowCounts_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, asReal(value), what2, narm, hasna, INTEGER(ans)); } else if (isInteger(x)) { rowCounts_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, asInteger(value), what2, narm, hasna, INTEGER(ans)); } else if (isLogical(x)) { rowCounts_Logical[rowsType][colsType](LOGICAL(x), nrow, ncol, crows, nrows, ccols, ncols, asLogical(value), what2, narm, hasna, INTEGER(ans)); } UNPROTECT(1); return(ans); } // rowCounts() /*************************************************************************** HISTORY: 2015-04-13 [DJ] o Supported subsetted computation. 2014-06-02 [HB] o Created. **************************************************************************/ matrixStats/src/templates-types_undef.h0000644000175100001440000000013312776336765020117 0ustar hornikusers#undef METHOD_NAME #undef X_TYPE #undef Y_TYPE #undef ANS_TYPE #undef MARGIN #undef OP matrixStats/src/templates-types.h0000644000175100001440000003155312776336765016750 0ustar hornikusers#include #include "macros.h" #undef X_C_TYPE #undef X_IN_C #undef X_ISNAN #undef X_ISNA #undef X_ABS #undef X_PSORT #undef X_QSORT_I #undef X_NA #undef Y_C_TYPE #undef Y_IN_C #undef Y_ISNAN #undef Y_ISNA #undef Y_ABS #undef Y_PSORT #undef Y_QSORT_I #undef Y_NA #undef ANS_SXP #undef ANS_NA #undef ANS_ISNAN #undef ANS_ISNA #undef ANS_C_TYPE #undef ANS_IN_C /* Data type macros for argument 'x' */ #ifdef X_TYPE #if X_TYPE == 'i' #define X_C_TYPE int #define X_IN_C INTEGER #define X_ISNAN(x) (x == NA_INTEGER) #define X_ISNA(x) (x == NA_INTEGER) #define X_ABS(x) abs(x) #define X_PSORT iPsort #define X_QSORT_I R_qsort_int_I #define X_NA NA_INTEGER #elif X_TYPE == 'r' #define X_C_TYPE double #define X_IN_C REAL #define X_ISNAN(x) ISNAN(x) /* NA or NaN */ #define X_ISNA(x) ISNA(x) /* NA only */ #define X_ABS(x) fabs(x) #define X_PSORT rPsort #define X_QSORT_I R_qsort_I #define X_NA NA_REAL #elif X_TYPE == 'l' #define X_C_TYPE int #define X_IN_C LOGICAL #define X_ISNAN(x) (x == NA_LOGICAL) #define X_NA NA_LOGICAL #else #error "INTERNAL ERROR: Failed to set C macro X_C_TYPE etc.: Unknown X_TYPE" #endif #endif /* Data type macros for argument 'y' */ #ifdef Y_TYPE #if Y_TYPE == 'i' #define Y_C_TYPE int #define Y_IN_C INTEGER #define Y_ISNAN(x) (x == NA_INTEGER) #define Y_ISNA(x) (x == NA_INTEGER) #define Y_ABS(x) abs(x) #define Y_PSORT iPsort #define Y_QSORT_I R_qsort_int_I #define Y_NA NA_INTEGER #elif Y_TYPE == 'r' #define Y_C_TYPE double #define Y_IN_C REAL #define Y_ISNAN(x) ISNAN(x) /* NA or NaN */ #define Y_ISNA(x) ISNA(x) /* NA only */ #define Y_ABS(x) fabs(x) #define Y_PSORT rPsort #define Y_QSORT_I R_qsort_I #define Y_NA NA_REAL #elif Y_TYPE == 'l' #define Y_C_TYPE int #define Y_IN_C LOGICAL #define Y_ISNAN(x) (x == NA_LOGICAL) #define Y_NA NA_LOGICAL #else #error "INTERNAL ERROR: Failed to set C macro Y_C_TYPE etc.: Unknown Y_TYPE" #endif #endif /* Data type macros for result ('ans') */ #ifndef ANS_TYPE #ifdef X_TYPE /* Default to same as 'x' */ #define ANS_TYPE X_TYPE #endif #endif #ifdef ANS_TYPE #if ANS_TYPE == 'i' #define ANS_SXP INTSXP #define ANS_NA NA_INTEGER #define ANS_ISNAN(x) (x == NA_INTEGER) #define ANS_ISNA(x) (x == NA_INTEGER) #define ANS_C_TYPE int #define ANS_IN_C INTEGER #elif ANS_TYPE == 'r' #define ANS_SXP REALSXP #define ANS_NA NA_REAL #define ANS_ISNAN(x) ISNAN(x) /* NA or NaN */ #define ANS_ISNA(x) ISNA(x) /* NA only */ #define ANS_C_TYPE double #define ANS_IN_C REAL #elif ANS_TYPE == 'l' #define ANS_SXP LGLSXP #define ANS_NA NA_LOGICAL #define ANS_ISNAN(x) (x == NA_LOGICAL) #define ANS_C_TYPE int #define ANS_IN_C LOGICAL #else #error "INTERNAL ERROR: Failed to set C macro ANS_C_TYPE: Unknown ANS_TYPE" #endif #endif /* Method name based on 'x' (and 'y') types */ #ifndef METHOD_NAME #if X_TYPE == 'i' #if Y_TYPE == 'i' #define METHOD_NAME CONCAT_MACROS(METHOD, Integer_Integer) #elif Y_TYPE == 'r' #define METHOD_NAME CONCAT_MACROS(METHOD, Integer_Real) #elif Y_TYPE == 'l' #define METHOD_NAME CONCAT_MACROS(METHOD, Integer_Logical) #else #define METHOD_NAME CONCAT_MACROS(METHOD, Integer) #endif #elif X_TYPE == 'r' #if Y_TYPE == 'i' #define METHOD_NAME CONCAT_MACROS(METHOD, Real_Integer) #elif Y_TYPE == 'r' #define METHOD_NAME CONCAT_MACROS(METHOD, Real_Real) #elif Y_TYPE == 'l' #define METHOD_NAME CONCAT_MACROS(METHOD, Real_Logical) #else #define METHOD_NAME CONCAT_MACROS(METHOD, Real) #endif #elif X_TYPE == 'l' #if Y_TYPE == 'i' #define METHOD_NAME CONCAT_MACROS(METHOD, Logical_Integer) #elif Y_TYPE == 'r' #define METHOD_NAME CONCAT_MACROS(METHOD, Logical_Real) #elif Y_TYPE == 'l' #define METHOD_NAME CONCAT_MACROS(METHOD, Logical_Logical) #else #define METHOD_NAME CONCAT_MACROS(METHOD, Logical) #endif #else #error "INTERNAL ERROR: Failed to set C macro METHOD_NAME: Unknown X_TYPE" #endif #endif /* Subsetted indexing: matrix */ #undef ROW_INDEX_NONA #undef ROW_INDEX #undef ROWS_C_TYPE #undef METHOD_NAME_ROWS #undef COL_INDEX_NONA #undef COL_INDEX #undef COLS_C_TYPE #undef METHOD_NAME_ROWS_COLS #ifdef ROWS_TYPE #define ROW_INDEX_NONA(rows, ii) ((R_xlen_t)rows[ii]-1) #if ROWS_TYPE == 'i' #define ROWS_C_TYPE int #define ROW_INDEX(rows, ii) (rows[ii] == NA_INTEGER ? NA_R_XLEN_T : (R_xlen_t)rows[ii]-1) #define METHOD_NAME_ROWS CONCAT_MACROS(METHOD_NAME, intRows) #elif ROWS_TYPE == 'r' #define ROWS_C_TYPE double #define ROW_INDEX(rows, ii) (ISNAN(rows[ii]) ? NA_R_XLEN_T : (R_xlen_t)rows[ii]-1) #define METHOD_NAME_ROWS CONCAT_MACROS(METHOD_NAME, realRows) #else #error "INTERNAL ERROR: Failed to set C macro METHOD_NAME: Unknown ROWS_TYPE" #endif #else #define ROW_INDEX_NONA(rows, ii) (ii) #define ROW_INDEX(rows, ii) (ii) #define ROWS_C_TYPE void #define METHOD_NAME_ROWS CONCAT_MACROS(METHOD_NAME, noRows) #endif #ifdef COLS_TYPE #define COL_INDEX_NONA(cols, jj) ((R_xlen_t)cols[jj]-1) #if COLS_TYPE == 'i' #define COLS_C_TYPE int #define COL_INDEX(cols, jj) (cols[jj] == NA_INTEGER ? NA_R_XLEN_T : (R_xlen_t)cols[jj]-1) #define METHOD_NAME_ROWS_COLS CONCAT_MACROS(METHOD_NAME_ROWS, intCols) #elif COLS_TYPE == 'r' #define COLS_C_TYPE double #define COL_INDEX(cols, jj) (ISNAN(cols[jj]) ? NA_R_XLEN_T : (R_xlen_t)cols[jj]-1) #define METHOD_NAME_ROWS_COLS CONCAT_MACROS(METHOD_NAME_ROWS, realCols) #else #error "INTERNAL ERROR: Failed to set C macro METHOD_NAME: Unknown ROWS_TYPE" #endif #else #define COL_INDEX_NONA(cols, jj) (jj) #define COL_INDEX(cols, jj) (jj) #define COLS_C_TYPE void #define METHOD_NAME_ROWS_COLS CONCAT_MACROS(METHOD_NAME_ROWS, noCols) #endif #undef METHOD_NAME_noRows #undef METHOD_NAME_noRows_noCols #undef METHOD_NAME_noRows_intCols #undef METHOD_NAME_noRows_realCols #undef METHOD_NAME_intRows #undef METHOD_NAME_intRows_noCols #undef METHOD_NAME_intRows_intCols #undef METHOD_NAME_intRows_realCols #undef METHOD_NAME_realRows #undef METHOD_NAME_realRows_noCols #undef METHOD_NAME_realRows_intCols #undef METHOD_NAME_realRows_realCols #define METHOD_NAME_noRows CONCAT_MACROS(METHOD_NAME, noRows) #define METHOD_NAME_noRows_noCols CONCAT_MACROS(METHOD_NAME_noRows, noCols) #define METHOD_NAME_noRows_intCols CONCAT_MACROS(METHOD_NAME_noRows, intCols) #define METHOD_NAME_noRows_realCols CONCAT_MACROS(METHOD_NAME_noRows, realCols) #define METHOD_NAME_intRows CONCAT_MACROS(METHOD_NAME, intRows) #define METHOD_NAME_intRows_noCols CONCAT_MACROS(METHOD_NAME_intRows, noCols) #define METHOD_NAME_intRows_intCols CONCAT_MACROS(METHOD_NAME_intRows, intCols) #define METHOD_NAME_intRows_realCols CONCAT_MACROS(METHOD_NAME_intRows, realCols) #define METHOD_NAME_realRows CONCAT_MACROS(METHOD_NAME, realRows) #define METHOD_NAME_realRows_noCols CONCAT_MACROS(METHOD_NAME_realRows, noCols) #define METHOD_NAME_realRows_intCols CONCAT_MACROS(METHOD_NAME_realRows, intCols) #define METHOD_NAME_realRows_realCols CONCAT_MACROS(METHOD_NAME_realRows, realCols) /* Subsetted indexing: vector */ #undef IDX_INDEX_NONA #undef IDX_INDEX #undef IDXS_C_TYPE #undef METHOD_NAME_IDXS #ifdef IDXS_TYPE #define IDX_INDEX_NONA(idxs, ii) ((R_xlen_t)idxs[ii]-1) #if IDXS_TYPE == 'i' #define IDXS_C_TYPE int #define IDX_INDEX(idxs, ii) (idxs[ii] == NA_INTEGER ? NA_R_XLEN_T : (R_xlen_t)idxs[ii]-1) #define METHOD_NAME_IDXS CONCAT_MACROS(METHOD_NAME, intIdxs) #elif IDXS_TYPE == 'r' #define IDXS_C_TYPE double #define IDX_INDEX(idxs, ii) (ISNAN(idxs[ii]) ? NA_R_XLEN_T : (R_xlen_t)idxs[ii]-1) #define METHOD_NAME_IDXS CONCAT_MACROS(METHOD_NAME, realIdxs) #else #error "INTERNAL ERROR: Failed to set C macro METHOD_NAME: Unknown IDXS_TYPE" #endif #else #define IDX_INDEX_NONA(idxs, ii) (ii) #define IDX_INDEX(idxs, ii) (ii) #define IDXS_C_TYPE void #define METHOD_NAME_IDXS CONCAT_MACROS(METHOD_NAME, noIdxs) #endif #undef METHOD_NAME_noIdxs #undef METHOD_NAME_intIdxs #undef METHOD_NAME_realIdxs #define METHOD_NAME_noIdxs CONCAT_MACROS(METHOD_NAME, noIdxs) #define METHOD_NAME_intIdxs CONCAT_MACROS(METHOD_NAME, intIdxs) #define METHOD_NAME_realIdxs CONCAT_MACROS(METHOD_NAME, realIdxs) /* Subsetted indexing: matrix + vector */ #undef METHOD_NAME_ROWS_COLS_IDXS #ifdef IDXS_TYPE #if IDXS_TYPE == 'i' #define METHOD_NAME_ROWS_COLS_IDXS CONCAT_MACROS(METHOD_NAME_ROWS_COLS, intIdxs) #elif IDXS_TYPE == 'r' #define METHOD_NAME_ROWS_COLS_IDXS CONCAT_MACROS(METHOD_NAME_ROWS_COLS, realIdxs) #endif #else #define METHOD_NAME_ROWS_COLS_IDXS CONCAT_MACROS(METHOD_NAME_ROWS_COLS, noIdxs) #endif #define METHOD_NAME_noIdxs CONCAT_MACROS(METHOD_NAME, noIdxs) #undef METHOD_NAME_noRows_noCols_noIdxs #undef METHOD_NAME_noRows_noCols_intIdxs #undef METHOD_NAME_noRows_noCols_realIdxs #undef METHOD_NAME_noRows_intCols_noIdxs #undef METHOD_NAME_noRows_intCols_intIdxs #undef METHOD_NAME_noRows_intCols_realIdxs #undef METHOD_NAME_noRows_realCols_noIdxs #undef METHOD_NAME_noRows_realCols_intIdxs #undef METHOD_NAME_noRows_realCols_realIdxs #undef METHOD_NAME_intRows_noCols_noIdxs #undef METHOD_NAME_intRows_noCols_intIdxs #undef METHOD_NAME_intRows_noCols_realIdxs #undef METHOD_NAME_intRows_intCols_noIdxs #undef METHOD_NAME_intRows_intCols_intIdxs #undef METHOD_NAME_intRows_intCols_realIdxs #undef METHOD_NAME_intRows_realCols_noIdxs #undef METHOD_NAME_intRows_realCols_intIdxs #undef METHOD_NAME_intRows_realCols_realIdxs #undef METHOD_NAME_realRows_noCols_noIdxs #undef METHOD_NAME_realRows_noCols_intIdxs #undef METHOD_NAME_realRows_noCols_realIdxs #undef METHOD_NAME_realRows_intCols_noIdxs #undef METHOD_NAME_realRows_intCols_intIdxs #undef METHOD_NAME_realRows_intCols_realIdxs #undef METHOD_NAME_realRows_realCols_noIdxs #undef METHOD_NAME_realRows_realCols_intIdxs #undef METHOD_NAME_realRows_realCols_realIdxs #define METHOD_NAME_noRows_noCols_noIdxs CONCAT_MACROS(METHOD_NAME_noRows_noCols, noIdxs) #define METHOD_NAME_noRows_noCols_intIdxs CONCAT_MACROS(METHOD_NAME_noRows_noCols, intIdxs) #define METHOD_NAME_noRows_noCols_realIdxs CONCAT_MACROS(METHOD_NAME_noRows_noCols, realIdxs) #define METHOD_NAME_noRows_intCols_noIdxs CONCAT_MACROS(METHOD_NAME_noRows_intCols, noIdxs) #define METHOD_NAME_noRows_intCols_intIdxs CONCAT_MACROS(METHOD_NAME_noRows_intCols, intIdxs) #define METHOD_NAME_noRows_intCols_realIdxs CONCAT_MACROS(METHOD_NAME_noRows_intCols, realIdxs) #define METHOD_NAME_noRows_realCols_noIdxs CONCAT_MACROS(METHOD_NAME_noRows_realCols, noIdxs) #define METHOD_NAME_noRows_realCols_intIdxs CONCAT_MACROS(METHOD_NAME_noRows_realCols, intIdxs) #define METHOD_NAME_noRows_realCols_realIdxs CONCAT_MACROS(METHOD_NAME_noRows_realCols, realIdxs) #define METHOD_NAME_intRows_noCols_noIdxs CONCAT_MACROS(METHOD_NAME_intRows_noCols, noIdxs) #define METHOD_NAME_intRows_noCols_intIdxs CONCAT_MACROS(METHOD_NAME_intRows_noCols, intIdxs) #define METHOD_NAME_intRows_noCols_realIdxs CONCAT_MACROS(METHOD_NAME_intRows_noCols, realIdxs) #define METHOD_NAME_intRows_intCols_noIdxs CONCAT_MACROS(METHOD_NAME_intRows_intCols, noIdxs) #define METHOD_NAME_intRows_intCols_intIdxs CONCAT_MACROS(METHOD_NAME_intRows_intCols, intIdxs) #define METHOD_NAME_intRows_intCols_realIdxs CONCAT_MACROS(METHOD_NAME_intRows_intCols, realIdxs) #define METHOD_NAME_intRows_realCols_noIdxs CONCAT_MACROS(METHOD_NAME_intRows_realCols, noIdxs) #define METHOD_NAME_intRows_realCols_intIdxs CONCAT_MACROS(METHOD_NAME_intRows_realCols, intIdxs) #define METHOD_NAME_intRows_realCols_realIdxs CONCAT_MACROS(METHOD_NAME_intRows_realCols, realIdxs) #define METHOD_NAME_realRows_noCols_noIdxs CONCAT_MACROS(METHOD_NAME_realRows_noCols, noIdxs) #define METHOD_NAME_realRows_noCols_intIdxs CONCAT_MACROS(METHOD_NAME_realRows_noCols, intIdxs) #define METHOD_NAME_realRows_noCols_realIdxs CONCAT_MACROS(METHOD_NAME_realRows_noCols, realIdxs) #define METHOD_NAME_realRows_intCols_noIdxs CONCAT_MACROS(METHOD_NAME_realRows_intCols, noIdxs) #define METHOD_NAME_realRows_intCols_intIdxs CONCAT_MACROS(METHOD_NAME_realRows_intCols, intIdxs) #define METHOD_NAME_realRows_intCols_realIdxs CONCAT_MACROS(METHOD_NAME_realRows_intCols, realIdxs) #define METHOD_NAME_realRows_realCols_noIdxs CONCAT_MACROS(METHOD_NAME_realRows_realCols, noIdxs) #define METHOD_NAME_realRows_realCols_intIdxs CONCAT_MACROS(METHOD_NAME_realRows_realCols, intIdxs) #define METHOD_NAME_realRows_realCols_realIdxs CONCAT_MACROS(METHOD_NAME_realRows_realCols, realIdxs) /* Subsetted indexing: whether to check NA according to indexing */ #undef R_INDEX_OP #undef R_INDEX_GET #if !defined(ROWS_TYPE) && !defined(COLS_TYPE) && !defined(IDXS_TYPE) #define R_INDEX_OP(a, OP, b) (a OP b) #define R_INDEX_GET(x, i, NA) x[i] #else #define R_INDEX_OP(a, OP, b) (a == NA_R_XLEN_T || b == NA_R_XLEN_T ? NA_R_XLEN_T : a OP b) #define R_INDEX_GET(x, i, NA) (i == NA_R_XLEN_T ? NA : x[i]) #endif matrixStats/src/rowMads_TYPE-template.h0000644000175100001440000001634612776336765017701 0ustar hornikusers/*********************************************************************** TEMPLATE: void rowMads_(ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, double scale, int narm, int hasna, int byrow, double *ans Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD: the name of the resulting function - X_TYPE: 'i' or 'r' Authors: Adopted from rowQuantiles.c by R. Gentleman. Template by Henrik Bengtsson. Copyright: Henrik Bengtsson, 2007-2013 ***********************************************************************/ #include #include #include "types.h" #include /* abs() and fabs() */ /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, X_ISNAN, [METHOD_NAME]) */ #include "templates-types.h" RETURN_TYPE METHOD_NAME_ROWS_COLS(ARGUMENTS_LIST) { int isOdd; R_xlen_t ii, jj, kk, qq, idx; R_xlen_t *colOffset; X_C_TYPE *values, value, mu; double *values_d, value_d, mu_d; #ifdef ROWS_TYPE ROWS_C_TYPE *crows = (ROWS_C_TYPE*) rows; #endif #ifdef COLS_TYPE COLS_C_TYPE *ccols = (COLS_C_TYPE*) cols; #endif /* R allocate memory for the 'values'. This will be taken care of by the R garbage collector later on. */ values = (X_C_TYPE *) R_alloc(ncols, sizeof(X_C_TYPE)); values_d = (double *) R_alloc(ncols, sizeof(double)); /* If there are no missing values, don't try to remove them. */ if (hasna == FALSE) narm = FALSE; /* When narm == FALSE, isOdd and qq are the same for all rows */ if (narm == FALSE) { isOdd = (ncols % 2 == 1); qq = (R_xlen_t)(ncols/2) - 1; } else { isOdd = FALSE; qq = 0; } value = 0; /* Pre-calculate the column offsets */ colOffset = (R_xlen_t *) R_alloc(ncols, sizeof(R_xlen_t)); // HJ begin if (byrow) { for (jj=0; jj < ncols; jj++) colOffset[jj] = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); } else { for (jj=0; jj < ncols; jj++) colOffset[jj] = COL_INDEX(ccols,jj); } // HJ end hasna = TRUE; if (hasna == TRUE) { for (ii=0; ii < nrows; ii++) { R_xlen_t rowIdx = byrow ? ROW_INDEX(crows,ii) : R_INDEX_OP(ROW_INDEX(crows,ii), *, ncol); //HJ kk = 0; /* The index of the last non-NA value detected */ for (jj=0; jj < ncols; jj++) { idx = R_INDEX_OP(rowIdx, +, colOffset[jj]); value = R_INDEX_GET(x, idx, X_NA); //HJ if (X_ISNAN(value)) { if (narm == FALSE) { kk = -1; break; } } else { values[kk] = value; kk = kk + 1; } } /* for (jj ...) */ /* Note that 'values' will never contain NA/NaNs */ if (kk == 0) { ans[ii] = NA_REAL; } else if (kk == 1) { ans[ii] = 0; } else if (kk == -1) { ans[ii] = R_NaReal; } else { /* When narm == TRUE, isOdd and qq may change with row */ if (narm == TRUE) { isOdd = (kk % 2 == 1); qq = (R_xlen_t)(kk/2) - 1; } /* Permute x[0:kk-1] so that x[qq] is in the correct place with smaller values to the left, ... */ X_PSORT(values, kk, qq+1); value = values[qq+1]; /* Calculate mu and sigma */ if (isOdd == TRUE) { /* Since there are an odd number of values, then we also know that 'mu' is one of the values in 'x', which in turn mean we don't have to coerce integers to doubles, if 'x' is an integer. Simple benchmarking shows that it significantly faster to avoid coercion. */ mu = value; /* (a) Subtract mu and absolute value, i.e. x <- |x-mu| */ for (jj=0; jj < kk; jj++) { value = (values[jj] - mu); values[jj] = X_ABS(value); } /* (b) Calculate median of |x-mu| */ /* Permute x[0:kk-1] so that x[qq] is in the correct place with smaller values to the left, ... */ X_PSORT(values, kk, qq+1); value = values[qq+1]; ans[ii] = scale * (double)value; } else { /* Here we have to coerce to doubles since 'mu' is an average. */ /* Permute x[0:qq-2] so that x[qq-1] is in the correct place with smaller values to the left, ... */ X_PSORT(values, qq+1, qq); #if X_TYPE == 'i' /* If the difference between two integers is an even number, then their means is also an integer, and then we can avoid coercion to double also here. This should happen roughly half the time we end up here which is worth optimizing for. Simple benchmarking show a significant difference in speed, particular for the column-based version. */ if ((values[qq] - value) % 2 == 0) { /* No need to coerce */ mu = (values[qq] + value)/2; /* (a) Subtract mu and absolute value, i.e. x <- |x-mu| */ for (jj=0; jj < kk; jj++) { value = (values[jj] - mu); values[jj] = X_ABS(value); } /* (b) Calculate median of |x-mu| */ /* Permute x[0:kk-1] so that x[qq] is in the correct place with smaller values to the left, ... */ X_PSORT(values, kk, qq+1); X_PSORT(values, qq+1, qq); ans[ii] = scale * ((double)values[qq] + (double)values[qq+1])/2; /* Done, continue to next vector */ continue; } #endif mu_d = ((double)values[qq] + (double)value)/2; /* (a) Subtract mu and square, i.e. x <- (x-mu)^2 */ for (jj=0; jj < kk; jj++) { value_d = ((double)values[jj] - mu_d); values_d[jj] = fabs(value_d); } /* (b) Calculate median */ /* Permute x[0:kk-1] so that x[qq-1] and x[qq] are in the correct places with smaller values to the left, ... */ rPsort(values_d, kk, qq+1); rPsort(values_d, qq+1, qq); ans[ii] = scale * (values_d[qq] + values_d[qq+1])/2; } } /* if (kk == 0) */ R_CHECK_USER_INTERRUPT(ii); } /* for (ii ...) */ } else { for (ii=0; ii < nrows; ii++) { R_xlen_t rowIdx = byrow ? ROW_INDEX_NONA(crows,ii) : ROW_INDEX_NONA(crows,ii)*ncol; //HJ for (jj=0; jj < ncols; jj++) values[jj] = x[rowIdx+colOffset[jj]]; //HJ /* Permute x[0:ncols-1] so that x[qq] is in the correct place with smaller values to the left, ... */ X_PSORT(values, ncols, qq+1); value = values[qq+1]; if (isOdd == TRUE) { ans[ii] = (double)value; } else { /* Permute x[0:qq-2] so that x[qq-1] is in the correct place with smaller values to the left, ... */ X_PSORT(values, qq+1, qq); ans[ii] = ((double)values[qq] + value)/2; } R_CHECK_USER_INTERRUPT(ii); } /* for (ii ...) */ } /* if (hasna ...) */ } /*************************************************************************** HISTORY: 2015-06-13 [DJ] o Supported subsetted computation. 2014-11-17 [HB] o Created from rowMedians_TYPE-template.h. **************************************************************************/ matrixStats/src/productExpSumLog_TYPE-template.h0000644000175100001440000000536312776336765021546 0ustar hornikusers/*********************************************************************** TEMPLATE: double productExpSumLog_[idxsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nx, void *idxs, R_xlen_t nidxs, int narm, int hasna Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD_NAME: the name of the resulting function - X_TYPE: 'i' or 'r' Copyright: Henrik Bengtsson, 2014 ***********************************************************************/ #include #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, [METHOD_NAME]) */ #include "templates-types.h" RETURN_TYPE METHOD_NAME_IDXS(ARGUMENTS_LIST) { LDOUBLE y = 0.0, t; R_xlen_t ii; int isneg = 0; int hasZero = 0; #ifdef IDXS_TYPE IDXS_C_TYPE *cidxs = (IDXS_C_TYPE*) idxs; #endif /* Calculate sum(log(abs(x))) */ for (ii = 0 ; ii < nidxs; ii++) { t = R_INDEX_GET(x, IDX_INDEX(cidxs,ii), X_NA); /* Missing values? */ if (narm) { if (X_ISNAN(t)) continue; } #if X_TYPE == 'i' /* Early stopping? */ if (X_ISNAN(t)) { y = NA_REAL; break; } else if (t < 0) { isneg = !isneg; t = -t; } else if (t == 0) { hasZero = 1; /* Early stopping? */ if (narm) break; } #elif X_TYPE == 'r' if (t < 0) { isneg = !isneg; t = -t; } #endif t = log(t); y += t; /* Rprintf("#%d: x=%g, is.nan(x)=%d, abs(x)=%g, is.nan(abs(x))=%d, log(abs(x))=%g, is.nan(log(abs(x)))=%d, sum=%g, is.nan(sum)=%d\n", ii, x[ii], R_IsNaN(x[ii]), X_ABS(x[ii]), R_IsNaN(abs(x[ii])), t, R_IsNaN(y), y, R_IsNaN(y)); */ #if X_TYPE == 'r' /* Early stopping? Special for long LDOUBLE vectors */ if (ii % 1048576 == 0 && ISNAN(y)) break; #endif } if (ISNAN(y)) { /* If there where NA and/or NaN elements, then 'y' will at this point be NaN. The information on an NA value is lost when calculating fabs(NA), which returns NaN. For consistency with integers, we return NA in all cases. */ y = NA_REAL; } else if (hasZero) { /* no NA in 'x' and 'x' contains zero */ y = 0; } else { y = exp(y); /* Update sign */ if (isneg) { y = -y; } /* Overflow or underflow? */ if (y > DOUBLE_XMAX) { y = R_PosInf; } else if (y < -DOUBLE_XMAX) { y = R_NegInf; } } return (double)y; } /*************************************************************************** HISTORY: 2015-07-04 [DJ] o Supported subsetted computation. 2014-11-06 [HB] o CLEANUP: Moving away from R data types in low-level C functions. 2014-06-04 [HB] o Created. **************************************************************************/ matrixStats/src/anyMissing.c0000644000175100001440000000254712776336765015725 0ustar hornikusers/*************************************************************************** Public methods: anyMissing(SEXP x, SEXP idxs) TO DO: Support list():s too. Copyright Henrik Bengtsson, 2007 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD anyMissing #define METHOD_NAME anyMissing_internal #define RETURN_TYPE int #define ARGUMENTS_LIST SEXP x, void *idxs, R_xlen_t nidxs #include "templates-gen-vector.h" SEXP anyMissing(SEXP x, SEXP idxs) { SEXP ans; R_xlen_t nx; nx = xlength(x); /* Argument 'idxs': */ R_xlen_t nidxs; int idxsType; void *cidxs = validateIndices(idxs, nx, 1, &nidxs, &idxsType); PROTECT(ans = allocVector(LGLSXP, 1)); LOGICAL(ans)[0] = 0; /* anyMissing() on zero-length objects should always return FALSE, just like any(double(0)). */ if (nidxs == 0) { UNPROTECT(1); return(ans); } LOGICAL(ans)[0] = anyMissing_internal[idxsType](x, cidxs, nidxs); UNPROTECT(1); /* ans */ return(ans); } // anyMissing() /*************************************************************************** HISTORY: 2015-06-14 [DJ] o Supported subsetted computation. 2007-08-14 [HB] o Created using do_isna() in src/main/coerce.c as a template. **************************************************************************/ matrixStats/src/macros.h0000644000175100001440000000047412776336765015072 0ustar hornikusers#ifndef _MACROS_H_ #define _MACROS_H_ #define CONCAT(x,y) x ##_## y #define CONCAT_MACROS(x,y) CONCAT(x,y) #define QUOTE(str) #str #define QUOTE_MACROS(str) QUOTE(str) #ifndef METHOD_TEMPLATE_H #define METHOD_TEMPLATE_H QUOTE_MACROS(CONCAT_MACROS(METHOD,TYPE-template.h)) #endif #endif /* END OF _MACROS_H_ */ matrixStats/src/binCounts-BINBY-template.h0000644000175100001440000000640012776336765020217 0ustar hornikusers/*************************************************************************** TEMPLATE: void binCounts_(...) GENERATES: void binCounts_L(double *x, int nx, double *bx, int nbins, int *count) void binCounts_R(double *x, int nx, double *bx, int nbins, int *count) Arguments: The following macros ("arguments") should be defined for the template to work as intended. - BIN_BY: 'L' or 'R' Copyright Henrik Bengtsson, 2012-2014 **************************************************************************/ #include #if BIN_BY == 'L' /* [u,v) */ #define METHOD_NAME binCounts_L #define IS_PART_OF_FIRST_BIN(x, bx0) (x < bx0) #define IS_PART_OF_NEXT_BIN(x, bx1) (x >= bx1) #elif BIN_BY == 'R' /* (u,v] */ #define METHOD_NAME binCounts_R #define IS_PART_OF_FIRST_BIN(x, bx0) (x <= bx0) #define IS_PART_OF_NEXT_BIN(x, bx1) (x > bx1) #endif void METHOD_NAME(double *x, R_xlen_t nx, double *bx, R_xlen_t nbins, int *count) { R_xlen_t ii = 0, jj = 0, iStart = 0; int n = 0; int warn = 0; // Count? if (nbins > 0) { // Skip to the first bin while ((iStart < nx) && IS_PART_OF_FIRST_BIN(x[iStart], bx[0])) { ++iStart; } // For each x... for (ii = iStart; ii < nx; ++ii) { // Skip to a new bin? while (IS_PART_OF_NEXT_BIN(x[ii], bx[jj+1])) { count[jj++] = n; // No more bins? if (jj >= nbins) { ii = nx; // Cause outer for-loop to exit break; } n = 0; } /* Although unlikely, with long vectors the count for a bin can become greater than what is possible to represent by an integer. Detect and warn about this. */ if (n == R_INT_MAX) { warn = 1; // No point in keep counting for this bin break; } // Count ++n; } // Update count of the last bin? if (jj < nbins) { count[jj] = n; // Assign the remaining bins to zero counts while (++jj < nbins) { count[jj] = 0; } } } // if (nbins > 0) if (warn) { warning("Integer overflow. Detected one or more bins with a count that is greater than what can be represented by the integer data type. Setting count to the maximum integer possible (.Machine$integer.max = %d). The bin mean is still correct.", R_INT_MAX); } } /* Undo template macros */ #undef BIN_BY #undef IS_PART_OF_FIRST_BIN #undef IS_PART_OF_NEXT_BIN #include "templates-types_undef.h" /*************************************************************************** HISTORY: 2014-11-07 [HB] o ROBUSTNESS: Added protection for integer overflow in bin counts. 2014-11-06 [HB] o CLEANUP: Moving away from R data types in low-level C functions. 2013-10-08 [HB] o Created template for binCounts_() to create functions that bin either by [u,v) or (u,v]. 2013-05-10 [HB] o SPEEDUP: binCounts() no longer tests in every iteration (=for every data point) whether the last bin has been reached or not. 2012-10-10 [HB] o BUG FIX: binCounts() would return random/garbage counts for bins that were beyond the last data point. o BUG FIX: In some cases binCounts() could try to go past the last bin. 2012-10-03 [HB] o Created. **************************************************************************/ matrixStats/src/utils.h0000644000175100001440000000722012776336765014742 0ustar hornikusers#include #include "types.h" #define R_TYPE_LGL 1 /* 0b0001 */ #define R_TYPE_INT 2 /* 0b0010 */ #define R_TYPE_REAL 4 /* 0b0100 */ static R_INLINE void assertArgVector(SEXP x, int type, char *xlabel) { /* Argument 'x': */ if (!isVectorAtomic(x)) { error("Argument '%s' must be a matrix or a vector.", xlabel); } switch (TYPEOF(x)) { case LGLSXP: if (!(type & R_TYPE_LGL)) error("Argument '%s' cannot be logical.", xlabel); break; case INTSXP: if (!(type & R_TYPE_INT)) error("Argument '%s' cannot be integer.", xlabel); break; case REALSXP: if (!(type & R_TYPE_REAL)) error("Argument '%s' cannot be numeric.", xlabel); break; } /* switch */ } /* assertArgVector() */ static R_INLINE void assertArgDim(SEXP dim, double max, char *maxlabel) { double nrow, ncol; /* Argument 'dim': */ if (!isVectorAtomic(dim) || xlength(dim) != 2 || !isInteger(dim)) { error("Argument 'dim' must be an integer vector of length two."); } nrow = (double)INTEGER(dim)[0]; ncol = (double)INTEGER(dim)[1]; if (nrow < 0) { error("Argument 'dim' specifies a negative number of rows (dim[1]): %d", nrow); } else if (ncol < 0) { error("Argument 'dim' specifies a negative number of columns (dim[2]): %d", ncol); } else if (nrow * ncol != max) { error("Argument 'dim' does not match length of argument '%s': %g * %g != %g", maxlabel, nrow, ncol, max); } } /* assertArgDim() */ static R_INLINE void assertArgMatrix(SEXP x, SEXP dim, int type, char *xlabel) { /* Argument 'x': */ if (isMatrix(x)) { } else if (isVectorAtomic(x)) { } else { error("Argument '%s' must be a matrix or a vector.", xlabel); } switch (TYPEOF(x)) { case LGLSXP: if (!(type & R_TYPE_LGL)) error("Argument '%s' cannot be logical.", xlabel); break; case INTSXP: if (!(type & R_TYPE_INT)) error("Argument '%s' cannot be integer.", xlabel); break; case REALSXP: if (!(type & R_TYPE_REAL)) error("Argument '%s' cannot be numeric.", xlabel); break; } /* switch */ /* Argument 'dim': */ assertArgDim(dim, xlength(x), "x"); } /* assertArgMatrix() */ static R_INLINE int asLogicalNoNA(SEXP x, char *xlabel) { int value = 0; if (length(x) != 1) error("Argument '%s' must be a single value.", xlabel); if (isLogical(x)) { value = asLogical(x); } else if (isInteger(x)) { value = asInteger(x); } else { error("Argument '%s' must be a logical.", xlabel); } if (value != TRUE && value != FALSE) error("Argument '%s' must be either TRUE or FALSE.", xlabel); return value; } /* asLogicalNoNA() */ /* Retrieve the 'i'th element of 'x' as R_xlen_t */ static R_INLINE R_xlen_t asR_xlen_t(SEXP x, R_xlen_t i) { int mode = TYPEOF(x); switch (mode) { case INTSXP: return INTEGER(x)[i]; case REALSXP: return REAL(x)[i]; default: error("only integer and numeric are supported."); } return 0; } /* asR_xlen_t() */ /* Specified in validateIndices.c */ void *validateIndicesCheckNA(SEXP idxs, R_xlen_t maxIdx, int allowOutOfBound, R_xlen_t *ansNidxs, int *type, int *hasna); void *validateIndices(SEXP idxs, R_xlen_t maxIdx, int allowOutOfBound, R_xlen_t *ansNidxs, int *type); static R_INLINE int IntegerFromReal(double x) { if (ISNAN(x)) return NA_INTEGER; if (x > INT_MAX || x <= INT_MIN) return NA_INTEGER; return x; } /* IntegerFromReal() */ static R_INLINE double RealFromInteger(int x) { if (x == NA_INTEGER) return NA_REAL; return x; } /* RealFromInteger() */ #define SWAP(type, x, y) { \ type tmp = x; \ x = y; \ y = tmp; \ } matrixStats/src/colOrderStats.c0000644000175100001440000000522412776336765016367 0ustar hornikusers/*************************************************************************** Public methods: SEXP colOrderStats(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP which) Authors: Henrik Bengtsson To do: Add support for missing values. Copyright Henrik Bengtsson, 2007-2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD colOrderStats #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, R_xlen_t qq, X_C_TYPE *ans #define X_TYPE 'i' #include "templates-gen-matrix.h" #define X_TYPE 'r' #include "templates-gen-matrix.h" SEXP colOrderStats(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP which) { SEXP ans = NILSXP; R_xlen_t nrow, ncol, qq; /* Argument 'x' and 'dim': */ assertArgMatrix(x, dim, (R_TYPE_INT | R_TYPE_REAL), "x"); nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'which': */ if (length(which) != 1) error("Argument 'which' must be a single number."); if (!isNumeric(which)) error("Argument 'which' must be a numeric number."); /* Argument 'rows' and 'cols': */ R_xlen_t nrows, ncols; int rowsType, colsType; int rowsHasna, colsHasna; void *crows = validateIndicesCheckNA(rows, nrow, 0, &nrows, &rowsType, &rowsHasna); void *ccols = validateIndicesCheckNA(cols, ncol, 0, &ncols, &colsType, &colsHasna); // Check missing rows if (rowsHasna && ncols > 0) { error("Argument 'rows' must not contain missing value"); } // Check missing cols if (colsHasna && nrows > 0) { error("Argument 'cols' must not contain missing value"); } /* Subtract one here, since rPsort does zero based addressing */ qq = asInteger(which) - 1; /* Assert that 'qq' is a valid index */ if (qq < 0 || qq >= nrows) { error("Argument 'which' is out of range."); } /* Double matrices are more common to use. */ if (isReal(x)) { PROTECT(ans = allocVector(REALSXP, ncols)); colOrderStats_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, qq, REAL(ans)); UNPROTECT(1); } else if (isInteger(x)) { PROTECT(ans = allocVector(INTSXP, ncols)); colOrderStats_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, qq, INTEGER(ans)); UNPROTECT(1); } return(ans); } // colOrderStats() /*************************************************************************** HISTORY: 2015-07-08 [DJ] o Supported subsetted computation. 2014-11-16 [HB] o Created from rowOrderStats.c. **************************************************************************/ matrixStats/src/templates-gen-vector.h0000644000175100001440000000052012776336765017643 0ustar hornikusers#include "macros.h" #include METHOD_TEMPLATE_H #define IDXS_TYPE 'i' #include METHOD_TEMPLATE_H #undef IDXS_TYPE #define IDXS_TYPE 'r' #include METHOD_TEMPLATE_H #undef IDXS_TYPE RETURN_TYPE (*METHOD_NAME[3])(ARGUMENTS_LIST) = { METHOD_NAME_noIdxs, METHOD_NAME_intIdxs, METHOD_NAME_realIdxs }; #include "templates-types_undef.h" matrixStats/src/colRanges.c0000644000175100001440000001035312776336765015513 0ustar hornikusers/*************************************************************************** Public methods: SEXP colRanges(SEXP x, ...) Authors: Henrik Bengtsson. Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD colRanges #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int what, int narm, int hasna, X_C_TYPE *ans, int *is_counted #define X_TYPE 'i' #include "templates-gen-matrix.h" #define X_TYPE 'r' #include "templates-gen-matrix.h" SEXP colRanges(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP what, SEXP naRm, SEXP hasNA) { SEXP ans = NILSXP, ans2 = NILSXP; int *mins, *maxs; double *mins2, *maxs2; int *is_counted, all_counted = 0; int what2, narm, hasna; R_xlen_t nrow, ncol, jj; /* Argument 'x' and 'dim': */ assertArgMatrix(x, dim, (R_TYPE_INT | R_TYPE_REAL), "x"); nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'what': */ if (length(what) != 1) error("Argument 'what' must be a single number."); if (!isNumeric(what)) error("Argument 'what' must be a numeric number."); what2 = asInteger(what); if (what2 < 0 || what2 > 2) error("Invalid value of 'what': %d", what2); /* Argument 'naRm': */ narm = asLogicalNoNA(naRm, "na.rm"); /* Argument 'hasNA': */ hasna = asLogicalNoNA(hasNA, "hasNA"); /* Argument 'rows' and 'cols': */ R_xlen_t nrows, ncols; int rowsType, colsType; void *crows = validateIndices(rows, nrow, 0, &nrows, &rowsType); void *ccols = validateIndices(cols, ncol, 0, &ncols, &colsType); is_counted = (int *) R_alloc(ncols, sizeof(int)); if (isReal(x)) { if (what2 == 2) { PROTECT(ans = allocMatrix(REALSXP, ncols, 2)); } else { PROTECT(ans = allocVector(REALSXP, ncols)); } colRanges_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, what2, narm, hasna, REAL(ans), is_counted); UNPROTECT(1); } else if (isInteger(x)) { if (what2 == 2) { PROTECT(ans = allocMatrix(INTSXP, ncols, 2)); } else { PROTECT(ans = allocVector(INTSXP, ncols)); } colRanges_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, what2, narm, hasna, INTEGER(ans), is_counted); /* Any entries with zero non-missing values? */ all_counted = 1; for (jj=0; jj < ncols; jj++) { if (!is_counted[jj]) { all_counted = 0; break; } } if (!all_counted) { /* Handle zero non-missing values */ /* Instead of return INTSXP, we must return REALSXP (to hold -Inf, and Inf) */ if (what2 == 0) { PROTECT(ans2 = allocVector(REALSXP, ncols)); mins = INTEGER(ans); mins2 = REAL(ans2); for (jj=0; jj < ncols; jj++) { if (is_counted[jj]) { mins2[jj] = (double)mins[jj]; } else { mins2[jj] = R_PosInf; } } UNPROTECT(1); /* ans2 */ } else if (what2 == 1) { PROTECT(ans2 = allocVector(REALSXP, ncols)); maxs = INTEGER(ans); maxs2 = REAL(ans2); for (jj=0; jj < ncols; jj++) { if (is_counted[jj]) { maxs2[jj] = (double)maxs[jj]; } else { maxs2[jj] = R_NegInf; } } UNPROTECT(1); /* ans2 */ } else if (what2 == 2) { PROTECT(ans2 = allocMatrix(REALSXP, ncols, 2)); mins = INTEGER(ans); maxs = &INTEGER(ans)[ncols]; mins2 = REAL(ans2); maxs2 = &REAL(ans2)[ncols]; for (jj=0; jj < ncols; jj++) { if (is_counted[jj]) { mins2[jj] = (double)mins[jj]; maxs2[jj] = (double)maxs[jj]; } else { mins2[jj] = R_PosInf; maxs2[jj] = R_NegInf; } } UNPROTECT(1); /* ans2 */ } ans = ans2; } UNPROTECT(1); /* ans */ } return(ans); } // colRanges() /*************************************************************************** HISTORY: 2015-06-07 [DJ] o Supported subsetted computation. 2014-11-16 [HB] o Created. **************************************************************************/ matrixStats/src/signTabulate_TYPE-template.h0000644000175100001440000000333512776336765020701 0ustar hornikusers/*********************************************************************** TEMPLATE: void signTabulate_[idxsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nx, void *idxs, R_xlen_t nidxs, double *ans Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD_NAME: the name of the resulting function - X_TYPE: 'i' or 'r' Copyright: Henrik Bengtsson, 2014 ***********************************************************************/ #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, [METHOD_NAME]) */ #include "templates-types.h" RETURN_TYPE METHOD_NAME_IDXS(ARGUMENTS_LIST) { X_C_TYPE xi; R_xlen_t ii; R_xlen_t nNeg = 0, nZero = 0, nPos = 0, nNA=0; #if X_TYPE == 'r' R_xlen_t nPosInf=0, nNegInf=0; #endif #ifdef IDXS_TYPE IDXS_C_TYPE *cidxs = (IDXS_C_TYPE*) idxs; #endif for (ii = 0; ii < nidxs; ii++) { xi = R_INDEX_GET(x, IDX_INDEX(cidxs,ii), X_NA); if (X_ISNAN(xi)) { nNA++; } else if (xi > 0) { nPos++; #if X_TYPE == 'r' if (xi == R_PosInf) nPosInf++; #endif } else if (xi < 0) { nNeg++; #if X_TYPE == 'r' if (xi == R_NegInf) nNegInf++; #endif } else if (xi == 0) { nZero++; } } ans[0] = nNeg; ans[1] = nZero; ans[2] = nPos; ans[3] = nNA; #if X_TYPE == 'r' ans[4] = nNegInf; ans[5] = nPosInf; #endif } /*************************************************************************** HISTORY: 2015-07-04 [DJ] o Supported subsetted computation. 2014-11-06 [HB] o CLEANUP: Moving away from R data types in low-level C functions. 2014-06-04 [HB] o Created. **************************************************************************/ matrixStats/src/colOrderStats_TYPE-template.h0000644000175100001440000000464012776336765021047 0ustar hornikusers/*********************************************************************** TEMPLATE: void colOrderStats_[rowsType][colsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, R_xlen_t qq, X_C_TYPE *ans Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD_NAME: the name of the resulting function - X_TYPE: 'i' or 'r' - ANS_TYPE: 'i' or 'r' Authors: Adopted from ditto for rows. Copyright: Henrik Bengtsson, 2007-2014 ***********************************************************************/ #include #include #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, [METHOD_NAME]) ANS_TYPE => (ANS_SXP, ANS_NA, ANS_C_TYPE, ANS_IN_C) */ #include "templates-types.h" RETURN_TYPE METHOD_NAME_ROWS_COLS(ARGUMENTS_LIST) { R_xlen_t ii, jj; R_xlen_t offset; X_C_TYPE *values; #ifdef ROWS_TYPE ROWS_C_TYPE *crows = (ROWS_C_TYPE*) rows; // Check missing rows for (ii=0; ii < nrows; ++ii) { if (ROW_INDEX(crows,ii) == NA_R_XLEN_T) break; } if (ii < nrows && ncols > 0) { error("Argument 'rows' must not contain missing value"); } #endif #ifdef COLS_TYPE COLS_C_TYPE *ccols = (COLS_C_TYPE*) cols; // Check missing cols for (jj=0; jj < ncols; ++jj) { if (COL_INDEX(ccols,jj) == NA_R_XLEN_T) break; } if (jj < ncols && nrows > 0) { error("Argument 'cols' must not contain missing value"); } #endif /* R allocate memory for the 'values'. This will be taken care of by the R garbage collector later on. */ values = (X_C_TYPE *) R_alloc(nrows, sizeof(X_C_TYPE)); for (jj=0; jj < ncols; jj++) { offset = COL_INDEX_NONA(ccols,jj) * nrow; for (ii=0; ii < nrows; ii++) values[ii] = x[ROW_INDEX_NONA(crows,ii) + offset]; /* Sort vector of length 'nrows' up to position 'qq'. "...partial sorting: they permute x so that x[qq] is in the correct place with smaller values to the left, larger ones to the right." */ X_PSORT(values, nrows, qq); ans[jj] = values[qq]; } } /*************************************************************************** HISTORY: 2015-07-08 [DJ] o Supported subsetted computation. 2014-11-16 [HB] o Created from rowOrderStats() ditto. **************************************************************************/ matrixStats/src/signTabulate.c0000644000175100001440000000275112776336765016223 0ustar hornikusers/*************************************************************************** Public methods: SEXP signTabulate(SEXP x, SEXP idxs) Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD signTabulate #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nx, void *idxs, R_xlen_t nidxs, double *ans #define X_TYPE 'i' #include "templates-gen-vector.h" #define X_TYPE 'r' #include "templates-gen-vector.h" SEXP signTabulate(SEXP x, SEXP idxs) { SEXP ans = NILSXP; R_xlen_t nx; /* Argument 'x': */ assertArgVector(x, (R_TYPE_INT | R_TYPE_REAL), "x"); nx = xlength(x); /* Argument 'idxs': */ R_xlen_t nidxs; int idxsType; void *cidxs = validateIndices(idxs, nx, 1, &nidxs, &idxsType); /* Double matrices are more common to use. */ if (isReal(x)) { PROTECT(ans = allocVector(REALSXP, 6)); signTabulate_Real[idxsType](REAL(x), nx, cidxs, nidxs, REAL(ans)); UNPROTECT(1); } else if (isInteger(x)) { PROTECT(ans = allocVector(REALSXP, 4)); signTabulate_Integer[idxsType](INTEGER(x), nx, cidxs, nidxs, REAL(ans)); UNPROTECT(1); } return(ans); } // signTabulate() /*************************************************************************** HISTORY: 2015-07-04 [DJ] o Supported subsetted computation. 2014-06-04 [HB] o Created. **************************************************************************/ matrixStats/src/binCounts.c0000644000175100001440000000360712776336765015546 0ustar hornikusers/*************************************************************************** Public methods: binCounts(SEXP x, SEXP bx, SEXP right) Copyright Henrik Bengtsson, 2012-2013 **************************************************************************/ #include #include "types.h" #include "utils.h" #include #define BIN_BY 'L' #include "binCounts-BINBY-template.h" #define BIN_BY 'R' #include "binCounts-BINBY-template.h" SEXP binCounts(SEXP x, SEXP bx, SEXP right) { SEXP counts = NILSXP; R_xlen_t nbins; int closedRight; /* Argument 'x': */ assertArgVector(x, (R_TYPE_REAL), "x"); /* Argument 'bx': */ assertArgVector(bx, (R_TYPE_REAL), "bx"); nbins = xlength(bx)-1; if (nbins <= 0) { error("Argument 'bx' must specify at least two bin boundaries (= one bin): %d", xlength(bx)); } /* Argument 'right': */ closedRight = asLogicalNoNA(right, "right"); PROTECT(counts = allocVector(INTSXP, nbins)); if (closedRight) { binCounts_R(REAL(x), xlength(x), REAL(bx), nbins, INTEGER(counts)); } else { binCounts_L(REAL(x), xlength(x), REAL(bx), nbins, INTEGER(counts)); } UNPROTECT(1); return(counts); } // binCounts() /*************************************************************************** HISTORY: 2015-05-30 [HB] o Added protected against 'bx' too short. 2014-06-03 [HB] o Dropped unused variable 'count'. 2013-10-08 [HB] o Now binCounts() calls binCounts_(). 2013-05-10 [HB] o SPEEDUP: binCounts() no longer tests in every iteration (=for every data point) whether the last bin has been reached or not. 2012-10-10 [HB] o BUG FIX: binCounts() would return random/garbage counts for bins that were beyond the last data point. o BUG FIX: In some cases binCounts() could try to go past the last bin. 2012-10-03 [HB] o Created. **************************************************************************/ matrixStats/src/rowCumprods.c0000644000175100001440000000365712776336765016133 0ustar hornikusers/*************************************************************************** Public methods: SEXP rowCumprods(SEXP x, ...) Authors: Henrik Bengtsson Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD rowCumprods #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int byrow, ANS_C_TYPE *ans #define X_TYPE 'i' #include "templates-gen-matrix.h" #define X_TYPE 'r' #include "templates-gen-matrix.h" SEXP rowCumprods(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP byRow) { int byrow; SEXP ans = NILSXP; R_xlen_t nrow, ncol; /* Argument 'x' and 'dim': */ assertArgMatrix(x, dim, (R_TYPE_INT | R_TYPE_REAL), "x"); nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'rows' and 'cols': */ R_xlen_t nrows, ncols; int rowsType, colsType; void *crows = validateIndices(rows, nrow, 0, &nrows, &rowsType); void *ccols = validateIndices(cols, ncol, 0, &ncols, &colsType); /* Argument 'byRow': */ byrow = asLogical(byRow); /* Double matrices are more common to use. */ if (isReal(x)) { PROTECT(ans = allocMatrix(REALSXP, nrows, ncols)); rowCumprods_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, byrow, REAL(ans)); UNPROTECT(1); } else if (isInteger(x)) { PROTECT(ans = allocMatrix(INTSXP, nrows, ncols)); rowCumprods_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, byrow, INTEGER(ans)); UNPROTECT(1); } return(ans); } /* rowCumprods() */ /*************************************************************************** HISTORY: 2015-06-07 [DJ] o Supported subsetted computation. 2014-11-26 [HB] o Created from rowVars.c. **************************************************************************/ matrixStats/src/rowRanges_TYPE-template.h0000644000175100001440000001503112776336765020222 0ustar hornikusers/*********************************************************************** TEMPLATE: void rowRanges_[rowsType][colsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int what, int narm, int hasna, X_C_TYPE *ans, int *is_counted Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD_NAME: the name of the resulting function - X_TYPE: 'i' or 'r' - ANS_TYPE: 'i' or 'r' Authors: Henrik Bengtsson. Copyright: Henrik Bengtsson, 2014 ***********************************************************************/ #include #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, [METHOD_NAME]) ANS_TYPE => (ANS_SXP, ANS_NA, ANS_C_TYPE, ANS_IN_C) */ #include "templates-types.h" RETURN_TYPE METHOD_NAME_ROWS_COLS(ARGUMENTS_LIST) { R_xlen_t ii, jj; R_xlen_t colBegin, idx; X_C_TYPE value, *mins = NULL, *maxs = NULL; int *skip = NULL; #ifdef ROWS_TYPE ROWS_C_TYPE *crows = (ROWS_C_TYPE*) rows; #endif #ifdef COLS_TYPE COLS_C_TYPE *ccols = (COLS_C_TYPE*) cols; #endif /* Rprintf("(nrow,ncol)=(%d,%d), what=%d\n", nrow, ncol, what); */ /* If there are no missing values, don't try to remove them. */ if (hasna == FALSE) narm = FALSE; if (hasna) { skip = (int *) R_alloc(nrows, sizeof(int)); for (ii=0; ii < nrows; ii++) { is_counted[ii] = 0; skip[ii] = 0; } /* Missing values */ if (what == 0) { /* rowMins() */ mins = ans; for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); for (ii=0; ii < nrows; ii++) { if (!narm && skip[ii]) continue; idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); value = R_INDEX_GET(x, idx, X_NA); if (X_ISNAN(value)) { if (!narm) { mins[ii] = value; is_counted[ii] = 1; /* Early stopping? */ #if X_TYPE == 'i' skip[ii] = 1; #elif X_TYPE == 'r' if (X_ISNA(value)) skip[ii] = 1; #endif } } else if (!is_counted[ii]) { mins[ii] = value; is_counted[ii] = 1; } else if (value < mins[ii]) { mins[ii] = value; } } } /* for (jj ...) */ #if X_TYPE == 'r' /* Handle zero non-missing values */ for (ii=0; ii < nrows; ii++) { if (!is_counted[ii]) { mins[ii] = R_PosInf; } } #endif } else if (what == 1) { /* rowMaxs() */ maxs = ans; for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); for (ii=0; ii < nrows; ii++) { if (!narm && skip[ii]) continue; idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); value = R_INDEX_GET(x, idx, X_NA); if (X_ISNAN(value)) { if (!narm) { maxs[ii] = value; is_counted[ii] = 1; /* Early stopping? */ #if X_TYPE == 'i' skip[ii] = 1; #elif X_TYPE == 'r' if (X_ISNA(value)) skip[ii] = 1; #endif } } else if (!is_counted[ii]) { maxs[ii] = value; is_counted[ii] = 1; } else if (value > maxs[ii]) { maxs[ii] = value; } } } /* for (jj ...) */ #if X_TYPE == 'r' /* Handle zero non-missing values */ for (ii=0; ii < nrows; ii++) { if (!is_counted[ii]) { maxs[ii] = R_NegInf; } } #endif } else if (what == 2) { /* rowRanges() */ mins = ans; maxs = &ans[nrows]; for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); for (ii=0; ii < nrows; ii++) { if (!narm && skip[ii]) continue; idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); value = R_INDEX_GET(x, idx, X_NA); if (X_ISNAN(value)) { if (!narm) { mins[ii] = value; maxs[ii] = value; is_counted[ii] = 1; /* Early stopping? */ #if X_TYPE == 'i' skip[ii] = 1; #elif X_TYPE == 'r' if (X_ISNA(value)) skip[ii] = 1; #endif } } else if (!is_counted[ii]) { mins[ii] = value; maxs[ii] = value; is_counted[ii] = 1; } else if (value < mins[ii]) { mins[ii] = value; } else if (value > maxs[ii]) { maxs[ii] = value; } } } /* for (jj ...) */ #if X_TYPE == 'r' /* Handle zero non-missing values */ for (ii=0; ii < nrows; ii++) { if (!is_counted[ii]) { mins[ii] = R_PosInf; maxs[ii] = R_NegInf; } } #endif } /* if (what ...) */ } else { /* No missing values */ if (what == 0) { /* rowMins() */ mins = ans; /* Initiate results */ for (ii=0; ii < nrows; ii++) { mins[ii] = x[ii]; } for (jj=1; jj < ncols; jj++) { colBegin = COL_INDEX_NONA(ccols,jj) * nrow; for (ii=0; ii < nrows; ii++) { value = x[ROW_INDEX_NONA(crows,ii)+colBegin]; if (value < mins[ii]) mins[ii] = value; } } } else if (what == 1) { /* rowMax() */ maxs = ans; /* Initiate results */ for (ii=0; ii < nrows; ii++) { maxs[ii] = x[ii]; } for (jj=1; jj < ncols; jj++) { colBegin = COL_INDEX_NONA(ccols,jj) * nrow; for (ii=0; ii < nrows; ii++) { value = x[ROW_INDEX_NONA(crows,ii)+colBegin]; if (value > maxs[ii]) maxs[ii] = value; } } } else if (what == 2) { /* rowRanges()*/ mins = ans; maxs = &ans[nrows]; /* Initiate results */ for (ii=0; ii < nrows; ii++) { mins[ii] = x[ii]; maxs[ii] = x[ii]; } for (jj=1; jj < ncols; jj++) { colBegin = COL_INDEX_NONA(ccols,jj) * nrow; for (ii=0; ii < nrows; ii++) { value = x[ROW_INDEX_NONA(crows,ii)+colBegin]; if (value < mins[ii]) { mins[ii] = value; } else if (value > maxs[ii]) { maxs[ii] = value; } } } } /* if (what ...) */ } /* if (narm) */ } /*************************************************************************** HISTORY: 2015-06-07 [DJ] o Supported subsetted computation. 2014-11-16 [HB] o Created. **************************************************************************/ matrixStats/src/diff2.c0000644000175100001440000000411512776336765014567 0ustar hornikusers/*************************************************************************** Public methods: SEXP diff2(SEXP x, SEXP idxs, SEXP lag, SEXP differences) Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include #include "types.h" #include "utils.h" #define METHOD diff2 #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nx, void *idxs, R_xlen_t nidxs, R_xlen_t lag, R_xlen_t differences, X_C_TYPE *ans, R_xlen_t nans #define X_TYPE 'i' #include "templates-gen-vector.h" #define X_TYPE 'r' #include "templates-gen-vector.h" SEXP diff2(SEXP x, SEXP idxs, SEXP lag, SEXP differences) { SEXP ans = NILSXP; R_xlen_t nx, nans, lagg, diff; /* Argument 'x': */ assertArgVector(x, (R_TYPE_INT | R_TYPE_REAL), "x"); nx = xlength(x); /* Argument 'lag': */ lagg = asInteger(lag); if (lagg < 1) { error("Argument 'lag' must be a positive integer."); } /* Argument 'differences': */ diff = asInteger(differences); if (diff < 1) { error("Argument 'differences' must be a positive integer."); } /* Argument 'idxs': */ R_xlen_t nidxs; int idxsType; void *cidxs = validateIndices(idxs, nx, 1, &nidxs, &idxsType); /* Length of result vector */ nans = (R_xlen_t)((double)nidxs - ((double)diff*(double)lagg)); if (nans < 0) nans = 0; /* Dispatch to low-level C function */ if (isReal(x)) { PROTECT(ans = allocVector(REALSXP, nans)); diff2_Real[idxsType](REAL(x), nx, cidxs, nidxs, lagg, diff, REAL(ans), nans); UNPROTECT(1); } else if (isInteger(x)) { PROTECT(ans = allocVector(INTSXP, nans)); diff2_Integer[idxsType](INTEGER(x), nx, cidxs, nidxs, lagg, diff, INTEGER(ans), nans); UNPROTECT(1); } else { error("Argument 'x' must be numeric."); } return ans; } // diff2() /*************************************************************************** HISTORY: 2015-06-14 [DJ] o Supported subsetted computation. 2014-12-29 [HB] o Created. **************************************************************************/ matrixStats/src/logSumExp_TYPE-template.h0000644000175100001440000001210112776336765020171 0ustar hornikusers/*********************************************************************** TEMPLATE: double logSumExp_double[idxsType](ARGUMENTS_LIST) ARGUMENTS_LIST: double *x, void *idxs, R_xlen_t nidxs, int narm, int hasna, int by, double *xx ***********************************************************************/ #include #include #include "types.h" #include "templates-types.h" /* logSumExp_double(x, by=0, xx=NULL): 1. Scans for the maximum value of x=(x[0], x[1], ..., x[n-1]) 2. Computes result from 'x'. NOTE: The above sweeps the "contiguous" 'x' vector twice. --- logSumExp_double(x, by=by, xx=xx): 1. Scans for the maximum value of x=(x[0], x[by], ..., x[(n-1)*by]) and copies the values to xx = (xx[0], xx[1], xx[2], ..., xx[n-1]), which *must* be preallocated. 2. Computes result from 'xx'. NOTE: The above sweeps the "scattered" 'x' vector only once, and then the "contigous" 'xx' vector once. This is more likely to create cache hits. */ RETURN_TYPE METHOD_NAME_IDXS(ARGUMENTS_LIST) { R_xlen_t ii, iMax, idx; double xii, xMax; LDOUBLE sum; int hasna2 = FALSE; /* Indicates whether NAs where detected or not */ int xMaxIsNA; #ifdef IDXS_TYPE IDXS_C_TYPE *cidxs = (IDXS_C_TYPE*) idxs; #endif /* Quick return? */ if (nidxs == 0) { return(R_NegInf); } /* Find the maximum value */ iMax = 0; if (by) { idx = R_INDEX_OP(IDX_INDEX(cidxs,0), *, by); xMax = R_INDEX_GET(x, idx, NA_REAL); } else { xMax = R_INDEX_GET(x, IDX_INDEX(cidxs,0), NA_REAL); } xMaxIsNA = ISNAN(xMax); if (nidxs == 1) { if (narm && xMaxIsNA) { return(R_NegInf); } else { return(xMax); } } if (xMaxIsNA) hasna2 = TRUE; if (by) { /* To increase the chances for cache hits below, which sweeps through the data twice, we copy data into a temporary contigous vector while scanning for the maximum value. */ xx[0] = xMax; for (ii=1; ii < nidxs; ii++) { /* Get the ii:th value */ idx = R_INDEX_OP(IDX_INDEX(cidxs,ii), *, by); xii = R_INDEX_GET(x, idx, NA_REAL); /* Copy */ xx[ii] = xii; if (hasna && ISNAN(xii)) { if (narm) { hasna2 = TRUE; continue; } else { return(R_NaReal); } } if (xii > xMax || (narm && xMaxIsNA)) { iMax = ii; xMax = xii; xMaxIsNA = ISNAN(xMax); } R_CHECK_USER_INTERRUPT(ii); } /* for (ii ...) */ } else { for (ii=1; ii < nidxs; ii++) { /* Get the ii:th value */ xii = R_INDEX_GET(x, IDX_INDEX(cidxs,ii), NA_REAL); if (hasna && ISNAN(xii)) { if (narm) { hasna2 = TRUE; continue; } else { return(R_NaReal); } } if (xii > xMax || (narm && xMaxIsNA)) { iMax = ii; xMax = xii; xMaxIsNA = ISNAN(xMax); } R_CHECK_USER_INTERRUPT(ii); } /* for (ii ...) */ } /* by */ /* Early stopping? */ if (xMaxIsNA) { /* Found only missing values? */ return narm ? R_NegInf : R_NaReal; } else if (xMax == R_PosInf) { /* Found +Inf? */ return(R_PosInf); } else if (xMax == R_NegInf) { /* all values are -Inf */ return(R_NegInf); } /* Sum differences */ sum = 0.0; if (by) { for (ii=0; ii < nidxs; ii++) { if (ii == iMax) { continue; } /* Get the ii:th value */ xii = xx[ii]; if (!hasna2 || !ISNAN(xii)) { sum += exp(xii - xMax); } /* Early LDOUBLE stopping on -Inf/+Inf and user interrupt? */ if (ii % 1048576 == 0) { if (!R_FINITE(sum)) break; R_CheckUserInterrupt(); } } /* for (ii ...) */ } else { for (ii=0; ii < nidxs; ii++) { if (ii == iMax) { continue; } /* Get the ii:th value */ xii = R_INDEX_GET(x, IDX_INDEX(cidxs,ii), NA_REAL); if (!hasna2 || !ISNAN(xii)) { sum += exp(xii - xMax); } /* Early LDOUBLE stopping on -Inf/+Inf and user interrupt? */ if (ii % 1048576 == 0) { if (!R_FINITE(sum)) break; R_CheckUserInterrupt(); } } /* for (ii ...) */ } /* if (by) */ sum = xMax + log1p(sum); return(sum); } /*************************************************************************** HISTORY: 2015-06-11 [DJ] o Supported subsetted computation. 2015-06-10 [DJ] o Merge 'logSumExp_double_by' to 'logSumExp_double' 2015-01-26 [HB] o SPEEDUP: Now step 2 ("summing") only checks where NAs if NAs were detected in step 1 ("max value"), which should be noticibly faster since testing for NA is expensive for double values. o SPEEDUP: Now function returns early after step 1 ("max value") if the maximum value found is +Inf, or if all values where NAs. o BUG FIX: Now logSumExp(, na.rm=TRUE) also returns -Inf. 2013-05-02 [HB] o BUG FIX: Incorrectly used ISNAN() on an int variable as caught by the 'cc' compiler on Solaris. Reported by Brian Ripley upon CRAN submission. 2013-04-30 [HB] o Created. **************************************************************************/ matrixStats/src/binMeans-BINBY-template.h0000644000175100001440000001206612776336765020014 0ustar hornikusers/*************************************************************************** TEMPLATE: binMeans_(...) GENERATES: void binMeans_L(double *y, R_xlen_t ny, double *x, R_xlen_t nx, double *bx, R_xlen_t nbins, double *ans, int *count) void binMeans_R(double *y, R_xlen_t ny, double *x, R_xlen_t nx, double *bx, R_xlen_t nbins, double *ans, int *count) Arguments: The following macros ("arguments") should be defined for the template to work as intended. - BIN_BY: 'L' or 'R' Copyright Henrik Bengtsson, 2012-2013 **************************************************************************/ #include "types.h" #if BIN_BY == 'L' /* [u,v) */ #define METHOD_NAME binMeans_L #define IS_PART_OF_FIRST_BIN(x, bx0) (x < bx0) #define IS_PART_OF_NEXT_BIN(x, bx1) (x >= bx1) #elif BIN_BY == 'R' /* (u,v] */ #define METHOD_NAME binMeans_R #define IS_PART_OF_FIRST_BIN(x, bx0) (x <= bx0) #define IS_PART_OF_NEXT_BIN(x, bx1) (x > bx1) #endif void METHOD_NAME(double *y, R_xlen_t ny, double *x, R_xlen_t nx, double *bx, R_xlen_t nbins, double *ans, int *count) { R_xlen_t ii = 0, jj = 0, iStart=0; R_xlen_t n = 0; LDOUBLE sum = 0.0; int warn = 0; // Count? if (nbins > 0) { // Skip to the first bin while ((iStart < nx) && IS_PART_OF_FIRST_BIN(x[iStart], bx[0])) { ++iStart; } // For each x... for (ii = iStart; ii < nx; ++ii) { // Skip to a new bin? while (IS_PART_OF_NEXT_BIN(x[ii], bx[jj+1])) { // Update statistic of current bin? if (count) { /* Although unlikely, with long vectors the count for a bin can become greater than what is possible to represent by an integer. Detect and warn about this. */ if (n > R_INT_MAX) { warn = 1; count[jj] = R_INT_MAX; } else { count[jj] = n; } } ans[jj] = n > 0 ? sum / n : R_NaN; sum = 0.0; n = 0; // ...and move to next ++jj; // No more bins? if (jj >= nbins) { // Make the outer for-loop to exit... ii = nx - 1; // ...but correct for the fact that the y[nx-1] point will // be incorrectly added to the sum. Doing the correction // here avoids an if (ii < nx) sum += y[ii] below. sum -= y[ii]; break; } } // Sum and count sum += y[ii]; ++n; /* Early LDOUBLE stopping? */ if (n % 1048576 == 0 && !R_FINITE(sum)) break; } // Update statistic of the last bin? if (jj < nbins) { if (count) { /* Although unlikely, with long vectors the count for a bin can become greater than what is possible to represent by an integer. Detect and warn about this. */ if (n > R_INT_MAX) { warn= 1; count[jj] = R_INT_MAX; } else { count[jj] = n; } } ans[jj] = n > 0 ? sum / n : R_NaN; // Assign the remaining bins to zero counts and missing mean values while (++jj < nbins) { ans[jj] = R_NaN; if (count) count[jj] = 0; } } } // if (nbins > 0) if (warn) { warning("Integer overflow. Detected one or more bins with a count that is greater than what can be represented by the integer data type. Setting count to the maximum integer possible (.Machine$integer.max = %d). The bin mean is still correct.", R_INT_MAX); } } /* Undo template macros */ #undef BIN_BY #undef IS_PART_OF_FIRST_BIN #undef IS_PART_OF_NEXT_BIN #include "templates-types_undef.h" /*************************************************************************** HISTORY: 2014-11-07 [HB] o ROBUSTNESS: Added protection for integer overflow in bin counts. 2014-11-06 [HB] o CLEANUP: Moving away from R data types in low-level C functions. 2014-10-01 [HB] o BUG FIX: binMeans() returned 0.0 instead of NA_real_ for empty bins. 2014-04-04 [HB] o BUG FIX: The native code of binMeans(x, bx) would try to access an out-of-bounds value of argument 'y' iff 'x' contained elements that are left of all bins in 'bx'. This bug had no impact on the results and since no assignment was done it should also not crash/ core dump R. This was discovered thanks to new memtests (ASAN and valgrind) provided by CRAN. 2013-10-08 [HB] o Created template for binMeans_() to create functions that bin either by [u,v) or (u,v]. 2013-05-10 [HB] o SPEEDUP: binMeans() no longer tests in every iteration (=for every data point) whether the last bin has been reached or not. 2012-10-10 [HB] o BUG FIX: binMeans() would return random/garbage means/counts for bins that were beyond the last data point. o BUG FIX: In some cases binMeans() could try to go past the last bin. 2012-10-03 [HB] o Created binMeans(), which was adopted from from code proposed by Martin Morgan (Fred Hutchinson Cancer Research Center, Seattle) as a reply to HB's R-devel thread 'Fastest non-overlapping binning mean function out there?' on Oct 3, 2012. **************************************************************************/ matrixStats/src/types.h0000644000175100001440000000207312776336765014747 0ustar hornikusers#include /* R_xlen_t, ... */ #ifndef R_INT_MIN #define R_INT_MIN -INT_MAX #endif #ifndef R_INT_MAX #define R_INT_MAX INT_MAX #endif /* inf */ #ifndef IS_INF #define IS_INF(x) (x == R_PosInf || x == R_NegInf) #endif /* Subsetting index mode */ #ifndef SUBSETTED_MODE_INDEX #define SUBSETTED_MODE_INDEX #define SUBSETTED_ALL 0 #define SUBSETTED_INTEGER 1 #define SUBSETTED_REAL 2 #endif /* As in /src/include/Defn.h */ #ifdef HAVE_LONG_DOUBLE #define LDOUBLE long double #else #define LDOUBLE double #endif /* Backward compatibility with R (< 3.0.0) As in /src/include/Rinternals.h */ #ifndef R_XLEN_T_MAX typedef int R_xlen_t; #define R_XLEN_T_MAX R_LEN_T_MAX #ifndef xlength #define xlength length #endif #endif /* define NA_R_XLEN_T */ #ifdef LONG_VECTOR_SUPPORT #define R_XLEN_T_MIN -R_XLEN_T_MAX-1 #define NA_R_XLEN_T R_XLEN_T_MIN #else #define NA_R_XLEN_T NA_INTEGER #endif /* Macro to check for user interrupts every 2^20 iteration */ #define R_CHECK_USER_INTERRUPT(i) if (i % 1048576 == 0) R_CheckUserInterrupt() matrixStats/src/weightedMedian_TYPE-template.h0000644000175100001440000002102412776336765021170 0ustar hornikusers/*********************************************************************** TEMPLATE: double weightedMedian_[idxsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nx, double *w, void *idxs, R_xlen_t nidxs, int narm, int interpolate, int ties Copyright: Henrik Bengtsson, 2014 ***********************************************************************/ #include #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, [METHOD_NAME]) */ #include "templates-types.h" #include RETURN_TYPE METHOD_NAME_IDXS(ARGUMENTS_LIST) { X_C_TYPE value; X_C_TYPE *xtmp; double weight, res; double dx, dy, Dy; double *wtmp, *wcum, wtotal, wlow, whigh, tmp_d, tmp_d2; R_xlen_t nxt, ii, jj, half; int *idxs_int; int equalweights = 0; #ifdef IDXS_TYPE IDXS_C_TYPE *cidxs = (IDXS_C_TYPE*) idxs; #endif /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */ /* Weights */ /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */ wtmp = Calloc(nidxs, double); /* Check for missing, negative, and infite weights */ nxt = 0; for (ii=0; ii < nidxs; ii++) { /* Assume negative or missing weight by default or that the signals is missing and should be dropped */ wtmp[ii] = 0; weight = R_INDEX_GET(w, IDX_INDEX(cidxs,ii), NA_REAL); if (ISNAN(weight)) { if (!narm) { Free(wtmp); return NA_REAL; } } else if (weight <= 0) { /* Drop non-positive weights */ } else if (isinf(weight)) { /* Detected a +Inf. From now on, treat all +Inf weights equal and drop everything else */ nxt = 0; for (jj=0; jj < nidxs; jj++) { /* Assume non-infinite weight by default */ wtmp[jj] = 0; weight = R_INDEX_GET(w, IDX_INDEX(cidxs,jj), NA_REAL); if (isinf(weight)) { value = R_INDEX_GET(x, IDX_INDEX(cidxs,jj), X_NA); if (X_ISNAN(value)) { if (!narm) { Free(wtmp); return NA_REAL; } } else { /* Infinite weight, i.e. use data point */ wtmp[jj] = 1; nxt++; } } else if (ISNAN(weight)) { if (!narm) { Free(wtmp); return NA_REAL; } } } equalweights = 1; break; } else { /* A data points with a finite positive weight */ value = R_INDEX_GET(x, IDX_INDEX(cidxs,ii), X_NA); if (X_ISNAN(value)) { if (!narm) { Free(wtmp); return NA_REAL; } } else { /* A data point with a non-missing value */ wtmp[ii] = weight; nxt++; } } } /* printf("nx=%d, nxt=%d\n", nx, nxt); for (ii=0; ii < nx; ii++) printf("w[%d]=%g, wtmp[%d]=%g\n", (int)ii, (double)w[ii], (int)ii, wtmp[ii]); */ /* Nothing to do? */ if (nxt == 0) { Free(wtmp); return NA_REAL; } /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */ /* Copy (x,w) to work with and calculate total weight */ /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */ xtmp = Calloc(nxt, X_C_TYPE); jj = 0; wtotal = 0; for (ii=0; ii < nidxs; ii++) { if (wtmp[ii] > 0) { /* printf("ii=%d, jj=%d, wtmp[%d]=%g\n", (int)ii, (int)jj, (int)ii, wtmp[ii]); */ xtmp[jj] = x[IDX_INDEX(cidxs,ii)]; // sure that xvalue is not NA wtmp[jj] = wtmp[ii]; wtotal += wtmp[jj]; jj++; } } x = xtmp; w = wtmp; nx = nxt; /* for (ii=0; ii < nx; ii++) printf("x[%d]=%g, w[%d]=%g\n", (int)ii, (double)x[ii], (int)ii, w[ii]); */ /* Early stopping? */ if (nx == 1) { res = (double)x[0]; Free(xtmp); Free(wtmp); return res; } /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */ /* All weights equal? Happens if +Inf were detected. */ /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */ if (equalweights) { half = (nx+1) / 2; /* printf("half=%d\n", (int)half); */ X_PSORT(x, nx, half); /* for (ii=0; ii < nx; ii++) printf("x[%d]=%g\n", (int)ii, (double)x[ii]); */ /* FIXME: Add support for ties here too */ if (nx % 2 == 1) { res = (double)x[half-1]; } else { X_PSORT(x, half, half-1); res = ((double)x[half-1] + (double)x[half]) / 2; } Free(xtmp); Free(wtmp); return res; } /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */ /* Sort x and calculated the cumulative sum of weights (normalize to */ /* one) according to the reordered vector. */ /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */ /* (a) Sort x */ idxs_int = Calloc(nx, int); for (ii = 0; ii < nx; ii++) idxs_int[ii] = ii; X_QSORT_I(x, idxs_int, 1, nx); /* (b) Normalized cumulative weights */ wcum = Calloc(nx, double); tmp_d2 = 0; /* Index where cumulative weight passed 1/2 */ half = nx+1; /* Default is last */ if (interpolate) { /* Adjust */ for (ii = 0; ii < nx; ii++) { tmp_d = w[idxs_int[ii]] / wtotal; tmp_d2 += tmp_d; wcum[ii] = tmp_d2 - (tmp_d/2); if (wcum[ii] >= 0.5) { half = ii; /* Early stopping - no need to continue */ break; } } } else { for (ii = 0; ii < nx; ii++) { tmp_d2 += w[idxs_int[ii]] / wtotal; wcum[ii] = tmp_d2; if (tmp_d2 > 0.5) { half = ii; /* Early stopping - no need to continue */ break; } } } Free(wtmp); Free(idxs_int); /* Two special cases where more than half of the total weight is at a) the first, or b) the last value */ if (half == 0 || half == nx) { res = (double)x[half]; Free(wcum); Free(xtmp); return res; } /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */ /* Alt 1: Linearly interpolated weighted median */ /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */ if (interpolate) { /* The width and the height of the "rectangle". */ dx = (double)(x[half] - x[half-1]); Dy = wcum[half] - wcum[half-1]; /* printf("dx=%g, Dy=%g\n", dx, Dy); */ /* The width and the height of the triangle which upper corner touches the level where the cumulative sum of weights *equals* half the total weight. */ dy = 0.5 - wcum[half]; dx = (dy/Dy) * dx; /* printf("dx=%g, dy=%g\n", dx, dy); */ /* The corresponding x value */ res = dx + x[half]; Free(wcum); Free(xtmp); return res; } /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */ /* Alt 2: Classical weighted median (tied or not) */ /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */ /* At this point we know that: 1) at most half the total weight is in the set x[1:half], 2) that the set x[(half+2):n] contains less than half the total weight The question is whether x[(half+1):n] contains *more* than half the total weight (try x=c(1,2,3), w=c(1,1,1)). If it is then we can be sure that x[half+1] is the weighted median we are looking for, otherwise it is any function of x[k:(half+1)]. */ wlow = wcum[half-1]; whigh = 1 - wlow; /* printf("half=%d, wtotal=%g, wlow=%g, whigh=%g, ties=%d\n", half, (double)wtotal, (double)wlow, (double)whigh, ties); printf("x[half+(-1:1)]=c(%g, %g, %g)\n", x[half-1-1], x[half-1], x[half-1+1]); */ if (whigh > 0.5) { /* printf("matrixStats2: Not a tie!\n"); */ /* Not a tie */ res = x[half]; } else { /* printf("matrixStats2: A tie!\n"); */ /* A tie! */ if (ties == 1) { /* weighted */ /* printf("ties=%d, half=%d, wlow*x[half]=%g, whigh*x[half+1]=%g\n", ties, half, wlow*x[half-1], whigh*x[half]); */ res = wlow*(double)x[half-1] + whigh*(double)x[half]; } else if (ties == 2) { /* min */ res = (double)x[half-1]; } else if (ties == 4) { /* max */ res = (double)x[half]; } else if (ties == 8) { /* mean */ res = ((double)x[half-1] + (double)x[half]) / 2; } else { error("Unknown value of argument 'ties': %d", ties); } } Free(wcum); Free(xtmp); return res; } /*************************************************************************** HISTORY: 2015-07-09 [DJ] o Supported subsetted computation. 2015-01-01 [HB] o Created. **************************************************************************/ matrixStats/src/colRanges_TYPE-template.h0000644000175100001440000001440512776336765020174 0ustar hornikusers/*********************************************************************** TEMPLATE: void colRanges_[rowsType][colsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int what, int narm, int hasna, X_C_TYPE *ans, int *is_counted Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD_NAME: the name of the resulting function - X_TYPE: 'i' or 'r' - ANS_TYPE: 'i' or 'r' Authors: Henrik Bengtsson. Copyright: Henrik Bengtsson, 2014 ***********************************************************************/ #include #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, [METHOD_NAME]) ANS_TYPE => (ANS_SXP, ANS_NA, ANS_C_TYPE, ANS_IN_C) */ #include "templates-types.h" RETURN_TYPE METHOD_NAME_ROWS_COLS(ARGUMENTS_LIST) { R_xlen_t ii, jj; R_xlen_t colBegin, idx; X_C_TYPE value, *mins = NULL, *maxs = NULL; #ifdef ROWS_TYPE ROWS_C_TYPE *crows = (ROWS_C_TYPE*) rows; #endif #ifdef COLS_TYPE COLS_C_TYPE *ccols = (COLS_C_TYPE*) cols; #endif /* Rprintf("(nrow,ncol)=(%d,%d), what=%d\n", nrow, ncol, what); */ /* If there are no missing values, don't try to remove them. */ if (hasna == FALSE) narm = FALSE; if (hasna) { for (jj=0; jj < ncols; jj++) is_counted[jj] = 0; /* Missing values */ if (what == 0) { /* colMins() */ mins = ans; for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); for (ii=0; ii < nrows; ii++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); value = R_INDEX_GET(x, idx, X_NA); if (X_ISNAN(value)) { if (!narm) { mins[jj] = value; is_counted[jj] = 1; /* Early stopping? */ #if X_TYPE == 'i' break; #elif X_TYPE == 'r' if (X_ISNA(value)) break; #endif } } else if (!is_counted[jj]) { mins[jj] = value; is_counted[jj] = 1; } else if (value < mins[jj]) { mins[jj] = value; } } } /* for (jj ...) */ #if X_TYPE == 'r' /* Handle zero non-missing values */ for (jj=0; jj < ncols; jj++) { if (!is_counted[jj]) { mins[jj] = R_PosInf; } } #endif } else if (what == 1) { /* colMaxs() */ maxs = ans; for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); for (ii=0; ii < nrows; ii++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); value = R_INDEX_GET(x, idx, X_NA); if (X_ISNAN(value)) { if (!narm) { maxs[jj] = value; is_counted[jj] = 1; /* Early stopping? */ #if X_TYPE == 'i' break; #elif X_TYPE == 'r' if (X_ISNA(value)) break; #endif } } else if (!is_counted[jj]) { maxs[jj] = value; is_counted[jj] = 1; } else if (value > maxs[jj]) { maxs[jj] = value; } } } /* for (jj ...) */ #if X_TYPE == 'r' /* Handle zero non-missing values */ for (jj=0; jj < ncols; jj++) { if (!is_counted[jj]) { maxs[jj] = R_NegInf; } } #endif } else if (what == 2) { /* colRanges() */ mins = ans; maxs = &ans[ncols]; for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); for (ii=0; ii < nrows; ii++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); value = R_INDEX_GET(x, idx, X_NA); if (X_ISNAN(value)) { if (!narm) { mins[jj] = value; maxs[jj] = value; is_counted[jj] = 1; /* Early stopping? */ #if X_TYPE == 'i' break; #elif X_TYPE == 'r' if (X_ISNA(value)) break; #endif } } else if (!is_counted[jj]) { mins[jj] = value; maxs[jj] = value; is_counted[jj] = 1; } else if (value < mins[jj]) { mins[jj] = value; } else if (value > maxs[jj]) { maxs[jj] = value; } } } /* for (jj ...) */ #if X_TYPE == 'r' /* Handle zero non-missing values */ for (jj=0; jj < ncols; jj++) { if (!is_counted[jj]) { mins[jj] = R_PosInf; maxs[jj] = R_NegInf; } } #endif } /* if (what ...) */ } else { /* No missing values */ if (what == 0) { /* colMins() */ mins = ans; /* Initiate results */ for (jj=0; jj < ncols; jj++) { mins[jj] = x[jj]; } for (jj=1; jj < ncols; jj++) { colBegin = COL_INDEX_NONA(ccols,jj) * nrow; for (ii=0; ii < nrows; ii++) { value = x[ROW_INDEX_NONA(crows,ii)+colBegin]; if (value < mins[jj]) mins[jj] = value; } } } else if (what == 1) { /* colMax() */ maxs = ans; /* Initiate results */ for (jj=0; jj < ncols; jj++) { maxs[jj] = x[jj]; } for (jj=1; jj < ncols; jj++) { colBegin = COL_INDEX_NONA(ccols,jj) * nrow; for (ii=0; ii < nrows; ii++) { value = x[ROW_INDEX_NONA(crows,ii)+colBegin]; if (value > maxs[jj]) maxs[jj] = value; } } } else if (what == 2) { /* colRanges()*/ mins = ans; maxs = &ans[ncols]; /* Initiate results */ for (jj=0; jj < ncols; jj++) { mins[jj] = x[jj]; maxs[jj] = x[jj]; } for (jj=1; jj < ncols; jj++) { colBegin = COL_INDEX_NONA(ccols,jj) * nrow; for (ii=0; ii < nrows; ii++) { value = x[ROW_INDEX_NONA(crows,ii)+colBegin]; if (value < mins[jj]) { mins[jj] = value; } else if (value > maxs[jj]) { maxs[jj] = value; } } } } /* if (what ...) */ } /* if (narm) */ } /*************************************************************************** HISTORY: 2015-06-07 [DJ] o Supported subsetted computation. 2014-11-16 [HB] o Created. **************************************************************************/ matrixStats/src/anyMissing_TYPE-template.h0000644000175100001440000000376212776336765020404 0ustar hornikusers/*********************************************************************** TEMPLATE: double anyMissing[idxsType](ARGUMENTS_LIST) ARGUMENTS_LIST: SEXP x, void *idxs, R_xlen_t nidxs ***********************************************************************/ #include #include "types.h" #include "templates-types.h" #ifndef CHECK_MISSING #define CHECK_MISSING(cond) \ for (ii=0; ii < nidxs; ++ii) { \ if (cond) return 1; \ } #endif RETURN_TYPE METHOD_NAME_IDXS(ARGUMENTS_LIST) { R_xlen_t ii; double *xdp; int *xip, *xlp; Rcomplex *xcp; #ifdef IDXS_TYPE IDXS_C_TYPE *cidxs = (IDXS_C_TYPE*) idxs; #endif switch (TYPEOF(x)) { case REALSXP: xdp = REAL(x); CHECK_MISSING(ISNAN(R_INDEX_GET(xdp, IDX_INDEX(cidxs,ii), NA_REAL))); break; case INTSXP: xip = INTEGER(x); CHECK_MISSING(R_INDEX_GET(xip, IDX_INDEX(cidxs,ii), NA_INTEGER) == NA_INTEGER); break; case LGLSXP: xlp = LOGICAL(x); CHECK_MISSING(R_INDEX_GET(xlp, IDX_INDEX(cidxs,ii), NA_LOGICAL) == NA_LOGICAL); break; case CPLXSXP: xcp = COMPLEX(x); #ifdef IDXS_TYPE CHECK_MISSING(IDX_INDEX(cidxs,ii) == NA_R_XLEN_T || ISNAN(xcp[IDX_INDEX_NONA(cidxs,ii)].r) || ISNAN(xcp[IDX_INDEX_NONA(cidxs,ii)].i)); #else CHECK_MISSING(ISNAN(xcp[ii].r) || ISNAN(xcp[ii].i)); #endif break; case STRSXP: #ifdef IDXS_TYPE CHECK_MISSING(IDX_INDEX(cidxs,ii) == NA_R_XLEN_T || STRING_ELT(x, IDX_INDEX_NONA(cidxs,ii)) == NA_STRING); #else CHECK_MISSING(STRING_ELT(x, ii) == NA_STRING); #endif break; case RAWSXP: /* no such thing as a raw NA; always FALSE */ break; default: break; } /* switch() */ return 0; } // anyMissing() /*************************************************************************** HISTORY: 2015-07-15 [DJ] o Avoid 'embedding a directive within macro arguments'. 2015-06-15 [DJ] o Created. **************************************************************************/ matrixStats/src/rowRanksWithTies.c0000644000175100001440000001555512776336765017076 0ustar hornikusers/*************************************************************************** Public methods: SEXP rowRanksWithTies(SEXP x, SEXP rows, SEXP cols, SEXP tiesMethod, SEXP byRow) Authors: Hector Corrada Bravo, Peter Langfelder and Henrik Bengtsson TO DO: Add support for missing values. **************************************************************************/ #include #include "utils.h" /* Template Ranks__ties() */ #define METHOD_TEMPLATE_H "rowRanksWithTies_TYPE_TIES-template.h" #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, ANS_C_TYPE *ans /***************************************************************** * ties.method = "min" *****************************************************************/ #define TIESMETHOD '0' /* min */ #define METHOD rowRanks_tiesMin #define MARGIN 'r' #define X_TYPE 'r' #include "templates-gen-matrix.h" #define MARGIN 'r' #define X_TYPE 'i' #include "templates-gen-matrix.h" #undef METHOD #define METHOD colRanks_tiesMin #define MARGIN 'c' #define X_TYPE 'r' #include "templates-gen-matrix.h" #define MARGIN 'c' #define X_TYPE 'i' #include "templates-gen-matrix.h" #undef METHOD #undef TIESMETHOD /***************************************************************** * ties.method = "max" *****************************************************************/ #define TIESMETHOD '1' /* max */ #define METHOD rowRanks_tiesMax #define MARGIN 'r' #define X_TYPE 'r' #include "templates-gen-matrix.h" #define MARGIN 'r' #define X_TYPE 'i' #include "templates-gen-matrix.h" #undef METHOD #define METHOD colRanks_tiesMax #define MARGIN 'c' #define X_TYPE 'r' #include "templates-gen-matrix.h" #define MARGIN 'c' #define X_TYPE 'i' #include "templates-gen-matrix.h" #undef METHOD #undef TIESMETHOD /***************************************************************** * ties.method = "average" *****************************************************************/ #define TIESMETHOD 'a' /* average */ #define METHOD rowRanks_tiesAverage #define MARGIN 'r' #define X_TYPE 'r' #include "templates-gen-matrix.h" #define MARGIN 'r' #define X_TYPE 'i' #include "templates-gen-matrix.h" #undef METHOD #define METHOD colRanks_tiesAverage #define MARGIN 'c' #define X_TYPE 'r' #include "templates-gen-matrix.h" #define MARGIN 'c' #define X_TYPE 'i' #include "templates-gen-matrix.h" #undef METHOD #undef TIESMETHOD /* Peter Langfelder's modifications: * byrow: 0 => rank columns, !0 => rank rows * tiesMethod: 1: maximum, 2: average, 3:minimum * The returned rank is a REAL matrix to accomodate average ranks */ SEXP rowRanksWithTies(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP tiesMethod, SEXP byRow) { int tiesmethod, byrow; SEXP ans = NILSXP; R_xlen_t nrow, ncol; /* Argument 'x' and 'dim': */ assertArgMatrix(x, dim, (R_TYPE_INT | R_TYPE_REAL), "x"); nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'tiesMethod': */ tiesmethod = asInteger(tiesMethod); if (tiesmethod < 1 || tiesmethod > 3) { error("Argument 'tiesMethod' is out of range [1,3]: %d", tiesmethod); } /* Argument 'rows' and 'cols': */ R_xlen_t nrows, ncols; int rowsType, colsType; void *crows = validateIndices(rows, nrow, 0, &nrows, &rowsType); void *ccols = validateIndices(cols, ncol, 0, &ncols, &colsType); /* Argument 'byRow': */ byrow = asLogical(byRow); /* Double matrices are more common to use. */ if (isReal(x)) { if (byrow) { switch (tiesmethod) { case 1: PROTECT(ans = allocMatrix(INTSXP, nrows, ncols)); rowRanks_tiesMax_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, INTEGER(ans)); UNPROTECT(1); break; case 2: PROTECT(ans = allocMatrix(REALSXP, nrows, ncols)); rowRanks_tiesAverage_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, REAL(ans)); UNPROTECT(1); break; case 3: PROTECT(ans = allocMatrix(INTSXP, nrows, ncols)); rowRanks_tiesMin_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, INTEGER(ans)); UNPROTECT(1); break; } /* switch */ } else { switch (tiesmethod) { case 1: PROTECT(ans = allocMatrix(INTSXP, nrows, ncols)); colRanks_tiesMax_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, INTEGER(ans)); UNPROTECT(1); break; case 2: PROTECT(ans = allocMatrix(REALSXP, nrows, ncols)); colRanks_tiesAverage_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, REAL(ans)); UNPROTECT(1); break; case 3: PROTECT(ans = allocMatrix(INTSXP, nrows, ncols)); colRanks_tiesMin_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, INTEGER(ans)); UNPROTECT(1); break; } /* switch */ } } else if (isInteger(x)) { if (byrow) { switch (tiesmethod) { case 1: PROTECT(ans = allocMatrix(INTSXP, nrows, ncols)); rowRanks_tiesMax_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, INTEGER(ans)); UNPROTECT(1); break; case 2: PROTECT(ans = allocMatrix(REALSXP, nrows, ncols)); rowRanks_tiesAverage_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, REAL(ans)); UNPROTECT(1); break; case 3: PROTECT(ans = allocMatrix(INTSXP, nrows, ncols)); rowRanks_tiesMin_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, INTEGER(ans)); UNPROTECT(1); break; } /* switch */ } else { switch (tiesmethod) { case 1: PROTECT(ans = allocMatrix(INTSXP, nrows, ncols)); colRanks_tiesMax_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, INTEGER(ans)); UNPROTECT(1); break; case 2: PROTECT(ans = allocMatrix(REALSXP, nrows, ncols)); colRanks_tiesAverage_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, REAL(ans)); UNPROTECT(1); break; case 3: PROTECT(ans = allocMatrix(INTSXP, nrows, ncols)); colRanks_tiesMin_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, INTEGER(ans)); UNPROTECT(1); break; } /* switch */ } } return(ans); } // rowRanksWithTies() /*************************************************************************** HISTORY: 2015-06-12 [DJ] o Supported subsetted computation. 2013-01-13 [HB] o Added argument 'tiesMethod' to rowRanks(). **************************************************************************/ matrixStats/src/rowRanges.c0000644000175100001440000001035312776336765015545 0ustar hornikusers/*************************************************************************** Public methods: SEXP rowRanges(SEXP x, ...) Authors: Henrik Bengtsson. Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD rowRanges #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int what, int narm, int hasna, X_C_TYPE *ans, int *is_counted #define X_TYPE 'i' #include "templates-gen-matrix.h" #define X_TYPE 'r' #include "templates-gen-matrix.h" SEXP rowRanges(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP what, SEXP naRm, SEXP hasNA) { SEXP ans = NILSXP, ans2 = NILSXP; int *mins, *maxs; double *mins2, *maxs2; int *is_counted, all_counted = 0; int what2, narm, hasna; R_xlen_t nrow, ncol, ii; /* Argument 'x' and 'dim': */ assertArgMatrix(x, dim, (R_TYPE_INT | R_TYPE_REAL), "x"); nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'what': */ if (length(what) != 1) error("Argument 'what' must be a single number."); if (!isNumeric(what)) error("Argument 'what' must be a numeric number."); what2 = asInteger(what); if (what2 < 0 || what2 > 2) error("Invalid value of 'what': %d", what2); /* Argument 'naRm': */ narm = asLogicalNoNA(naRm, "na.rm"); /* Argument 'hasNA': */ hasna = asLogicalNoNA(hasNA, "hasNA"); /* Argument 'rows' and 'cols': */ R_xlen_t nrows, ncols; int rowsType, colsType; void *crows = validateIndices(rows, nrow, 0, &nrows, &rowsType); void *ccols = validateIndices(cols, ncol, 0, &ncols, &colsType); is_counted = (int *) R_alloc(nrows, sizeof(int)); if (isReal(x)) { if (what2 == 2) { PROTECT(ans = allocMatrix(REALSXP, nrows, 2)); } else { PROTECT(ans = allocVector(REALSXP, nrows)); } rowRanges_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, what2, narm, hasna, REAL(ans), is_counted); UNPROTECT(1); } else if (isInteger(x)) { if (what2 == 2) { PROTECT(ans = allocMatrix(INTSXP, nrows, 2)); } else { PROTECT(ans = allocVector(INTSXP, nrows)); } rowRanges_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, what2, narm, hasna, INTEGER(ans), is_counted); /* Any entries with zero non-missing values? */ all_counted = 1; for (ii=0; ii < nrows; ii++) { if (!is_counted[ii]) { all_counted = 0; break; } } if (!all_counted) { /* Handle zero non-missing values */ /* Instead of return INTSXP, we must return REALSXP (to hold -Inf, and Inf) */ if (what2 == 0) { PROTECT(ans2 = allocVector(REALSXP, nrows)); mins = INTEGER(ans); mins2 = REAL(ans2); for (ii=0; ii < nrows; ii++) { if (is_counted[ii]) { mins2[ii] = (double)mins[ii]; } else { mins2[ii] = R_PosInf; } } UNPROTECT(1); /* ans2 */ } else if (what2 == 1) { PROTECT(ans2 = allocVector(REALSXP, nrows)); maxs = INTEGER(ans); maxs2 = REAL(ans2); for (ii=0; ii < nrows; ii++) { if (is_counted[ii]) { maxs2[ii] = (double)maxs[ii]; } else { maxs2[ii] = R_NegInf; } } UNPROTECT(1); /* ans2 */ } else if (what2 == 2) { PROTECT(ans2 = allocMatrix(REALSXP, nrows, 2)); mins = INTEGER(ans); maxs = &INTEGER(ans)[nrows]; mins2 = REAL(ans2); maxs2 = &REAL(ans2)[nrows]; for (ii=0; ii < nrows; ii++) { if (is_counted[ii]) { mins2[ii] = (double)mins[ii]; maxs2[ii] = (double)maxs[ii]; } else { mins2[ii] = R_PosInf; maxs2[ii] = R_NegInf; } } UNPROTECT(1); /* ans2 */ } ans = ans2; } UNPROTECT(1); /* ans */ } return(ans); } // rowRanges() /*************************************************************************** HISTORY: 2015-06-07 [DJ] o Supported subsetted computation. 2014-11-16 [HB] o Created. **************************************************************************/ matrixStats/src/meanOver.c0000644000175100001440000000327212776336765015354 0ustar hornikusers/*************************************************************************** Public methods: SEXP meanOver(SEXP x, SEXP idxs, SEXP naRm, SEXP refine) Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD meanOver #define RETURN_TYPE double #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nx, void *idxs, R_xlen_t nidxs, int narm, int refine #define X_TYPE 'i' #include "templates-gen-vector.h" #define X_TYPE 'r' #include "templates-gen-vector.h" SEXP meanOver(SEXP x, SEXP idxs, SEXP naRm, SEXP refine) { SEXP ans; R_xlen_t nx; int narm, refine2; double avg = NA_REAL; /* Argument 'x': */ assertArgVector(x, (R_TYPE_INT | R_TYPE_REAL), "x"); nx = xlength(x); /* Argument 'naRm': */ narm = asLogicalNoNA(naRm, "na.rm"); /* Argument 'refine': */ refine2 = asLogicalNoNA(refine, "refine"); /* Argument 'idxs': */ R_xlen_t nidxs; int idxsType; void *cidxs = validateIndices(idxs, nx, 1, &nidxs, &idxsType); /* Double matrices are more common to use. */ if (isReal(x)) { avg = meanOver_Real[idxsType](REAL(x), nx, cidxs, nidxs, narm, refine2); } else if (isInteger(x)) { avg = meanOver_Integer[idxsType](INTEGER(x), nx, cidxs, nidxs, narm, refine2); } /* Return results */ PROTECT(ans = allocVector(REALSXP, 1)); REAL(ans)[0] = avg; UNPROTECT(1); return(ans); } // meanOver() /*************************************************************************** HISTORY: 2015-07-04 [DJ] o Supported subsetted computation. 2014-11-02 [HB] o Created. **************************************************************************/ matrixStats/src/rowCumsums.c0000644000175100001440000000365112776336765015765 0ustar hornikusers/*************************************************************************** Public methods: SEXP rowCumsums(SEXP x, ...) Authors: Henrik Bengtsson Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD rowCumsums #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int byrow, ANS_C_TYPE *ans #define X_TYPE 'i' #include "templates-gen-matrix.h" #define X_TYPE 'r' #include "templates-gen-matrix.h" SEXP rowCumsums(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP byRow) { int byrow; SEXP ans = NILSXP; R_xlen_t nrow, ncol; /* Argument 'x' and 'dim': */ assertArgMatrix(x, dim, (R_TYPE_INT | R_TYPE_REAL), "x"); nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'rows' and 'cols': */ R_xlen_t nrows, ncols; int rowsType, colsType; void *crows = validateIndices(rows, nrow, 0, &nrows, &rowsType); void *ccols = validateIndices(cols, ncol, 0, &ncols, &colsType); /* Argument 'byRow': */ byrow = asLogical(byRow); /* Double matrices are more common to use. */ if (isReal(x)) { PROTECT(ans = allocMatrix(REALSXP, nrows, ncols)); rowCumsums_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, byrow, REAL(ans)); UNPROTECT(1); } else if (isInteger(x)) { PROTECT(ans = allocMatrix(INTSXP, nrows, ncols)); rowCumsums_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, byrow, INTEGER(ans)); UNPROTECT(1); } return(ans); } /* rowCumsums() */ /*************************************************************************** HISTORY: 2015-06-07 [DJ] o Supported subsetted computation. 2014-11-26 [HB] o Created from rowVars.c. **************************************************************************/ matrixStats/src/rowOrderStats_TYPE-template.h0000644000175100001440000000643212776336765021102 0ustar hornikusers/*********************************************************************** TEMPLATE: void rowOrderStats_[rowsType][colsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, R_xlen_t qq, X_C_TYPE *ans Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD_NAME: the name of the resulting function - X_TYPE: 'i' or 'r' - ANS_TYPE: 'i' or 'r' Authors: Adopted from rowQ() by R. Gentleman. Template by Henrik Bengtsson. Copyright: Henrik Bengtsson, 2007-2014 ***********************************************************************/ #include #include #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, [METHOD_NAME]) ANS_TYPE => (ANS_SXP, ANS_NA, ANS_C_TYPE, ANS_IN_C) */ #include "templates-types.h" RETURN_TYPE METHOD_NAME_ROWS_COLS(ARGUMENTS_LIST) { R_xlen_t ii, jj; R_xlen_t *colOffset, rowIdx; X_C_TYPE *values; #ifdef ROWS_TYPE ROWS_C_TYPE *crows = (ROWS_C_TYPE*) rows; // Check missing rows for (ii=0; ii < nrows; ++ii) { if (ROW_INDEX(crows,ii) == NA_R_XLEN_T) break; } if (ii < nrows && ncols > 0) { error("Argument 'rows' must not contain missing value"); } #endif #ifdef COLS_TYPE COLS_C_TYPE *ccols = (COLS_C_TYPE*) cols; // Check missing cols for (jj=0; jj < ncols; ++jj) { if (COL_INDEX(ccols,jj) == NA_R_XLEN_T) break; } if (jj < ncols && nrows > 0) { error("Argument 'cols' must not contain missing value"); } #endif /* R allocate memory for the 'values'. This will be taken care of by the R garbage collector later on. */ values = (X_C_TYPE *) R_alloc(ncols, sizeof(X_C_TYPE)); /* Pre-calculate the column offsets */ colOffset = (R_xlen_t *) R_alloc(ncols, sizeof(R_xlen_t)); for (jj=0; jj < ncols; jj++) colOffset[jj] = COL_INDEX_NONA(ccols,jj) * nrow; for (ii=0; ii < nrows; ii++) { rowIdx = ROW_INDEX_NONA(crows,ii); for (jj=0; jj < ncols; jj++) values[jj] = x[rowIdx + colOffset[jj]]; /* Sort vector of length 'ncol' up to position 'qq'. "...partial sorting: they permute x so that x[qq] is in the correct place with smaller values to the left, larger ones to the right." */ X_PSORT(values, ncols, qq); ans[ii] = values[qq]; } } /*************************************************************************** HISTORY: 2015-07-08 [DJ] o Supported subsetted computation. 2014-11-06 [HB] o CLEANUP: Moving away from R data types in low-level C functions. 2013-01-13 [HB] o Merged rowOrderStatsReal() and rowOrderStatsInteger() into one rowOrderStats_() templated function. 2009-02-04 [HB] o BUG FIX: For some errors in rowOrderStats(), the stack would not become UNPROTECTED before calling error. 2008-03-25 [HB] o Renamed from 'rowQuantiles' to 'rowOrderStats'. 2007-08-10 [HB] o Removed arguments for NAs since rowOrderStats() still don't support it. 2005-11-24 [HB] o Cool, it works and compiles nicely. o Preallocate colOffset to speed up things even more. o Added more comments and error checking. o Adopted from rowQ() in Biobase of Bioconductor. **************************************************************************/ matrixStats/src/rowCumsums_TYPE-template.h0000644000175100001440000001037312776336765020443 0ustar hornikusers/*********************************************************************** TEMPLATE: void rowCumsums_[rowsType][colsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int byrow, ANS_C_TYPE *ans Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD: the name of the resulting function - X_TYPE: 'i' or 'r' Authors: Henrik Bengtsson. Copyright: Henrik Bengtsson, 2014 ***********************************************************************/ #include #include #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, X_ISNAN, [METHOD_NAME]) */ #include "templates-types.h" RETURN_TYPE METHOD_NAME_ROWS_COLS(ARGUMENTS_LIST) { R_xlen_t ii, jj, kk, kk_prev, idx; R_xlen_t colBegin; X_C_TYPE xvalue; LDOUBLE value; #ifdef ROWS_TYPE ROWS_C_TYPE *crows = (ROWS_C_TYPE*) rows; #endif #ifdef COLS_TYPE COLS_C_TYPE *ccols = (COLS_C_TYPE*) cols; #endif #if ANS_TYPE == 'i' double R_INT_MIN_d = (double)R_INT_MIN, R_INT_MAX_d = (double)R_INT_MAX; /* OK, i.e. no integer overflow yet? */ int warn = 0, ok, *oks = NULL; #endif if (ncols == 0 || nrows == 0) return; if (byrow) { #if ANS_TYPE == 'i' oks = (int *) R_alloc(nrows, sizeof(int)); #endif colBegin = R_INDEX_OP(COL_INDEX(ccols,0), *, nrow); for (kk=0; kk < nrows; kk++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,kk)); xvalue = R_INDEX_GET(x, idx, X_NA); ans[kk] = (ANS_C_TYPE) xvalue; #if ANS_TYPE == 'i' oks[kk] = !X_ISNA(xvalue); #endif } kk_prev = 0; for (jj=1; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); for (ii=0; ii < nrows; ii++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); #if ANS_TYPE == 'i' if (oks[ii]) { /* Missing value? */ if (X_ISNA(xvalue)) { oks[ii] = 0; ans[kk] = ANS_NA; } else { value = (LDOUBLE) ans[kk_prev] + (LDOUBLE) xvalue; /* Integer overflow? */ if (value < R_INT_MIN_d || value > R_INT_MAX_d) { oks[ii] = 0; warn = 1; ans[kk] = ANS_NA; } else { ans[kk] = (ANS_C_TYPE) value; } } } else { ans[kk] = ANS_NA; } #else ans[kk] = (ANS_C_TYPE) ((LDOUBLE) ans[kk_prev] + (LDOUBLE) xvalue); #endif kk++; kk_prev++; R_CHECK_USER_INTERRUPT(kk); } /* for (ii ...) */ } /* for (jj ...) */ } else { kk = 0; for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); value = 0; #if ANS_TYPE == 'i' ok = 1; #endif for (ii=0; ii < nrows; ii++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); #if ANS_TYPE == 'i' if (ok) { /* Missing value? */ if (X_ISNA(xvalue)) { ok = 0; ans[kk] = ANS_NA; } else { value += (LDOUBLE) xvalue; /* Integer overflow? */ if (value < R_INT_MIN_d || value > R_INT_MAX_d) { ok = 0; warn = 1; ans[kk] = ANS_NA; } else { ans[kk] = (ANS_C_TYPE) value; } } } else { ans[kk] = ANS_NA; } #else value += xvalue; ans[kk] = (ANS_C_TYPE) value; #endif kk++; R_CHECK_USER_INTERRUPT(kk); } /* for (ii ...) */ } /* for (jj ...) */ } /* if (byrow) */ #if ANS_TYPE == 'i' /* Warn on integer overflow? */ if (warn) { warning("Integer overflow. Detected one or more elements whose absolute values were out of the range [%d,%d] that can be used to for integers. Such values are set to NA_integer_.", R_INT_MIN, R_INT_MAX); } #endif } /*************************************************************************** HISTORY: 2015-06-07 [DJ] o Supported subsetted computation. 2014-11-26 [HB] o Created from rowVars_TYPE-template.h. **************************************************************************/ matrixStats/src/logSumExp.c0000644000175100001440000000317412776336765015524 0ustar hornikusers/*************************************************************************** Public methods: SEXP logSumExp(SEXP lx, SEXP idxs, SEXP naRm, SEXP hasNA) Arguments: lx : numeric vector idxs : subsetting indices naRm : a logical scalar hasNA: a logical scalar Authors: Henrik Bengtsson Copyright Henrik Bengtsson, 2013 **************************************************************************/ #include #include #include "utils.h" #define METHOD logSumExp #define METHOD_NAME logSumExp_double #define RETURN_TYPE double #define ARGUMENTS_LIST double *x, void *idxs, R_xlen_t nidxs, int narm, int hasna, R_xlen_t by, double *xx #include "templates-gen-vector.h" SEXP logSumExp(SEXP lx, SEXP idxs, SEXP naRm, SEXP hasNA) { int narm, hasna; /* Argument 'lx': */ assertArgVector(lx, (R_TYPE_REAL), "lx"); /* Argument 'naRm': */ narm = asLogicalNoNA(naRm, "na.rm"); /* Argument 'hasNA': */ hasna = asLogicalNoNA(hasNA, "hasNA"); /* Argument 'idxs': */ R_xlen_t nidxs; int idxsType; void *cidxs = validateIndices(idxs, xlength(lx), 1, &nidxs, &idxsType); return(Rf_ScalarReal(logSumExp_double[idxsType](REAL(lx), cidxs, nidxs, narm, hasna, 0, NULL))); } /* logSumExp() */ /*************************************************************************** HISTORY: 2015-06-11 [DJ] o Supported subsetted computation. 2013-05-02 [HB] o BUG FIX: Incorrectly used ISNAN() on an int variable as caught by the 'cc' compiler on Solaris. Reported by Brian Ripley upon CRAN submission. 2013-04-30 [HB] o Created. **************************************************************************/ matrixStats/src/colCounts_TYPE-template.h0000644000175100001440000001244412776336765020231 0ustar hornikusers/*********************************************************************** TEMPLATE: void colCounts_[rowsType][colsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, X_C_TYPE value, int what, int narm, int hasna, int *ans Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD_NAME: the name of the resulting function - X_TYPE: 'i', 'r', or 'l' Copyright: Henrik Bengtsson, 2014 ***********************************************************************/ #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, [METHOD_NAME]) */ #include "templates-types.h" RETURN_TYPE METHOD_NAME_ROWS_COLS(ARGUMENTS_LIST) { R_xlen_t ii, jj; R_xlen_t colBegin, idx; int count; X_C_TYPE xvalue; #ifdef ROWS_TYPE ROWS_C_TYPE *crows = (ROWS_C_TYPE*) rows; #endif #ifdef COLS_TYPE COLS_C_TYPE *ccols = (COLS_C_TYPE*) cols; #endif if (what == 0L) { /* all */ /* Count missing values? [sic!] */ if (X_ISNAN(value)) { for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); count = 1; for (ii=0; ii < nrows; ii++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); if (!X_ISNAN(R_INDEX_GET(x, idx, X_NA))) { count = 0; /* Found another value! Early stopping */ break; } } ans[jj] = count; } } else { for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); count = 1; for (ii=0; ii < nrows; ii++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); if (xvalue == value) { } else if (narm && X_ISNAN(xvalue)) { /* Skip */ } else if (!narm && X_ISNAN(xvalue)) { /* Early stopping is not possible, because if we do find an element that is not 'value' later, then we know for sure that all = FALSE regardless of missing values. In other words, at this point the answer can be either NA or FALSE.*/ count = NA_INTEGER; } else { count = 0; /* Found another value! Early stopping */ break; } } /* for (ii ...) */ ans[jj] = count; } /* for (jj ...) */ } /* if (X_ISNAN(value)) */ } else if (what == 1L) { /* any */ /* Count missing values? [sic!] */ if (X_ISNAN(value)) { for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); count = 0; for (ii=0; ii < nrows; ii++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); if (X_ISNAN(R_INDEX_GET(x, idx, X_NA))) { count = 1; /* Found value! Early stopping */ break; } } ans[jj] = count; } } else { for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); count = 0; for (ii=0; ii < nrows; ii++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); if (xvalue == value) { count = 1; /* Found value! Early stopping */ break; } else if (narm && X_ISNAN(xvalue)) { /* Skipping */ } else if (!narm && X_ISNAN(xvalue)) { /* Early stopping is not possible, because if we do find an element that is 'value' later, then we know for sure that any = TRUE regardless of missing values. In other words, at this point the answer can be either NA or TRUE.*/ count = NA_INTEGER; } } /* for (ii ...) */ ans[jj] = count; } /* for (jj ...) */ } /* if (X_ISNAN(value)) */ } else if (what == 2L) { /* count */ /* Count missing values? [sic!] */ if (X_ISNAN(value)) { for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); count = 0; for (ii=0; ii < nrows; ii++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); if (X_ISNAN(R_INDEX_GET(x, idx, X_NA))) { ++count; } } ans[jj] = count; } } else { for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); count = 0; for (ii=0; ii < nrows; ii++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); if (xvalue == value) { ++count; } else if (!narm && X_ISNAN(xvalue)) { count = NA_INTEGER; /* Early stopping */ break; } } /* for (ii ...) */ ans[jj] = count; } /* for (jj ...) */ } /* if (X_ISNAN(value)) */ } /* if (what) */ } /*************************************************************************** HISTORY: 2015-04-18 [DJ] o Supported subsetted computation. 2014-11-14 [HB] o Created colCounts() templates from rowCounts() templates. **************************************************************************/ matrixStats/src/rowCumMinMaxs_TYPE-template.h0000644000175100001440000000732412776336765021032 0ustar hornikusers/*********************************************************************** TEMPLATE: void rowCummins_[rowsType][colsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int byrow, ANS_C_TYPE *ans Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD: the name of the resulting function - X_TYPE: 'i' or 'r' Authors: Henrik Bengtsson. Copyright: Henrik Bengtsson, 2014 ***********************************************************************/ #include #include #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, X_ISNAN, [METHOD_NAME]) */ #include "templates-types.h" #if COMP == '<' #define OP < #elif COMP == '>' #define OP > #endif RETURN_TYPE METHOD_NAME_ROWS_COLS(ARGUMENTS_LIST) { R_xlen_t ii, jj, kk, kk_prev, idx; R_xlen_t colBegin; ANS_C_TYPE value; int ok; int *oks = NULL; #ifdef ROWS_TYPE ROWS_C_TYPE *crows = (ROWS_C_TYPE*) rows; #endif #ifdef COLS_TYPE COLS_C_TYPE *ccols = (COLS_C_TYPE*) cols; #endif if (ncols == 0 || nrows == 0) return; if (byrow) { oks = (int *) R_alloc(nrows, sizeof(int)); colBegin = R_INDEX_OP(COL_INDEX(ccols,0), *, nrow); for (kk=0; kk < nrows; kk++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,kk)); value = (ANS_C_TYPE) R_INDEX_GET(x, idx, X_NA); if (ANS_ISNAN(value)) { oks[kk] = 0; value = ANS_NA; ans[kk] = ANS_NA; } else { oks[kk] = 1; ans[kk] = value; } } kk_prev = 0; for (jj=1; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); for (ii=0; ii < nrows; ii++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); value = (ANS_C_TYPE) R_INDEX_GET(x, idx, X_NA); if (oks[ii]) { if (ANS_ISNAN(value)) { oks[ii] = 0; ans[kk] = ANS_NA; } else { if (value OP ans[kk_prev]) { ans[kk] = value; } else { ans[kk] = (ANS_C_TYPE) ans[kk_prev]; } } } else { ans[kk] = ANS_NA; } kk++; kk_prev++; R_CHECK_USER_INTERRUPT(kk); } /* for (ii ...) */ } /* for (jj ...) */ } else { kk = 0; for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,0)); value = (ANS_C_TYPE) R_INDEX_GET(x, idx, X_NA); if (ANS_ISNAN(value)) { ok = 0; value = ANS_NA; ans[kk] = ANS_NA; } else { ok = 1; ans[kk] = value; } kk_prev = kk; kk++; for (ii=1; ii < nrows; ii++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); value = (ANS_C_TYPE) R_INDEX_GET(x, idx, X_NA); if (ok) { if (ANS_ISNAN(value)) { ok = 0; value = ANS_NA; ans[kk] = ANS_NA; } else { if (value OP ans[kk_prev]) { ans[kk] = value; } else { ans[kk] = (ANS_C_TYPE) ans[kk_prev]; } } kk++; kk_prev++; } else { ans[kk] = ANS_NA; kk++; } R_CHECK_USER_INTERRUPT(kk); } /* for (ii ...) */ } /* for (jj ...) */ } /* if (byrow) */ } #undef OP /*************************************************************************** HISTORY: 2015-06-07 [DJ] o Supported subsetted computation. 2014-11-26 [HB] o Created from rowVars_TYPE-template.h. **************************************************************************/ matrixStats/src/weightedMedian.c0000644000175100001440000000417112776336765016515 0ustar hornikusers/*************************************************************************** Public methods: SEXP weightedMedian(SEXP x, SEXP w, SEXP idxs, SEXP naRm, SEXP interpolate, SEXP ties) Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #include #define METHOD weightedMedian #define RETURN_TYPE double #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nx, double *w, void *idxs, R_xlen_t nidxs, int narm, int interpolate, int ties #define X_TYPE 'i' #include "templates-gen-vector.h" #define X_TYPE 'r' #include "templates-gen-vector.h" SEXP weightedMedian(SEXP x, SEXP w, SEXP idxs, SEXP naRm, SEXP interpolate, SEXP ties) { SEXP ans; int narm, interpolate2, ties2; double mu = NA_REAL; R_xlen_t nx, nw; /* Argument 'x': */ assertArgVector(x, (R_TYPE_INT | R_TYPE_REAL), "x"); nx = xlength(x); /* Argument 'x': */ assertArgVector(w, (R_TYPE_REAL), "w"); nw = xlength(w); if (nx != nw) { error("Argument 'x' and 'w' are of different lengths: %d != %d", nx, nw); } /* Argument 'naRm': */ narm = asLogicalNoNA(naRm, "na.rm"); /* Argument 'interpolate': */ interpolate2 = asLogicalNoNA(interpolate, "interpolate"); /* Argument 'idxs': */ R_xlen_t nidxs; int idxsType; void *cidxs = validateIndices(idxs, nx, 1, &nidxs, &idxsType); /* Argument 'ties': */ ties2 = asInteger(ties); /* Double matrices are more common to use. */ if (isReal(x)) { mu = weightedMedian_Real[idxsType](REAL(x), nx, REAL(w), cidxs, nidxs, narm, interpolate2, ties2); } else if (isInteger(x)) { mu = weightedMedian_Integer[idxsType](INTEGER(x), nx, REAL(w), cidxs, nidxs, narm, interpolate2, ties2); } /* Return results */ PROTECT(ans = allocVector(REALSXP, 1)); REAL(ans)[0] = mu; UNPROTECT(1); return(ans); } // weightedMedian() /*************************************************************************** HISTORY: 2015-07-09 [DJ] o Supported subsetted computation. 2015-01-01 [HB] o Created. **************************************************************************/ matrixStats/src/weightedMean_TYPE-template.h0000644000175100001440000000534312776336765020661 0ustar hornikusers/*********************************************************************** TEMPLATE: double weightedMean_[idxsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nx, double *w, void *idxs, R_xlen_t nidxs, int narm, int refine Copyright: Henrik Bengtsson, 2014 ***********************************************************************/ #include #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, [METHOD_NAME]) */ #include "templates-types.h" #include RETURN_TYPE METHOD_NAME_IDXS(ARGUMENTS_LIST) { X_C_TYPE value; double weight; R_xlen_t i; LDOUBLE sum = 0, wtotal = 0; LDOUBLE avg = R_NaN; #ifdef IDXS_TYPE IDXS_C_TYPE *cidxs = (IDXS_C_TYPE*) idxs; #endif for (i=0; i < nidxs; i++) { weight = R_INDEX_GET(w, IDX_INDEX(cidxs,i), NA_REAL); /* Skip or early stopping? */ if (weight == 0) { continue; } value = R_INDEX_GET(x, IDX_INDEX(cidxs,i), X_NA); #if X_TYPE == 'i' if (X_ISNAN(value)) { /* Skip or early stopping? */ if (narm) { continue; } else { sum = R_NaReal; break; } } else { sum += (LDOUBLE)weight * (LDOUBLE)value; wtotal += weight; } #elif X_TYPE == 'r' if (!narm) { sum += (LDOUBLE)weight * (LDOUBLE)value; wtotal += weight; /* Early stopping? Special for long LDOUBLE vectors */ if (i % 1048576 == 0 && ISNAN(sum)) break; } else if (!X_ISNAN(value)) { sum += (LDOUBLE)weight * (LDOUBLE)value; wtotal += weight; } #endif } /* for (i ...) */ if (wtotal > DOUBLE_XMAX || wtotal < -DOUBLE_XMAX) { avg = R_NaN; } else if (sum > DOUBLE_XMAX) { avg = R_PosInf; } else if (sum < -DOUBLE_XMAX) { avg = R_NegInf; } else { avg = sum / wtotal; #if X_TYPE == 'r' /* Extra precision by summing over residuals? */ if (refine && R_FINITE(avg)) { sum = 0; for (i=0; i < nidxs; i++) { weight = R_INDEX_GET(w, IDX_INDEX(cidxs,i), NA_REAL); /* Skip? */ if (weight == 0) { continue; } value = R_INDEX_GET(x, IDX_INDEX(cidxs,i), X_NA); if (!narm) { sum += (LDOUBLE)weight * (value - avg); /* Early stopping? Special for long LDOUBLE vectors */ if (i % 1048576 == 0 && ISNAN(sum)) break; } else if (!ISNAN(value)) { sum += (LDOUBLE)weight * (value - avg); } } avg += (sum / wtotal); } #endif } return (double)avg; } /*************************************************************************** HISTORY: 2015-06-07 [DJ] o Supported subsetted computation. 2014-12-08 [HB] o Created. **************************************************************************/ matrixStats/src/rowCounts_TYPE-template.h0000644000175100001440000001334712776336765020266 0ustar hornikusers/*********************************************************************** TEMPLATE: void rowCounts_[ROWS_TYPE][COLS_TYPE](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, X_C_TYPE value, int what, int narm, int hasna, int *ans Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD_NAME: the name of the resulting function - X_TYPE: 'i', 'r', or 'l' Copyright: Henrik Bengtsson, 2014 ***********************************************************************/ #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, [METHOD_NAME]) */ #include "templates-types.h" RETURN_TYPE METHOD_NAME_ROWS_COLS(ARGUMENTS_LIST) { R_xlen_t ii, jj; R_xlen_t colBegin, idx; int count; X_C_TYPE xvalue; #ifdef ROWS_TYPE ROWS_C_TYPE *crows = (ROWS_C_TYPE*) rows; #endif #ifdef COLS_TYPE COLS_C_TYPE *ccols = (COLS_C_TYPE*) cols; #endif if (what == 0) { /* all */ for (ii=0; ii < nrows; ii++) ans[ii] = 1; /* Count missing values? [sic!] */ if (X_ISNAN(value)) { for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); for (ii=0; ii < nrows; ii++) { /* Skip? */ if (ans[ii]) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); if (!X_ISNAN(xvalue)) { ans[ii] = 0; /* Found another value! Skip from now on */ } } } } } else { for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); for (ii=0; ii < nrows; ii++) { /* Skip? */ if (ans[ii]) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); if (xvalue == value) { } else if (narm && X_ISNAN(xvalue)) { /* Skip */ } else if (!narm && X_ISNAN(xvalue)) { /* Early stopping is not possible, because if we do find an element that is not 'value' later, then we know for sure that all = FALSE regardless of missing values. In other words, at this point the answer can be either NA or FALSE.*/ ans[ii] = NA_INTEGER; } else { /* Found another value! Skip from now on */ ans[ii] = 0; } } } /* for (ii ...) */ } /* for (jj ...) */ } } else if (what == 1) { /* any */ for (ii=0; ii < nrows; ii++) ans[ii] = 0; /* Count missing values? [sic!] */ if (X_ISNAN(value)) { for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); for (ii=0; ii < nrows; ii++) { /* Skip? */ if (!ans[ii]) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); if (X_ISNAN(xvalue)) { ans[ii] = 1; /* Found value! Skip from now on */ } } } } } else { for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); for (ii=0; ii < nrows; ii++) { /* Skip? */ if (ans[ii] == 0 || ans[ii] == NA_INTEGER) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); if (xvalue == value) { /* Found value! Skip from now on */ ans[ii] = 1; } else if (narm && X_ISNAN(xvalue)) { /* Skip */ } else if (!narm && X_ISNAN(xvalue)) { /* Early stopping is not possible, because if we do find an element that is 'value' later, then we know for sure that any = TRUE regardless of missing values. In other words, at this point the answer can be either NA or TRUE.*/ ans[ii] = NA_INTEGER; } } } /* for (ii ...) */ } /* for (jj ...) */ } } else if (what == 2) { /* count */ for (ii=0; ii < nrows; ii++) ans[ii] = 0; /* Count missing values? [sic!] */ if (X_ISNAN(value)) { for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); for (ii=0; ii < nrows; ii++) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); if (X_ISNAN(xvalue)) ans[ii] = ans[ii] + 1; } } } else { for (jj=0; jj < ncols; jj++) { colBegin = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); for (ii=0; ii < nrows; ii++) { count = ans[ii]; /* Nothing more to do on this row? */ if (count == NA_INTEGER) continue; idx = R_INDEX_OP(colBegin, +, ROW_INDEX(crows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); if (xvalue == value) { ans[ii] = count + 1; } else { if (!narm && X_ISNAN(xvalue)) { ans[ii] = NA_INTEGER; continue; } } } /* for (ii ...) */ } /* for (jj ...) */ } } /* if (what) */ } /*************************************************************************** HISTORY: 2015-04-13 [DJ] o Supported subsetted computation. 2014-11-06 [HB] o CLEANUP: Moving away from R data types in low-level C functions. 2014-11-01 [HB] o SPEEDUP: Now using ansp = INTEGER(ans) once and then querying/assigning 'ansp[i]' instead of INTEGER(ans)[i]. 2014-06-02 [HB] o Created. **************************************************************************/ matrixStats/src/meanOver_TYPE-template.h0000644000175100001440000000436612776336765020040 0ustar hornikusers/*********************************************************************** TEMPLATE: double meanOver_[idxsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nx, int *idxs, R_xlen_t nidxs, int narm, int refine Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD_NAME: the name of the resulting function - X_TYPE: 'i' or 'r' Copyright: Henrik Bengtsson, 2014 ***********************************************************************/ #include #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, [METHOD_NAME]) */ #include "templates-types.h" #include RETURN_TYPE METHOD_NAME_IDXS(ARGUMENTS_LIST) { X_C_TYPE value; R_xlen_t ii; LDOUBLE sum = 0, avg = R_NaN; #if X_TYPE == 'r' LDOUBLE rsum = 0; #endif R_xlen_t count = 0; #ifdef IDXS_TYPE IDXS_C_TYPE *cidxs = (IDXS_C_TYPE*) idxs; #endif for (ii=0; ii < nidxs; ++ii) { value = R_INDEX_GET(x, IDX_INDEX(cidxs,ii), X_NA); #if X_TYPE == 'i' if (!X_ISNAN(value)) { sum += (LDOUBLE)value; ++count; } else if (!narm) { sum = R_NaReal; break; } #elif X_TYPE == 'r' if (!narm) { sum += (LDOUBLE)value; ++count; if (ii % 1048576 == 0 && !R_FINITE(sum)) break; } else if (!ISNAN(value)) { sum += (LDOUBLE)value; ++count; } #endif } /* for (i ...) */ if (sum > DOUBLE_XMAX) { avg = R_PosInf; } else if (sum < -DOUBLE_XMAX) { avg = R_NegInf; } else { avg = sum / count; /* Extra precision by summing over residuals? */ #if X_TYPE == 'r' if (refine && R_FINITE(avg)) { for (ii=0; ii < nidxs; ++ii) { value = R_INDEX_GET(x, IDX_INDEX(cidxs,ii), X_NA); if (!narm || !ISNAN(value)) { rsum += (LDOUBLE)(value - avg); } } avg += (rsum / count); } #endif } return (double)avg; } /*************************************************************************** HISTORY: 2015-07-04 [DJ] o Supported subsetted computation. 2014-11-06 [HB] o CLEANUP: Now meanOver_() uses only basic C types. 2014-11-02 [HB] o Created. **************************************************************************/ matrixStats/src/init.c0000644000175100001440000000225212776336765014540 0ustar hornikusers#include #include #include "api.h" #define CALLDEF(name, n) {#name, (DL_FUNC) &name, n} static R_CallMethodDef callMethods[] = { CALLDEF(allocArray2, 2), CALLDEF(allocMatrix2, 3), CALLDEF(allocVector2, 2), CALLDEF(anyMissing, 2), CALLDEF(binCounts, 3), CALLDEF(binMeans, 5), CALLDEF(colCounts, 8), CALLDEF(colOrderStats, 5), CALLDEF(colRanges, 7), CALLDEF(count, 6), CALLDEF(diff2, 4), CALLDEF(indexByRow, 2), CALLDEF(logSumExp, 4), CALLDEF(meanOver, 4), CALLDEF(productExpSumLog, 4), CALLDEF(psortKM, 3), CALLDEF(rowCounts, 8), CALLDEF(rowCummaxs, 5), CALLDEF(rowCummins, 5), CALLDEF(rowCumprods, 5), CALLDEF(rowCumsums, 5), CALLDEF(rowDiffs, 7), CALLDEF(rowLogSumExps, 7), CALLDEF(rowMads, 8), CALLDEF(rowMedians, 7), CALLDEF(rowOrderStats, 5), CALLDEF(rowRanges, 7), CALLDEF(rowRanksWithTies, 6), CALLDEF(rowVars, 7), CALLDEF(signTabulate, 2), CALLDEF(sumOver, 4), CALLDEF(validate, 3), CALLDEF(weightedMean, 5), CALLDEF(weightedMedian, 6), CALLDEF(x_OP_y, 11), {NULL, NULL, 0} }; void R_init_matrixStats(DllInfo *info) { R_registerRoutines(info, NULL, callMethods, NULL, NULL); } matrixStats/src/sumOver_TYPE-template.h0000644000175100001440000000316512776336765017720 0ustar hornikusers/*********************************************************************** TEMPLATE: double sumOver_[idxsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nx, int *idxs, R_xlen_t nidxs, int narm, int mode Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD_NAME: the name of the resulting function - X_TYPE: 'i' or 'r' Copyright: Henrik Bengtsson, 2014 ***********************************************************************/ #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, [METHOD_NAME]) */ #include "templates-types.h" #include RETURN_TYPE METHOD_NAME_IDXS(ARGUMENTS_LIST) { X_C_TYPE value; R_xlen_t ii; LDOUBLE sum = 0; #ifdef IDXS_TYPE IDXS_C_TYPE *cidxs = (IDXS_C_TYPE*) idxs; #endif for (ii=0; ii < nidxs; ++ii) { value = R_INDEX_GET(x, IDX_INDEX(cidxs,ii), X_NA); #if X_TYPE == 'i' if (!X_ISNAN(value)) { sum += (LDOUBLE)value; } else if (!narm) { sum = R_NaReal; break; } #elif X_TYPE == 'r' if (!narm) { sum += (LDOUBLE)value; if (ii % 1048576 == 0 && !R_FINITE(sum)) break; } else if (!ISNAN(value)) { sum += (LDOUBLE)value; } #endif } /* for (ii ...) */ return (double)sum; } /*************************************************************************** HISTORY: 2015-07-11 [DJ] o Supported subsetted computation. 2014-11-06 [HB] o CLEANUP: Now sumOver_() uses only basic C types. 2014-11-02 [HB] o Created. **************************************************************************/ matrixStats/src/rowRanksWithTies_TYPE_TIES-template.h0000644000175100001440000001331112776336765022365 0ustar hornikusers/*********************************************************************** TEMPLATE: Ranks_Real_ties[rowsType][colsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, ANS_C_TYPE *ans Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD_NAME: the name of the resulting function - MARGIN: 'r' (rows) or 'c' (columns). - X_TYPE: 'i' or 'r' - ANS_TYPE: 'i' or 'r' - TIESMETHOD: '0' (min), '1' (max), 'a' (average) Authors: Hector Corrada Bravo [HCB] Peter Langfelder [PL] Henrik Bengtsson [HB] ***********************************************************************/ #include #undef RANK #if TIESMETHOD == '0' /* min */ #define ANS_TYPE 'i' #define RANK(firstTie, aboveTie) firstTie + 1 #elif TIESMETHOD == '1' /* max */ #define ANS_TYPE 'i' #define RANK(firstTie, aboveTie) aboveTie #elif TIESMETHOD == 'a' /* average */ #define ANS_TYPE 'r' #define RANK(firstTie, aboveTie) ((double) (firstTie + aboveTie + 1))/2 #endif /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, X_ISNAN, [METHOD_NAME]) ANS_TYPE => (ANS_SXP, ANS_NA, ANS_C_TYPE, ANS_IN_C) */ #include "templates-types.h" /* Indexing formula to compute the vector index of element j of vector i. Should take arguments element, vector, nElements, nVectors. */ #undef ANS_INDEX_OF #if MARGIN == 'r' /* rows */ #define ANS_INDEX_OF(element, vector, nRows) \ vector + element*nRows #elif MARGIN == 'c' /* columns */ #define ANS_INDEX_OF(element, vector, nRows) \ element + vector*nRows #else #error "MARGIN can only be 'r' or 'c'" #endif void METHOD_NAME_ROWS_COLS(ARGUMENTS_LIST) { ANS_C_TYPE rank; X_C_TYPE *values, current, tmp; R_xlen_t *colOffset; R_xlen_t ii, jj, kk, rowIdx; int *I; int lastFinite, firstTie, aboveTie; int nvalues, nVec; #ifdef ROWS_TYPE ROWS_C_TYPE *crows = (ROWS_C_TYPE*) rows; #endif #ifdef COLS_TYPE COLS_C_TYPE *ccols = (COLS_C_TYPE*) cols; #endif #if MARGIN == 'r' nvalues = ncols; nVec = nrows; /* Pre-calculate the column offsets */ colOffset = (R_xlen_t *) R_alloc(ncols, sizeof(R_xlen_t)); for (jj=0; jj < ncols; jj++) colOffset[jj] = R_INDEX_OP(COL_INDEX(ccols,jj), *, nrow); #elif MARGIN == 'c' nvalues = nrows; nVec = ncols; /* Pre-calculate the column offsets */ colOffset = (R_xlen_t *) R_alloc(nrows, sizeof(R_xlen_t)); for (jj=0; jj < nrows; jj++) colOffset[jj] = ROW_INDEX(crows,jj); #endif values = (X_C_TYPE *) R_alloc(nvalues, sizeof(X_C_TYPE)); I = (int *) R_alloc(nvalues, sizeof(int)); for (ii=0; ii < nVec; ii++) { #if MARGIN == 'r' rowIdx = ROW_INDEX(crows,ii); #elif MARGIN == 'c' rowIdx = R_INDEX_OP(COL_INDEX(ccols,ii), *, nrow); #endif lastFinite = nvalues-1; /* Put the NA/NaN elements at the end of the vector and update the index vector appropriately. This may be a bit faster since it only uses one loop over the length of the vector, plus it shortens the sort in case there are missing values. /PL (2012-12-14) */ for (jj = 0; jj <= lastFinite; jj++) { tmp = R_INDEX_GET(x, R_INDEX_OP(rowIdx,+,colOffset[jj]), X_NA); if (X_ISNAN(tmp)) { while (lastFinite > jj && X_ISNAN(R_INDEX_GET(x, R_INDEX_OP(rowIdx,+,colOffset[lastFinite]), X_NA))) { I[lastFinite] = lastFinite; lastFinite--; } I[lastFinite] = jj; I[jj] = lastFinite; values[ jj ] = R_INDEX_GET(x, R_INDEX_OP(rowIdx,+,colOffset[lastFinite]), X_NA); values[ lastFinite ] = tmp; lastFinite--; } else { I[jj] = jj; values[ jj ] = tmp; } } /* for (jj ...) */ // Diagnostic print-outs /* Rprintf("Swapped vector:\n"); for (jj=0; jj < nvalues; jj++) { Rprintf(" %8.4f,", values[jj]); if (((jj+1) % 5==0) || (jj==nvalues-1)) Rprintf("\n"); } Rprintf("Index vector:\n"); for (jj=0; jj 0) X_QSORT_I(values, I, 1, lastFinite + 1); // Calculate the ranks. for (jj=0; jj <= lastFinite;) { firstTie = jj; current = values[jj]; while ((jj <= lastFinite) && (values[jj] == current)) jj++; aboveTie = jj; // Depending on rank method, get maximum, average, or minimum rank rank = RANK(firstTie, aboveTie); for (kk=firstTie; kk < aboveTie; kk++) { ans[ ANS_INDEX_OF(I[kk], ii, nrows) ] = rank; } } // At this point jj = lastFinite + 1, no need to re-initialize again. for (; jj < nvalues; jj++) { ans[ ANS_INDEX_OF(I[jj], ii, nrows) ] = ANS_NA; } // Rprintf("\n"); } } /*************************************************************************** HISTORY: 2015-06-12 [DJ] o Supported subsetted computation. 2014-11-06 [HB] o CLEANUP: Moving away from R data types in low-level C functions. 2013-04-23 [HB] o BUG FIX: Ranks did not work for integers with NAs; now using X_ISNAN(). 2013-01-13 [HB] o Template cleanup. Extened tempate to integer matrices. o Added argument 'tiesMethod' to rowRanks(). 2012-12-14 [PL] o Added internal support for "min", "max" and "average" ties. Using template to generate the various versions of the functions. 2013-01-13 [HCB] o Created. Using "max" ties. **************************************************************************/ matrixStats/src/diff2_TYPE-template.h0000644000175100001440000000532412776336765017251 0ustar hornikusers/*********************************************************************** TEMPLATE: void diff2_[idxsType](ARGUMENTS_LIST) ARGUMENTS_LIST: X_C_TYPE *x, R_xlen_t nx, void *idxs, R_xlen_t nidxs, R_xlen_t lag, R_xlen_t differences, X_C_TYPE *ans, R_xlen_t nans Arguments: The following macros ("arguments") should be defined for the template to work as intended. - METHOD_NAME: the name of the resulting function - X_TYPE: 'i' or 'r' Copyright: Henrik Bengtsson, 2014 ***********************************************************************/ #include "types.h" /* Expand arguments: X_TYPE => (X_C_TYPE, X_IN_C, [METHOD_NAME]) */ #include "templates-types.h" #include #undef X_DIFF #if X_TYPE == 'i' #ifndef diff_int static R_INLINE int diff_int(int a, int b) { if (X_ISNA(a) || X_ISNA(b)) return(NA_INTEGER); return a-b; } #define diff_int diff_int #endif #define X_DIFF diff_int #elif X_TYPE == 'r' #define X_DIFF(a,b) a-b #endif RETURN_TYPE METHOD_NAME_IDXS(ARGUMENTS_LIST) { R_xlen_t ii, tt, uu; X_C_TYPE xvalue1, xvalue2; X_C_TYPE *tmp = NULL; #ifdef IDXS_TYPE IDXS_C_TYPE *cidxs = (IDXS_C_TYPE*) idxs; #endif /* Nothing to do? */ if (nans <= 0) return; /* Special case (difference == 1) */ if (differences == 1) { for (ii=0; ii < nans; ii++) { xvalue1 = R_INDEX_GET(x, IDX_INDEX(cidxs,ii), X_NA); xvalue2 = R_INDEX_GET(x, IDX_INDEX(cidxs,ii+lag), X_NA); ans[ii] = X_DIFF(xvalue2, xvalue1); } } else { /* Allocate temporary work vector (to hold intermediate differences) */ tmp = Calloc(nidxs - lag, X_C_TYPE); /* (a) First order of differences */ for (ii=0; ii < nidxs-lag; ii++) { xvalue1 = R_INDEX_GET(x, IDX_INDEX(cidxs,ii), X_NA); xvalue2 = R_INDEX_GET(x, IDX_INDEX(cidxs,ii+lag), X_NA); tmp[ii] = X_DIFF(xvalue2, xvalue1); } nidxs -= lag; /* (b) All other orders of differences but the last */ while (--differences > 1) { uu = lag; tt = 0; for (ii=0; ii < nidxs-lag; ii++) { tmp[ii] = X_DIFF(tmp[uu++], tmp[tt++]); } nidxs -= lag; } /* Sanity check */ /* if (nidxs-lag != nans) error("nidxs != nans: %d != %d\n", nidxs, nans); */ /* (c) Last order of differences */ uu = lag; tt = 0; for (ii=0; ii < nans; ii++) { ans[ii] = X_DIFF(tmp[uu++], tmp[tt++]); } /* Deallocate temorary work vector */ Free(tmp); } /* if (differences ...) */ } /*************************************************************************** HISTORY: 2015-06-14 [DJ] o Supported subsetted computation. 2014-12-29 [HB] o Created. **************************************************************************/ matrixStats/src/rowMads.c0000644000175100001440000000473612776336765015222 0ustar hornikusers/*************************************************************************** Public methods: SEXP rowMads(SEXP x, ...) Authors: Henrik Bengtsson Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #define METHOD rowMads #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, double scale, int narm, int hasna, int byrow, double *ans #define X_TYPE 'i' #include "templates-gen-matrix.h" #define X_TYPE 'r' #include "templates-gen-matrix.h" SEXP rowMads(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP constant, SEXP naRm, SEXP hasNA, SEXP byRow) { int narm, hasna, byrow; SEXP ans; R_xlen_t nrow, ncol; double scale; /* Argument 'x' and 'dim': */ assertArgMatrix(x, dim, (R_TYPE_INT | R_TYPE_REAL), "x"); nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'constant': */ if (!isNumeric(constant)) error("Argument 'constant' must be a numeric scale."); scale = asReal(constant); /* Argument 'naRm': */ narm = asLogicalNoNA(naRm, "na.rm"); /* Argument 'hasNA': */ hasna = asLogicalNoNA(hasNA, "hasNA"); /* Argument 'rows' and 'cols': */ R_xlen_t nrows, ncols; int rowsType, colsType; void *crows = validateIndices(rows, nrow, 0, &nrows, &rowsType); void *ccols = validateIndices(cols, ncol, 0, &ncols, &colsType); /* Argument 'byRow': */ byrow = asLogical(byRow); if (!byrow) { SWAP(R_xlen_t, nrow, ncol); SWAP(void*, crows, ccols); SWAP(R_xlen_t, nrows, ncols); SWAP(int, rowsType, colsType); } /* R allocate a double vector of length 'nrow' Note that 'nrow' means 'ncol' if byrow=FALSE. */ PROTECT(ans = allocVector(REALSXP, nrows)); /* Double matrices are more common to use. */ if (isReal(x)) { rowMads_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, scale, narm, hasna, byrow, REAL(ans)); } else if (isInteger(x)) { rowMads_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, scale, narm, hasna, byrow, REAL(ans)); } UNPROTECT(1); return(ans); } /* rowMads() */ /*************************************************************************** HISTORY: 2015-06-13 [DJ] o Supported subsetted computation. 2014-11-17 [HB] o Created from rowMedians.c. **************************************************************************/ matrixStats/src/templates-gen-matrix.h0000644000175100001440000000166212776336765017655 0ustar hornikusers#include "macros.h" #include METHOD_TEMPLATE_H #define COLS_TYPE 'i' #include METHOD_TEMPLATE_H #undef COLS_TYPE #define COLS_TYPE 'r' #include METHOD_TEMPLATE_H #undef COLS_TYPE #define ROWS_TYPE 'i' #include METHOD_TEMPLATE_H #define COLS_TYPE 'i' #include METHOD_TEMPLATE_H #undef COLS_TYPE #define COLS_TYPE 'r' #include METHOD_TEMPLATE_H #undef COLS_TYPE #undef ROWS_TYPE #define ROWS_TYPE 'r' #include METHOD_TEMPLATE_H #define COLS_TYPE 'i' #include METHOD_TEMPLATE_H #undef COLS_TYPE #define COLS_TYPE 'r' #include METHOD_TEMPLATE_H #undef COLS_TYPE #undef ROWS_TYPE RETURN_TYPE (*METHOD_NAME[3][3])(ARGUMENTS_LIST) = { {METHOD_NAME_noRows_noCols, METHOD_NAME_noRows_intCols, METHOD_NAME_noRows_realCols}, {METHOD_NAME_intRows_noCols, METHOD_NAME_intRows_intCols, METHOD_NAME_intRows_realCols}, {METHOD_NAME_realRows_noCols, METHOD_NAME_realRows_intCols, METHOD_NAME_realRows_realCols}, }; #include "templates-types_undef.h" matrixStats/src/rowCumMinMaxs.c0000644000175100001440000000665212776336765016356 0ustar hornikusers/*************************************************************************** Public methods: SEXP rowCummins(SEXP x, ...) SEXP colCummins(SEXP x, ...) Authors: Henrik Bengtsson Copyright Henrik Bengtsson, 2014 **************************************************************************/ #include #include "types.h" #include "utils.h" #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int byrow, ANS_C_TYPE *ans #define METHOD rowCummins #define COMP '<' #define METHOD_TEMPLATE_H "rowCumMinMaxs_TYPE-template.h" #define X_TYPE 'i' #include "templates-gen-matrix.h" #define X_TYPE 'r' #include "templates-gen-matrix.h" SEXP rowCummins(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP byRow) { int byrow; SEXP ans = NILSXP; R_xlen_t nrow, ncol; /* Argument 'x' and 'dim': */ assertArgMatrix(x, dim, (R_TYPE_INT | R_TYPE_REAL), "x"); nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'rows' and 'cols': */ R_xlen_t nrows, ncols; int rowsType, colsType; void *crows = validateIndices(rows, nrow, 0, &nrows, &rowsType); void *ccols = validateIndices(cols, ncol, 0, &ncols, &colsType); /* Argument 'byRow': */ byrow = asLogical(byRow); /* Double matrices are more common to use. */ if (isReal(x)) { PROTECT(ans = allocMatrix(REALSXP, nrows, ncols)); rowCummins_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, byrow, REAL(ans)); UNPROTECT(1); } else if (isInteger(x)) { PROTECT(ans = allocMatrix(INTSXP, nrows, ncols)); rowCummins_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, byrow, INTEGER(ans)); UNPROTECT(1); } return(ans); } /* rowCummins() */ #undef COMP #undef METHOD #define RETURN_TYPE void #define ARGUMENTS_LIST X_C_TYPE *x, R_xlen_t nrow, R_xlen_t ncol, void *rows, R_xlen_t nrows, void *cols, R_xlen_t ncols, int byrow, ANS_C_TYPE *ans #define METHOD rowCummaxs #define COMP '>' #define X_TYPE 'i' #include "templates-gen-matrix.h" #define X_TYPE 'r' #include "templates-gen-matrix.h" SEXP rowCummaxs(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP byRow) { int byrow; SEXP ans = NILSXP; R_xlen_t nrow, ncol; /* Argument 'x' and 'dim': */ assertArgMatrix(x, dim, (R_TYPE_INT | R_TYPE_REAL), "x"); nrow = asR_xlen_t(dim, 0); ncol = asR_xlen_t(dim, 1); /* Argument 'rows' and 'cols': */ R_xlen_t nrows, ncols; int rowsType, colsType; void *crows = validateIndices(rows, nrow, 0, &nrows, &rowsType); void *ccols = validateIndices(cols, ncol, 0, &ncols, &colsType); /* Argument 'byRow': */ byrow = asLogical(byRow); /* Double matrices are more common to use. */ if (isReal(x)) { PROTECT(ans = allocMatrix(REALSXP, nrows, ncols)); rowCummaxs_Real[rowsType][colsType](REAL(x), nrow, ncol, crows, nrows, ccols, ncols, byrow, REAL(ans)); UNPROTECT(1); } else if (isInteger(x)) { PROTECT(ans = allocMatrix(INTSXP, nrows, ncols)); rowCummaxs_Integer[rowsType][colsType](INTEGER(x), nrow, ncol, crows, nrows, ccols, ncols, byrow, INTEGER(ans)); UNPROTECT(1); } return(ans); } /* rowCummaxs() */ #undef COMP /*************************************************************************** HISTORY: 2015-06-07 [DJ] o Supported subsetted computation. 2014-11-26 [HB] o Created from rowVars.c. **************************************************************************/ matrixStats/src/api.h0000644000175100001440000000475712776336765014367 0ustar hornikusers/* C-level API that is called from R */ SEXP allocArray2(SEXP dim, SEXP value); SEXP allocMatrix2(SEXP nrow, SEXP ncol, SEXP value); SEXP allocVector2(SEXP length, SEXP value); SEXP anyMissing(SEXP x, SEXP idxs); SEXP binCounts(SEXP x, SEXP bx, SEXP right); SEXP binMeans(SEXP y, SEXP x, SEXP bx, SEXP retCount, SEXP right); SEXP colCounts(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP value, SEXP what, SEXP naRm, SEXP hasNA); SEXP colOrderStats(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP which); SEXP colRanges(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP what, SEXP naRm, SEXP hasNA); SEXP count(SEXP x, SEXP idxs, SEXP value, SEXP what, SEXP naRm, SEXP hasNA); SEXP diff2(SEXP x, SEXP idxs, SEXP lag, SEXP differences); SEXP indexByRow(SEXP dim, SEXP idxs); SEXP logSumExp(SEXP lx, SEXP idxs, SEXP naRm, SEXP hasNA); SEXP meanOver(SEXP x, SEXP idxs, SEXP naRm, SEXP refine); SEXP productExpSumLog(SEXP x, SEXP idxs, SEXP naRm, SEXP hasNA); SEXP psortKM(SEXP x, SEXP k, SEXP m); SEXP rowCounts(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP value, SEXP what, SEXP naRm, SEXP hasNA); SEXP rowCummaxs(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP byRow); SEXP rowCummins(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP byRow); SEXP rowCumprods(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP byRow); SEXP rowCumsums(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP byRow); SEXP rowDiffs(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP lag, SEXP differences, SEXP byRow); SEXP rowLogSumExps(SEXP lx, SEXP dim, SEXP rows, SEXP cols, SEXP naRm, SEXP hasNA, SEXP byRow); SEXP rowMads(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP constant, SEXP naRm, SEXP hasNA, SEXP byRow); SEXP rowMedians(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP naRm, SEXP hasNA, SEXP byRow); SEXP rowOrderStats(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP which); SEXP rowRanges(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP what, SEXP naRm, SEXP hasNA); SEXP rowRanksWithTies(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP tiesMethod, SEXP byRow); SEXP rowVars(SEXP x, SEXP dim, SEXP rows, SEXP cols, SEXP naRm, SEXP hasNA, SEXP byRow); SEXP signTabulate(SEXP x, SEXP idxs); SEXP sumOver(SEXP x, SEXP idxs, SEXP naRm, SEXP mode); SEXP validate(SEXP idxs, SEXP maxIdx, SEXP allowOutOfBound); SEXP weightedMean(SEXP x, SEXP w, SEXP idxs, SEXP naRm, SEXP refine); SEXP weightedMedian(SEXP x, SEXP w, SEXP idxs, SEXP naRm, SEXP interpolate, SEXP ties); SEXP x_OP_y(SEXP x, SEXP y, SEXP dim, SEXP operator, SEXP xrows, SEXP xcols, SEXP yidxs, SEXP commute, SEXP naRm, SEXP hasNA, SEXP byRow); matrixStats/src/x_OP_y_TYPE-template.h0000644000175100001440000002220712776336765017453 0ustar hornikusers#include "types.h" #include "templates-types.h" #if OP == '+' #define FUN_no_NA CONCAT_MACROS(FUN_no_NA, METHOD_NAME_ROWS_COLS_IDXS) static R_INLINE double FUN_no_NA(X_C_TYPE x, Y_C_TYPE y) { #if X_TYPE == 'i' if (X_ISNAN(x)) return NA_REAL; #endif #if Y_TYPE == 'i' if (Y_ISNAN(y)) return NA_REAL; #endif return (double)x + (double)y; } #define FUN_narm CONCAT_MACROS(FUN, METHOD_NAME_ROWS_COLS_IDXS) static R_INLINE double FUN_narm(X_C_TYPE x, Y_C_TYPE y) { if (X_ISNAN(x)) { return (double)y; } else if (Y_ISNAN(y)) { return (double)x; } else { return (double)x + (double)y; } } #elif OP == '-' #define FUN_no_NA CONCAT_MACROS(FUN_no_NA, METHOD_NAME_ROWS_COLS_IDXS) static R_INLINE double FUN_no_NA(X_C_TYPE x, Y_C_TYPE y) { #if X_TYPE == 'i' if (X_ISNAN(x)) return NA_REAL; #endif #if Y_TYPE == 'i' if (Y_ISNAN(y)) return NA_REAL; #endif return (double)x - (double)y; } #define FUN_narm FUN_no_NA #elif OP == '*' #define FUN_no_NA CONCAT_MACROS(FUN_no_NA, METHOD_NAME_ROWS_COLS_IDXS) static R_INLINE double FUN_no_NA(X_C_TYPE x, Y_C_TYPE y) { #if X_TYPE == 'i' if (X_ISNAN(x)) return NA_REAL; #endif #if Y_TYPE == 'i' if (Y_ISNAN(y)) return NA_REAL; #endif return (double)x * (double)y; } #define FUN_narm CONCAT_MACROS(FUN, METHOD_NAME_ROWS_COLS_IDXS) static R_INLINE double FUN_narm(X_C_TYPE x, Y_C_TYPE y) { if (X_ISNAN(x)) { return (double)y; } else if (Y_ISNAN(y)) { return (double)x; } else { return (double)x * (double)y; } } #elif OP == '/' #define FUN_no_NA CONCAT_MACROS(FUN_no_NA, METHOD_NAME_ROWS_COLS_IDXS) static R_INLINE double FUN_no_NA(X_C_TYPE x, Y_C_TYPE y) { #if X_TYPE == 'i' if (X_ISNAN(x)) return NA_REAL; #endif #if Y_TYPE == 'i' if (Y_ISNAN(y)) return NA_REAL; #endif return (double)x / (double)y; } #define FUN_narm FUN_no_NA #else #error "INTERNAL ERROR: Failed to set C inline function FUN(x, y): Unknown OP" #endif RETURN_TYPE METHOD_NAME_ROWS_COLS_IDXS(ARGUMENTS_LIST) { R_xlen_t ii, jj, kk, idx, colBegin; R_xlen_t txi, yi; X_C_TYPE xvalue; Y_C_TYPE yvalue; double value; #if ANS_TYPE == 'i' int ok = 1; /* OK, i.e. no integer overflow yet? */ double R_INT_MIN_d = (double)R_INT_MIN, R_INT_MAX_d = (double)R_INT_MAX; #endif #ifdef ROWS_TYPE ROWS_C_TYPE *cxrows = (ROWS_C_TYPE*) xrows; #endif #ifdef COLS_TYPE COLS_C_TYPE *cxcols = (COLS_C_TYPE*) xcols; #endif #ifdef IDXS_TYPE IDXS_C_TYPE *cyidxs = (IDXS_C_TYPE*) yidxs; #endif yi = 0; kk = 0; if (byrow) { if (commute) { if (narm) { for (jj=0; jj < nxcols; ++jj) { colBegin = R_INDEX_OP(COL_INDEX(cxcols,jj), *, nrow); txi = jj; for (ii=0; ii < nxrows; ++ii) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(cxrows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); idx = IDX_INDEX(cyidxs, txi%nyidxs); yvalue = R_INDEX_GET(y, idx, Y_NA); value = FUN_narm(yvalue, xvalue); #if ANS_TYPE == 'i' if (ok && (value < R_INT_MIN_d || value > R_INT_MAX_d)) { ok = 0; value = NA_REAL; } ans[kk ++] = ISNAN(value) ? NA_INTEGER : (ANS_C_TYPE) value; #else ans[kk ++] = (ANS_C_TYPE) value; #endif txi += nxcols; /* txi = ii * nxcols + jj; */ } } } else { for (jj=0; jj < nxcols; ++jj) { colBegin = R_INDEX_OP(COL_INDEX(cxcols,jj), *, nrow); txi = jj; for (ii=0; ii < nxrows; ++ii) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(cxrows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); idx = IDX_INDEX(cyidxs, txi%nyidxs); yvalue = R_INDEX_GET(y, idx, Y_NA); value = FUN_no_NA(yvalue, xvalue); #if ANS_TYPE == 'i' if (ok && (value < R_INT_MIN_d || value > R_INT_MAX_d)) { ok = 0; value = NA_REAL; } ans[kk ++] = ISNAN(value) ? NA_INTEGER : (ANS_C_TYPE) value; #else ans[kk ++] = (ANS_C_TYPE) value; #endif txi += nxcols; /* txi = ii * nxcols + jj; */ } } } } else { if (narm) { for (jj=0; jj < nxcols; ++jj) { colBegin = R_INDEX_OP(COL_INDEX(cxcols,jj), *, nrow); txi = jj; for (ii=0; ii < nxrows; ++ii) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(cxrows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); idx = IDX_INDEX(cyidxs, txi%nyidxs); yvalue = R_INDEX_GET(y, idx, Y_NA); value = FUN_narm(xvalue, yvalue); #if ANS_TYPE == 'i' if (ok && (value < R_INT_MIN_d || value > R_INT_MAX_d)) { ok = 0; value = NA_REAL; } ans[kk ++] = ISNAN(value) ? NA_INTEGER : (ANS_C_TYPE) value; #else ans[kk ++] = (ANS_C_TYPE) value; #endif txi += nxcols; /* txi = ii * nxcols + jj; */ } } } else { for (jj=0; jj < nxcols; ++jj) { colBegin = R_INDEX_OP(COL_INDEX(cxcols,jj), *, nrow); txi = jj; for (ii=0; ii < nxrows; ++ii) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(cxrows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); idx = IDX_INDEX(cyidxs, txi%nyidxs); yvalue = R_INDEX_GET(y, idx, Y_NA); value = FUN_no_NA(xvalue, yvalue); #if ANS_TYPE == 'i' if (ok && (value < R_INT_MIN_d || value > R_INT_MAX_d)) { ok = 0; value = NA_REAL; } ans[kk ++] = ISNAN(value) ? NA_INTEGER : (ANS_C_TYPE) value; #else ans[kk ++] = (ANS_C_TYPE) value; #endif txi += nxcols; /* txi = ii * nxcols + jj; */ } } } } } else { if (commute) { if (narm) { for (jj=0; jj < nxcols; ++jj) { colBegin = R_INDEX_OP(COL_INDEX(cxcols,jj), *, nrow); for (ii=0; ii < nxrows; ++ii) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(cxrows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); idx = IDX_INDEX(cyidxs, yi); yvalue = R_INDEX_GET(y, idx, Y_NA); value = FUN_narm(yvalue, xvalue); #if ANS_TYPE == 'i' if (ok && (value < R_INT_MIN_d || value > R_INT_MAX_d)) { ok = 0; value = NA_REAL; } ans[kk ++] = ISNAN(value) ? NA_INTEGER : (ANS_C_TYPE) value; #else ans[kk ++] = (ANS_C_TYPE) value; #endif if (++ yi >= nyidxs) yi = 0; } } } else { for (jj=0; jj < nxcols; ++jj) { colBegin = R_INDEX_OP(COL_INDEX(cxcols,jj), *, nrow); for (ii=0; ii < nxrows; ++ii) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(cxrows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); idx = IDX_INDEX(cyidxs, yi); yvalue = R_INDEX_GET(y, idx, Y_NA); value = FUN_no_NA(yvalue, xvalue); #if ANS_TYPE == 'i' if (ok && (value < R_INT_MIN_d || value > R_INT_MAX_d)) { ok = 0; value = NA_REAL; } ans[kk ++] = ISNAN(value) ? NA_INTEGER : (ANS_C_TYPE) value; #else ans[kk ++] = (ANS_C_TYPE) value; #endif if (++ yi >= nyidxs) yi = 0; } } } } else { if (narm) { for (jj=0; jj < nxcols; ++jj) { colBegin = R_INDEX_OP(COL_INDEX(cxcols,jj), *, nrow); for (ii=0; ii < nxrows; ++ii) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(cxrows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); idx = IDX_INDEX(cyidxs, yi); yvalue = R_INDEX_GET(y, idx, Y_NA); value = FUN_narm(xvalue, yvalue); #if ANS_TYPE == 'i' if (ok && (value < R_INT_MIN_d || value > R_INT_MAX_d)) { ok = 0; value = NA_REAL; } ans[kk ++] = ISNAN(value) ? NA_INTEGER : (ANS_C_TYPE) value; #else ans[kk ++] = (ANS_C_TYPE) value; #endif if (++ yi >= nyidxs) yi = 0; } } } else { for (jj=0; jj < nxcols; ++jj) { colBegin = R_INDEX_OP(COL_INDEX(cxcols,jj), *, nrow); for (ii=0; ii < nxrows; ++ii) { idx = R_INDEX_OP(colBegin, +, ROW_INDEX(cxrows,ii)); xvalue = R_INDEX_GET(x, idx, X_NA); idx = IDX_INDEX(cyidxs, yi); yvalue = R_INDEX_GET(y, idx, Y_NA); value = FUN_no_NA(xvalue, yvalue); #if ANS_TYPE == 'i' if (ok && (value < R_INT_MIN_d || value > R_INT_MAX_d)) { ok = 0; value = NA_REAL; } ans[kk ++] = ISNAN(value) ? NA_INTEGER : (ANS_C_TYPE) value; #else ans[kk ++] = (ANS_C_TYPE) value; #endif if (++ yi >= nyidxs) yi = 0; } } } } } /* if (byrow) */ #if ANS_TYPE == 'i' /* Warn on integer overflow? */ if (!ok) { warning("Integer overflow. Detected one or more elements whose absolute values were out of the range [%d,%d] that can be used to for integers. Such values are set to NA_integer_.", R_INT_MIN, R_INT_MAX); } #endif } #undef FUN #undef FUN_narm matrixStats/NAMESPACE0000644000175100001440000000467512776226773014071 0ustar hornikusersuseDynLib("matrixStats", .registration=TRUE, .fixes="C_") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # IMPORTS # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - importFrom("stats", "mad") importFrom("stats", "median") importFrom("stats", "quantile") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # EXPORTS # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - export("allocVector") export("allocMatrix") export("allocArray") export("allValue") export("anyMissing") export("anyValue") export("binCounts") export("binMeans") export("colAlls") export("colAnys") export("colAnyMissings") export("colAvgsPerRowSet") export("colCollapse") export("colCounts") export("colCummins") export("colCummaxs") export("colCumprods") export("colCumsums") export("colDiffs") export("colIQRs") export("colIQRDiffs") export("colLogSumExps") export("colMadDiffs") export("colMads") export("colMaxs") export("colMedians") export("colMins") export("colOrderStats") export("colProds") export("colQuantiles") export("colRanges") export("colRanks") export("colSdDiffs") export("colSds") export("colTabulates") export("colVarDiffs") export("colVars") export("colWeightedMads") export("colWeightedMeans") export("colWeightedMedians") export("colWeightedSds") export("colWeightedVars") export("count") export("diff2") export("indexByRow") export("iqr") export("iqrDiff") export("logSumExp") export("madDiff") export("meanOver") export("product") export("rowAlls") export("rowAnys") export("rowAnyMissings") export("rowAvgsPerColSet") export("rowCollapse") export("rowCounts") export("rowCummins") export("rowCummaxs") export("rowCumprods") export("rowCumsums") export("rowDiffs") export("rowIQRs") export("rowIQRDiffs") export("rowLogSumExps") export("rowMadDiffs") export("rowMads") export("rowMaxs") export("rowMedians") export("rowMins") export("rowOrderStats") export("rowProds") export("rowQuantiles") export("rowRanges") export("rowRanks") export("rowSdDiffs") export("rowSds") export("rowTabulates") export("rowVarDiffs") export("rowVars") export("rowWeightedMads") export("rowWeightedMeans") export("rowWeightedMedians") export("rowWeightedSds") export("rowWeightedVars") export("sdDiff") export("signTabulate") export("sumOver") export("x_OP_y") export("t_tx_OP_y") export("varDiff") export("weightedMad") export("weightedMean") export("weightedMedian") export("weightedSd") export("weightedVar") export("validateIndices") matrixStats/NEWS0000644000175100001440000007415312776330444013336 0ustar hornikusersPackage: matrixStats ==================== Version: 0.51.0 [2016-10-08] o SPEEDUP / CLEANUP: rowMedians() and colMedians() are now plain functions. They were previously S4 methods (due to a Bioconductor legacy). The package no longer imports the methods package. o SPEEDUP: Now native API is formally registered allowing for faster lookup of routines from R. Version: 0.50.2 [2016-04-24] o BUG FIX: logSumExp(c(-Inf, -Inf, ...)) would return NaN rather than -Inf (Issue #48). Thanks to Jason Xu (University of Washington) for reporting and Brennan Vincent for troubleshooting and contributing a fix. o Package now installs on R (>= 2.12.0) as claimed. Thanks to Mikko Korpela at Aalto University School of Science, Finland, for troubleshooting and providing a fix. Version: 0.50.1 [2015-12-14] o BUG FIX: The Undefined Behavior Sanitizer (UBsan) reported on a memcall(src, dest, 0) call where dest == null. Thanks to Brian Ripley and the CRAN check tools for catching this. We could reproduce this with gcc 5.1.1 but not with gcc 4.9.2. Version: 0.50.0 [2015-12-13] o MAJOR FEATURE UPDATE: Subsetting arguments 'idxs', 'rows' and 'cols' were added to all functions such that the calculations are performed on the requested subset while avoiding creating a subsetted copy, i.e. rowVars(x, cols=4:6) is a much faster and more memory efficient version than rowVars(x[,4:6]) and even yet more efficient than apply(x, MARGIN=1L, FUN=var). These features were added by Dongcan Jiang, Peking University, with support from the Google Summer of Code program. A great thank you to Dongcan and to Google for making this possible. Version: 0.15.0 [2015-10-26] o CONSISTENCY: Now all weight arguments ('w' and 'W') default to NULL, which corresponds to uniform weights. o ROBUSTNESS: Importing 'stats' functions in namespace. o CLEANUP: Defunct argument 'centers' for col-/rowMads(); use 'center'. o BUG FIX: weightedVar(x, w) used the wrong bias correction factor resulting in an estimate that was tau too large, where tau = ((sum(w) - 1) / sum(w)) / ((length(w) - 1) / length(w)). Thanks to Wolfgang Abele for reporting and troubleshooting on this. o BUG FIX: weightedVar(x) with length(x) = 1 returned 0 no NA. Same for weightedSd(). o BUG FIX: weightedMedian(x, w=NA_real_) returned 'x' rather than NA_real_. This only happened for length(w) = 1. o BUG FIX: allocArray(dim) failed for prod(dim) >= .Machine$integer.max. Version: 0.14.2 [2015-06-23] o BUG FIX: x_OP_y() and t_tx_OP_y() would return garbage on Solaris SPARC (and possibly other architectures as well) when input was integer and had missing values. Version: 0.14.1 [2015-06-17] o BUG FIX: product(x, na.rm=FALSE) for integer 'x' with both zeros and NAs returned zero rather than NA. o BUG FIX: weightedMean(x, w, na.rm=TRUE) did not handle missing values in 'x' properly, if it was an integer. It would also return NaN if there were weights 'w' with missing values, whereas stats::weighted.mean() would skip such data points. Now weightedMean() does the same. o BUG FIX: (col|row)WeightedMedians() did not handle infinite weights as weightedMedian() does. o BUG FIX: x_OP_y(x, y, OP, na.rm=FALSE) returned garbage iff 'x' or 'y' had missing values of type integer. o BUG FIX: rowQuantiles() and rowIQRs() did not work for single-row matrices. Analogously for the corresponding column functions. o BUG FIX: rowCumsums(), rowCumprods() rowCummins(), and rowCummaxs(), accessed out-of-bound elements for Nx0 matrices where N > 0. The corresponding column methods has similar memory errors for 0xK matrices where K > 0. o BUG FIX: anyMissing(list(NULL)) returned NULL; now FALSE. o BUG FIX: rowCounts() resulted in garbage if a previous column had NAs (because forgot to update index kk in such cases). o BUG FIX: rowCumprods(x) handled missing values and zeros incorrectly for integer 'x (not double); a zero would trump an existing missing value causing the following cumulative products to become zero. It was only a zero that trumped NAs; any other integer would work as expected. Also, this bug was not in colCumprods(). o BUG FIX: rowAnys(x, value, na.rm=FALSE) did not handle missing values in a numeric 'x' properly. Similarly, for non-numeric and non-logical 'x', row- and colAnys(), row- and colAlls(), anyValue() and allValue() did not handle when 'value' was a missing value. o All of the above bugs were identified and fixed by Dongcan Jiang (Peking University, China), who also added corresponding unit tests. Version: 0.14.0 [2015-02-13] o ROBUSTNESS/TESTS: Package tests cover 96% of the code (was 91%). o CONSISTENCY: Renamed argument 'centers' of col- and rowMads() to 'center'. This is consistent with col- and rowVars(). o CONSISTENCY: col- and rowVars() now using na.rm=FALSE as the default (na.rm=TRUE was mistakenly introduced as the default in v0.9.7). o SPEEDUP: The check for user interrupts at the C level is now done less frequently of the functions. It does every k:th iteration, where k = 2^20, which is tested for using (iter % k == 0). It turns out, at least with the default compiler optimization settings that I use, that this test is 3 times faster if k = 2^n where n is an integer. The following functions checks for user interrupts: logSumExp(), (col|row)LogSumExps(), (col|row)Medians(),, (col|row)Mads(), (col|row)Vars(), and (col|row)Cum(Min|Max|prod|sum)s(). o SPEEDUP: logSumExp(x) is now faster if 'x' does not contain any missing values. It is also faster if all values are missing or the maximum value is +Inf - in both cases it can skip the actual summation step. o BUG FIX: all() and any() flavored methods on non-numeric and non-logical (e.g. character) vectors and matrices with na.rm=FALSE did not give results consistent with all() and any() if there were missing values. For example, with x <- c("a", NA, "b") we have all(x == "a") == FALSE and any(x == "a") == TRUE whereas our corresponding methods would return NA in those cases. The methods fixed are allValue(), anyValue(), col- and rowAlls(), and col- and rowAnys(). Added more package tests to cover these cases. o BUG FIX: Now logSumExp(x, na.rm=TRUE) would return NA if all values were NA and length(x) > 1. Now it returns -Inf for all length(x):s. o CLEANUP: anyMissing() is no longer an S4 generic. This was done as part of the migration of making all functions of matrixStats plain R functions, which minimizes calling overhead and it will also allow us to drop 'methods' from the package dependencies. I've scanned all CRAN and Bioconductor packages depending on matrixStats and none of them relied on anyMissing() dispatching on class, so hopefully this move has little impact. The only remaining S4 methods are now colMedians() and rowMedians(). o CLEANUP: Package no longer depends on R.methodsS3. Version: 0.13.1 [2015-01-21] o BUG FIX: diff2() with differences >= 3 would *read* spurious values beyond the allocated memory. This error, introduced in 0.13.0, was harmless in the sense that the returned value was unaffected and still correct. Thanks to Brian Ripley and the CRAN check tools for catching this. I could reproduce it locally with 'valgrind'. Version: 0.13.0 [2015-01-20] o Added iqrDiff() and (col|row)IqrDiffs(). o CONSISTENCY: Now rowQuantiles(x, na.rm=TRUE) returns all NAs for rows with missing values. Analogously for colQuantiles(), colIQRs(), rowIQRs() and iqr(). Previously, all these functions gave an error saying missing values are not allowed. o SPEEDUP: (col|row)Diffs() are now implemented in native code and notably faster than diff() for matrices. o SPEEDUP: Added diff2(), which is notably faster than base::diff() for vectors, which it is designed for. o DOCUMENTATION: Added vignette summarizing available functions. o COMPLETENESS: Added corresponding "missing" vector functions for already existing column and row functions. Similarly, added "missing" column and row functions for already existing vector functions, e.g. added iqr() and count() to complement already existing (col|row)IQRs() and (col|row)Counts() functions. o SPEEDUP: Made binCounts() and binMeans() a bit faster. o SPEEDUP: Added count(x, value) which is a notably faster than sum(x == value). This can also be used to count missing values etc. Also, added allValue() and anyValue() for all(x == value) and any(x == value). o SPEEDUP: Implemented weightedMedian() in native code, which made it ~3-10 times faster. Dropped support for ties="both", because it would have to return two values in case of ties, which made the API unnecessarily complicated. If really needed, then call the function twice with ties="min" and ties="max". o SPEEDUP: Added weightedMean(), which is ~10 times faster than stats::weighted.mean(). o SPEEDUP: (col|row)Anys() and (col|row)Alls() is now notably faster compared to previous versions. o SPEEDUP/CLEANUP: Turned several S3 and S4 methods into plain R functions, which decreases the overhead of calling the functions. After this there are no longer any S3 methods. Remaining S4 methods are anyMissing() and rowMedians(). o ROBUSTNESS: Now column and row methods give slightly more informative error messages if a data.frame is passed instead of a matrix. o CLEANUP: In the effort of migrating anyMissing() into a plain R function, the specific anyMissing() implementations for data.frame:s and and list:s were dropped and is now handled by anyMissing() for "ANY", which is the only S4 method remaining now. In a near future release, this remaining "ANY" method will turned into a plain R function and the current S4 generic will be dropped. We know of know CRAN and Bioconductor packages that relies on it being a generic function. Note also that since R (>= 3.1.0) there is a base::anyNA() function that does the exact same thing making anyMissing() obsolete. o BUG FIX: weightedMedian(..., ties="both") would give an error if there was a tie. Added package test for this case. Version: 0.12.2 [2014-12-07] o CODE FIX: The native code for product() on integer vector incorrectly used C-level abs() on intermediate values despite those being doubles requiring fabs(). Despite this, the calculated product would still be correct (at least when validated on several local setups as well as on the CRAN servers). Again, thanks to Brian Ripley for pointing out another invalid integer-double coersion at the C level. Version: 0.12.1 [2014-12-06] o ROBUSTNESS: Updated package tests to check methods in more scenarios, especially with both integer and numeric input data. o BUG FIX: (col|row)Cumsums(x) where 'x' is integer would return garbage for columns (rows) containing missing values. o BUG FIX: rowMads(x) where 'x' is numeric (not integer) would give incorrect results for rows that had an *odd* number of values (no ties). Analogously issues with colMads(). Added package tests for such cases too. Thanks to Brian Ripley and the CRAN check tools for (yet again) catching another coding mistake. Details: This was because the C-level calculation of the absolute value of residuals toward the median would use integer-based abs() rather than double- based fabs(). Now it fabs() is used when the values are double and abs() when they are integers. Version: 0.12.0 [2014-12-05] o Submitted to CRAN. Version: 0.11.9 [2014-11-26] o Added (col|row)Cumsums(), (col|row)Cumprods(), (col|row)Cummins(), and (col|row)Cummaxs(). o BUG FIX: (col|row)WeightedMeans() with all zero weights gave mean estimates with values 0 instead of NaN. Version: 0.11.8 [2014-11-25] o SPEEDUP: Implemented (col|row)Mads(), (col|row)Sds() and (col|row)Vars() in native code. o SPEEDUP: Made (col|row)Quantiles(x) faster for 'x' without missing values (and default type=7L quantiles). It should still be implemented in native code. o SPEEDUP: Made rowWeightedMeans() faster. o BUG FIX: (col|row)Medians(x) when 'x' is integer would give invalid median values in case (a) it was calculated as the mean of two values ("ties"), and (b) the sum of those values where greater than .Machine$integer.max. Now such ties are calculated using floating point precision. Add lots of package tests. Version: 0.11.6 [2014-11-16] o SPEEDUP: Now (col|row)Mins(), (col|row)Maxs() and (col|row)Ranges() are implemented in native code providing a significant speedup. o SPEEDUP: Now colOrderStats() also is implemented in native code, which indirectly makes colMins(), colMaxs() and colRanges() faster. o SPEEDUP: colTabulates(x) no longer uses rowTabulates(t(x)). o SPEEDUP: colQuantiles(x) no longer uses rowQuantiles(t(x)). o CLEANUP: Argument 'flavor' of (col|row)Ranks() is now ignored. Version: 0.11.5 [2014-11-15] o SPEEDUP: Now colCollapse(x) no longer utilizes rowCollapse(t(x)). Added package tests for (col|row)Collapse(). o SPEEDUP: Now colDiffs(x) no longer uses rowDiffs(t(x)). Added package tests for (col|row)Diffs(). o SPEEDUP: Package no longer utilizes match.arg() due to its overhead; methods sumOver(), (col|row)Prods() and (col|row)Ranks() were updated. o (col|row)Prods() now uses default method="direct" (was "expSumLog"). Version: 0.11.4 [2014-11-14] o Added support for vector input to several of the row- and column methods as long as the "intended" matrix dimension is specified via argument 'dim'. For instance, rowCounts(x, dim=c(nrow, ncol)) is the same as rowCounts(matrix(x, nrow, ncol)), but more efficient since it avoids creating/allocating a temporary matrix. o SPEEDUP: Now colCounts() is implemented in native code. Moreover, (col|row)Counts() are now also implemented in native code for logical input (previously only for integer and double input). Added more package tests and benchmarks for these functions. Version: 0.11.3 [2014-11-11] o Turned sdDiff(), madDiff(), varDiff(), weightedSd(), weightedVar() and weightedMad() into plain functions (were generic functions). o Removed unnecessary usage of '::'. Version: 0.11.2 [2014-11-09] o SPEEDUP: Implemented indexByRow() in native code and it is no longer a generic function, but a regular function, which is also faster to call. The first argument of indexByRow() has been changed to 'dim' such that one should use indexByRow(dim(X)) instead of indexByRow(X) as in the past. The latter form is still supported, but deprecated. o Added allocVector(), allocMatrix() and allocArray() for faster allocation numeric vectors, matrices and arrays, particularly when filled with non-missing values. Version: 0.11.1 [2014-11-07] o Better support for long vectors. o ROBUSTNESS: Although unlikely, with long vectors support for binCounts() and binMeans() it is possible that a bin gets a higher count than what can be represented by an R integer (.Machine$integer.max=2^31-1). If that happens, an informative warning is generated and the bin count is set to .Machine$integer.max. If this happens for binMeans(), the corresponding mean is still properly calculated and valid. o PRECISION: Using greater floating-point precision in more internal intermediate calculations, where possible. o CLEANUP: Cleanup and harmonized the internal C API such there are two well defined API levels. The high-level API is called by R via .Call() and takes care of most of the argument validation and construction of the return value. This function dispatch to functions in the low-level API based on data type(s) and other arguments. The low-level API is written to work with basic C data types only. o BUG FIX: Package incorrectly redefined R_xlen_t on R (>= 3.0.0) systems where LONG_VECTOR_SUPPORT is not supported. Version: 0.11.0 [2014-11-02] o Added sumOver() and meanOver(), which are notably faster versions of sum(x[idxs]) and mean(x[idxs]). Moreover, instead of having to do sum(as.numeric(x)) to avoid integer overflow when 'x' is an integer vector, one can do sumOver(x, mode="numeric"), which avoids the extra copy created when coercing to numeric (this numeric copy is also twice as large as the integer vector). Added package tests and benchmark reports for these functions. Version: 0.10.4 [2014-11-01] o SPEEDUP: Made anyMissing(), logSumExp(), (col|row)Medians(), (col|row)Counts() slightly faster by making the native code assign the results directly to the native vector instead of to the R vector, e.g. ansp[i] = v where ansp=REAL(ans) instead of REAL(ans)[i] = v. o Added benchmark reports for anyMissing() and logSumExp(). Version: 0.10.3 [2014-10-01] o BUG FIX: binMeans() returned 0.0 instead of NA_real_ for empty bins. Version: 0.10.2 [2014-09-01] o BUG FIX: On some systems, the package failed to build on R (<= 2.15.3) with compilation error: "redefinition of typedef 'R_xlen_t'". Version: 0.10.1 [2014-06-09] o Added benchmark reports for also non-matrixStats functions col/rowSums() and col/rowMeans(). o Now all colNnn() and rowNnn() methods are benchmarked in a combined report making it possible to also compare colNnn(x) with rowNnn(t(x)). Version: 0.10.0 [2014-06-07] o BUG FIX: The package tests for product() incorrectly assumed that the value of prod(c(NaN, NA)) is uniquely defined. However, as documented in help("is.nan"), it may be NA or NaN depending on R system/platform. o Relaxed some packages tests such that they assert numerical correctness via all.equal() rather than identical(). o Submitted to CRAN. Version: 0.9.7 [2014-06-05] o BUG FIX: Introduced a bug in v0.9.5 causing col- and rowVars() and hence also col- and rowSds() to return garbage. Add package tests for these now. o Submitted to CRAN. Version: 0.9.6 [2014-06-04] o SPEEDUP: Now col- and rowProds() utilizes new product() function. o SPEEDUP: Added product() for calculating the product of a numeric vector via the logarithm. o Added signTabulate() for tabulating the number of negatives, zeros, positives and missing values. For doubles, the number of negative and positive infinite values are also counted. Version: 0.9.5 [2014-06-04] o Added argument 'method' to col- and rowProds() for controlling how the product is calculated. o SPEEDUP: Package is now byte compiled. o SPEEDUP: Made weightedMedian() a plain function (was an S3 method). o SPEEDUP: Made rowProds() and rowTabulates() notably faster. o SPEEDUP: Now rowCounts(), rowAnys(), rowAlls() and corresponding column methods can search for any value in addition to the default TRUE. The search for a matching integer or double value is done in native code, which is notably faster (and more memory efficient because it avoids creating any new objects). o SPEEDUP: Made colVars() and colSds() notably faster and rowVars() and rowSds() a slightly bit faster. o SPEEDUP: Turned more S4 methods into S3 methods, e.g. rowCounts(), rowAlls(), rowAnys(), rowTabulates() and rowCollapse(). o Added benchmark reports, e.g. matrixStats:::benchmark('colMins'). o CLEANUP: Now only exporting plain functions and generic functions. Version: 0.9.4 [2014-05-23] o SPEEDUP: Turned several S4 methods into S3 methods, e.g. indexByRow(), madDiff(), sdDiff() and varDiff(). Version: 0.9.3 [2014-04-26] o Added argument 'trim' to madDiff(), sdDiff() and varDiff(). Version: 0.9.2 [2014-04-04] o BUG FIX: The native code of binMeans(x, bx) would try to access an out-of-bounds value of argument 'y' iff 'x' contained elements that are left of all bins in 'bx'. This bug had no impact on the results and since no assignment was done it should also not crash/ core dump R. This was discovered thanks to new memtests (ASAN and valgrind) provided by CRAN. Version: 0.9.1 [2014-03-31] o BUG FIX: rowProds() would throw "Error in rowSums(isNeg) : 'x' must be an array of at least two dimensions" on matrices where all rows contained at least one zero. Thanks to Roel Verbelen at KU Leuven for the report. Version: 0.9.0 [2014-03-26] o Added weighedVar() and weightedSd(). Version: 0.8.14 [2013-11-23] o MEMORY: Updated all functions to do a better job of cleaning out temporarily allocated objects as soon as possible such that the garbage collector can remove them sooner, iff wanted. This increase the chance for a smaller memory footprint. o Submitted to CRAN. Version: 0.8.13 [2013-10-08] o Added argument 'right' to binCounts() and binMeans() to specify whether binning should be done by (u,v] or [u,v). Added system tests validating the correctness of the two cases. o Bumped up package dependencies. Version: 0.8.12 [2013-09-26] o SPEEDUP: Now utilizing anyMissing() everywhere possible. Version: 0.8.11 [2013-09-21] o ROBUSTNESS: Now importing 'loadMethod' from 'methods' package such that 'matrixStats' S4-based methods also work when 'methods' is not loaded, e.g. when 'Rscript' is used, cf. Section 'Default packages' in 'R Installation and Administration'. o ROBUSTNESS: Updates package system tests such that the can run with only the 'base' package loaded. Version: 0.8.10 [2013-09-15] o CLEANUP: Now only importing two functions from the 'methods' package. o Bumped up package dependencies. Version: 0.8.9 [2013-08-29] o CLEANUP: Now the package startup message acknowledges argument 'quietly' of library()/require(). Version: 0.8.8 [2013-07-29] o DOCUMENTATION: The dimension of the return value was swapped in help("rowQuantiles"). Version: 0.8.7 [2013-07-28] o SPEEDUP: Made (col|row)Mins() and (col|row)Maxs() much faster. o BUG FIX: rowRanges(x) on an Nx0 matrix would give an error. Same for colRanges(x) on an 0xN matrix. Added system tests for these and other special cases. Version: 0.8.6 [2013-07-20] o Forgot to declare S3 methods (col|row)WeightedMedians(). o Bumped up package dependencies. Version: 0.8.5 [2013-05-25] o Minor speedup of (col|row)Tabulates() by replacing rm() calls with NULL assignments. Version: 0.8.4 [2013-05-20] o CRAN POLICY: Now all Rd \usage{} lines are at most 90 characters long. Version: 0.8.3 [2013-05-10] o SPEEDUP: binCounts() and binMeans() now uses Hoare's Quicksort for presorting 'x' before counting/averaging. They also no longer test in every iteration (=for every data point) whether the last bin has been reached or not, but only after completing a bin. Version: 0.8.2 [2013-05-02] o DOCUMENTATION: Minor corrections and updates to help pages. Version: 0.8.1 [2013-05-02] o BUG FIX: Native code of logSumExp() used an invalid check for missing value of an integer argument. Detected by Brian Ripley upon CRAN submission. Version: 0.8.0 [2013-05-01] o Added logSumExp(lx) and (col|row)LogSumExps(lx) for accurately computing of log(sum(exp(lx))) for standalone vectors, and row and column vectors of matrices. Thanks to Nakayama (Japan) for the suggestion and contributing a draft in R. Version: 0.7.1 [2013-04-23] o Added argument 'preserveShape' to colRanks(). For backward compatibility the default is preserveShape=FALSE, but it may change in the future. o BUG FIX: Since v0.6.4, (col|row)Ranks() gave the incorrect results for integer matrices with missing values. o BUG FIX: Since v0.6.4, (col|row)Medians() for integers would calculate ties as floor(tieAvg). Version: 0.7.0 [2013-01-14] o Now (col|row)Ranks() support "max" (default), "min" and "average" for argument 'ties.method'. Added system tests validation these cases. Thanks Peter Langfelder (UCLA) for contributing this. Version: 0.6.4 [2013-01-13] o Added argument 'ties.method' to rowRanks() and colRanks(), but still only support for "max" (as before). o ROBUSTNESS: Lots of cleanup of the internal/native code. Native code for integer and double cases have been harmonized and are now generated from a common code template. This was inspired by code contributions from Peter Langfelder (UCLA). Version: 0.6.3 [2013-01-13] o Added anyMissing() for data type 'raw', which always returns FALSE. o ROBUSTNESS: Added system test for anyMissing(). o ROBUSTNESS: Now S3 methods are declared in the namespace. Version: 0.6.2 [2012-11-15] o CRAN POLICY: Made example(weightedMedian) faster. Version: 0.6.1 [2012-10-10] o BUG FIX: In some cases binCounts() and binMeans() could try to go past the last bin resulting a core dump. o BUG FIX: binCounts() and binMeans() would return random/garbage values for bins that were beyond the last data point. Version: 0.6.0 [2012-10-04] o Added binMeans() for fast sample-mean calculation in bins. Thanks to Martin Morgan at the Fred Hutchinson Cancer Research Center, Seattle, for contributing the core code for this. o Added binCounts() for fast element counting in bins. Version: 0.5.3 [2012-09-10] o CRAN POLICY: Replaced the .Internal(psort(...)) call with a call to a new internal partial sorting function, which utilizes the native rPsort() part of the R internals. Version: 0.5.2 [2012-07-02] o Updated package dependencies to match CRAN. Version: 0.5.1 [2012-06-25] o GENERALIZATION: Now (col|row)Prods() handle missing values. o BUG FIX: In certain cases, (col|row)Prods() would return NA instead of 0 for some elements. Added a redundancy test for the case. Thanks Brenton Kenkel at University of Rochester for reporting on this. o Now this package only imports methods. Version: 0.5.0 [2012-04-16] o Added weightedMad() from aroma.core v2.5.0. o Added weightedMedian() from aroma.light v1.25.2. o This package no longer depends on the aroma.light package for any of its functions. o Now this package only imports R.methodsS3, meaning it no longer loads R.methodsS3 when it is loaded. Version: 0.4.5 [2012-03-19] o Updated the default argument 'centers' of rowMads()/colMads() to explicitly be (col|row)Medians(x,...). The default behavior has not changed. Version: 0.4.4 [2012-03-05] o BUG FIX: colMads() would return the incorrect estimates. This bug was introduced in matrixStats v0.4.0 (2011-11-11). o ROBUSTNESS: Added system/redundancy tests for rowMads()/colMads(). o CRAN: Made the system tests "lighter" by default, but full tests can still be run, cf. tests/*.R scripts. Version: 0.4.3 [2011-12-11] o BUG FIX: rowMedians(..., na.rm=TRUE) did not handle NaN (only NA). The reason for this was the the native code used ISNA() to test for NA and NaN, but it should have been ISNAN(), which is opposite to how is.na() and is.nan() at the R level work. Added system tests for this case. Version: 0.4.2 [2011-11-29] o Added rowAvgsPerColSet() and colAvgsPerRowSet(). Version: 0.4.1 [2011-11-25] o Added help pages with an example to rowIQRs() and colIQRs(). o Added example to rowQuantiles(). o BUG FIX: rowIQRs() and colIQRs() would return the 25% and the 75% quantiles, not the difference between them. Thanks Pierre Neuvial at CNRS, Evry, France for the report. Version: 0.4.0 [2011-11-11] o Added rowRanks() and colRanks(). Thanks Hector Corrada Bravo (University of Maryland) and Harris Jaffee (John Hopkins). o Dropped the previously introduced expansion of 'center' in rowMads() and colMads(). It added unnecessary overhead if not needed. Version: 0.3.0 [2011-10-13] o SPEEDUP/LESS MEMORY: colMedians(x) no longer uses rowMedians(t(x)); instead there is now an optimized native-code implementation. Also, colMads() utilizes the new colMedians() directly. This improvement was kindly contributed by Harris Jaffee at Biostatistics of John Hopkins, USA. o Added additional unit tests for colMedians() and rowMedians(). Version: 0.2.2 [2010-10-06] o Now the result of (col|row)Quantiles() contains column names. Version: 0.2.1 [2010-04-05] o Added a startup message when package is loaded. o CLEAN UP: Removed obsolete internal .First.lib() and .Last.lib(). Version: 0.2.0 [2010-03-30] o DOCUMENTATION: Fixed some incorrect cross references. Version: 0.1.9 [2010-02-03] o BUG FIX: (col|row)WeightedMeans(..., na.rm=TRUE) would incorrectly treat missing values as zeros. Added corresponding redundancy tests (also for the median case). Thanks Pierre Neuvial for reporting this. Version: 0.1.8 [2009-11-13] o BUG FIX: colRanges(x) would return a matrix of wrong dimension if 'x' did not have any missing values. This would affect all functions relying on colRanges(), e.g. colMins() and colMaxs(). Added a redundancy test for this case. Thanks Pierre Neuvial at UC Berkeley for reporting this. o BUG FIX: (col|row)Ranges() return a matrix with dimension names. Version: 0.1.7 [2009-06-20] WORKAROUND: Cannot use "%#x" in rowTabulates() when creating the column names of the result matrix. It gave an error OSX with R v2.9.0 devel (2009-01-13 r47593b) current the OSX server at R-forge. Version: 0.1.6 [2009-06-17] o Updated the Rdoc example for rowWeightedMedians() to run conditionally on aroma.light, which is only a suggested package - not a required one. This in order to prevent R CMD check to fail on CRAN, which prevents it for building binaries (as it currently happens on their OSX servers). Version: 0.1.5 [2009-02-04] o BUG FIX: For some errors in rowOrderStats(), the stack would not become UNPROTECTED before calling error. Version: 0.1.4 [2009-02-02] o Added methods (col|row)Weighted(Mean|Median)s() for weighted averaging. o Added more Rdoc comments. o Package passes R CMD check flawlessly. Version: 0.1.3 [2008-07-30] o Added (col|row)Tabulates() for integer and raw matrices. o BUG FIX: rowCollapse(x) was broken and returned the wrong elements. Version: 0.1.2 [2008-04-13] o Added (col|row)Collapse(). o Added varDiff(), sdDiff() and madDiff(). o Added indexByRow(). Version: 0.1.1 [2008-03-25] o Added (col|row)OrderStats(). o Added (col|row)Ranges() and (col|row)(Min|Max)s(). o Added colMedians(). o Now anyMissing() support most data types as structures. Version: 0.1.0 [2007-11-26] o Imported the rowNnn() methods from Biobase. o Created. matrixStats/R/0000755000175100001440000000000012776336765013042 5ustar hornikusersmatrixStats/R/signTabulate.R0000644000175100001440000000232012736212640015561 0ustar hornikusers############################################################################/** # @RdocFunction signTabulate # @alias signTabulate # # @title "Calculates the number of negative, zero, positive and missing values" # # @synopsis # # \description{ # @get "title" in a @numeric vector. For @double vectors, the number of # negative and positive infinite values are also counted. # } # # \arguments{ # \item{x}{a @numeric @vector.} # \item{idxs}{A @vector indicating subset of elements # to operate over. If @NULL, no subsetting is done.} # \item{...}{Not used.} # } # # \value{ # Returns a @named @numeric @vector. # } # # \seealso{ # @see "base::sign". # } # # @author "HB" # # @keyword internal #*/############################################################################ signTabulate <- function(x, idxs=NULL, ...) { res <- .Call(C_signTabulate, x, idxs); names(res) <- c("-1", "0", "+1", "NA", "-Inf", "+Inf")[1:length(res)]; res; } # signTabulate() ############################################################################ # HISTORY: # 2015-05-31 [DJ] # o Supported subsetted computation. # 2014-06-04 [HB] # o Created. ############################################################################ matrixStats/R/pkgStartupMessage.R0000644000175100001440000000332512726216471016623 0ustar hornikusers## covr: skip=all pkgStartupMessage <- function(..., quietly=NA) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Infer 'quietly' from argument 'argument' in library() call? # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (is.na(quietly)) { quietly <- FALSE # Just in case the below won't work one day due to R updates... tryCatch({ # The default, if not found quietly <- formals(base::library)$quietly # Identify the environment/frame of interest by making sure # it at least contains all the arguments of source(). argsToFind <- names(formals(base::library)) # Scan the call frames/environments backwards... srcfileList <- list() for (ff in sys.nframe():0) { env <- sys.frame(ff) # Does the environment look like a library() environment? exist <- sapply(argsToFind, FUN=exists, envir=env, inherits=FALSE) if (!all(exist)) { # Nope, then skip to the next one next } # Was argument 'quietly' specified? missing <- eval(expression(missing(quietly)), envir=env) if (!missing) { quietly <- get("quietly", envir=env, inherits=FALSE) break } # ...otherwise keep searching due to nested library() calls. } # for (ff ...) }, error = function() {}) } # if (is.na(quietly) # Output message? if (!quietly) { packageStartupMessage(...) } } ############################################################################ # HISTORY: # 2015-01-27 # o Copied from R.methodsS3. Here it will only be used internally. ############################################################################ matrixStats/R/diff2.R0000644000175100001440000000324512736212271014140 0ustar hornikusers############################################################################/** # @RdocFunction diff2 # # @title "Fast lagged differences" # # @synopsis # # \description{ # Computes the lagged and iterated differences. # } # # \arguments{ # \item{x}{A @numeric @vector of length N.} # \item{idxs}{A @vector indicating subset of elements # to operate over. If @NULL, no subsetting is done.} # \item{lag}{An @integer specifying the lag.} # \item{differences}{An @integer specifying the order of difference.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric @vector of length N - \code{differences}. # } # # @examples "../incl/diff2.Rex" # # \seealso{ # @see "base::diff". # } # # @author # # @keyword univar # @keyword internal #*/############################################################################ diff2 <- function(x, idxs=NULL, lag=1L, differences=1L, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'lag': if (length(lag) != 1L) { stop("Argument 'lag' is not a scalar: ", length(lag)) } # Argument 'differences': if (length(differences) != 1L) { stop("Argument 'differences' is not a scalar: ", length(differences)) } lag <- as.integer(lag) differences <- as.integer(differences) .Call(C_diff2, x, idxs, lag, differences); } # diff2() ############################################################################ # HISTORY: # 2015-05-26 [DJ] # o Supported subsetted computation. # 2014-12-29 # o Created. ############################################################################ matrixStats/R/rowRanks.R0000644000175100001440000001237612736212600014755 0ustar hornikusers###########################################################################/** # @RdocFunction rowRanks # @alias colRanks # # @title "Gets the rank of each row (column) of a matrix" # # \description{ # @get "title". # } # # \usage{ # @usage rowRanks # @usage colRanks # } # # \arguments{ # \item{x}{A @numeric or @integer NxK @matrix.} # \item{rows, cols}{A @vector indicating subset of rows (and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{ties.method}{A @character string specifying how ties are treated. # For details, see below.} # \item{dim.}{An @integer @vector of length two specifying the # dimension of \code{x}, also when not a @matrix.} # \item{preserveShape}{A @logical specifying whether the @matrix # returned should preserve the input shape of \code{x}, or not.} # \item{...}{Not used.} # } # # \value{ # An @integer @matrix is returned. # The \code{rowRanks()} function always returns an NxK @matrix, # where N (K) is the number of rows (columns) whose ranks are calculated. # # The \code{colRanks()} function returns an NxK @matrix, # if \code{preserveShape = TRUE}, otherwise a KxN @matrix. # # %% The mode of the returned matrix is @integer, except for # %% \code{ties.method == "average"} when it is @double. # } # # \details{ # The row ranks of \code{x} are collected as \emph{rows} # of the result matrix. # # The column ranks of \code{x} are collected as \emph{rows} # if \code{preserveShape = FALSE}, otherwise as \emph{columns}. # # The implementation is optimized for both speed and memory. # To avoid coercing to @doubles (and hence memory allocation), there # is a unique implementation for @integer matrices. # It is more memory efficient to do # \code{colRanks(x, preserveShape=TRUE)} than # \code{t(colRanks(x, preserveShape=FALSE))}. # # Any @names of \code{x} are ignored and absent in the result. # } # # \section{Missing and non- values}{ # These are ranked as \code{NA}, as with \code{na.last="keep"} # in the @see "base::rank" function. # } # # \section{Ties}{ # When some values are equal ("ties"), argument \code{ties.method} # specifies what their ranks should be. # If \code{ties.method} is \code{"max"}, ties # are ranked as the maximum value. # If \code{ties.method} is \code{"average"}, ties are ranked # by their average. # If \code{ties.method} is \code{"max"} (\code{"min"}), ties # are ranked as the maximum (minimum) value. # If \code{ties.method} is \code{"average"}, ties are ranked # by their average. # For further details, see @see "base::rank". # } # # \author{ # Hector Corrada Bravo and Harris Jaffee. # Peter Langfelder for adding 'ties.method' support. # Henrik Bengtsson adapted the original native implementation # of \code{rowRanks()} from Robert Gentleman's \code{rowQ()} # in the \pkg{Biobase} package. # } # # \seealso{ # @see "base::rank". # For developers, see also Section 'Utility functions' in # 'Writing R Extensions manual', particularly the native functions # \code{R_qsort_I()} and \code{R_qsort_int_I()}. # } # # @keyword array # @keyword iteration # @keyword robust # @keyword univar #*/########################################################################### rowRanks <- function(x, rows=NULL, cols=NULL, ties.method=c("max", "average", "min"), dim.=dim(x), ...) { # Argument 'ties.method': ties.method <- ties.method[1L] if (is.element("flavor", names(list(...)))) { .Deprecated(old="Argument 'flavor' of rowRanks()", package="matrixStats") } tiesMethod <- charmatch(ties.method, c("max", "average", "min"), nomatch=0L) if (tiesMethod == 0L) { stop("Unknown value of argument 'ties.method': ", ties.method) } dim. <- as.integer(dim.) # byrow=TRUE .Call(C_rowRanksWithTies, x, dim., rows, cols, tiesMethod, TRUE) } colRanks <- function(x, rows=NULL, cols=NULL, ties.method=c("max", "average", "min"), dim.=dim(x), preserveShape=FALSE, ...) { # Argument 'ties.method': ties.method <- ties.method[1L] if (is.element("flavor", names(list(...)))) { .Deprecated(old="Argument 'flavor' of rowRanks()", package="matrixStats") } # Argument 'preserveShape' preserveShape <- as.logical(preserveShape) tiesMethod <- charmatch(ties.method, c("max", "average", "min"), nomatch=0L) if (tiesMethod == 0L) { stop("Unknown value of argument 'ties.method': ", ties.method) } dim. <- as.integer(dim.) # byrow=FALSE y <- .Call(C_rowRanksWithTies, x, dim., rows, cols, tiesMethod, FALSE) if (!preserveShape) y <- t(y) y } ############################################################################ # HISTORY: # 2015-05-30 [DJ] # o Supported subsetted computation. # 2014-12-17 [HB] # o CLEANUP: Made col- and rowRanks() plain R functions. # 2014-11-15 [HB] # o SPEEDUP: No longer using match.arg() due to its overhead. # 2013-04-23 [HB] # o Added argument 'preserveShape' to colRanks(), cf. private email # 'row- and colRanks in package matrixStats' on 2012-10-05 until # 2013-02-28. # 2013-01-14 [HB] # o Added internal support for rowRanks() with ties "max", "min" and # "average". # 2011-11-11 [HB] # o Added '...' to generic functions rowRanks() and colRanks(). # 2011-10-17 [HJ] # o Added rowRanks and colRanks(). ############################################################################ matrixStats/R/rowCollapse.R0000644000175100001440000000560412726216471015446 0ustar hornikusers###########################################################################/** # @RdocFunction rowCollapse # @alias colCollapse # # @title "Extracts one cell per row (column) from a matrix" # # \description{ # @get "title". # The implementation is optimized for memory and speed. # } # # \usage{ # @usage rowCollapse # @usage colCollapse # } # # \arguments{ # \item{x}{An NxK @matrix.} # \item{idxs}{An index @vector of (maximum) length N (K) specifying the # columns (rows) to be extracted.} # \item{rows, cols}{A @vector indicating subset of rows (and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{dim.}{An @integer @vector of length two specifying the # dimension of \code{x}, also when not a @matrix.} # \item{...}{Not used.} # } # # \value{ # Returns a @vector of length N (K). # } # # @examples "../incl/rowCollapse.Rex" # # @author "HB" # # \seealso{ # \emph{Matrix indexing} to index elements in matrices and arrays, # cf. @see "base::[". # } # # @keyword utilities #*/########################################################################### rowCollapse <- function(x, idxs, rows=NULL, dim.=dim(x), ...) { # Apply subset if (is.vector(x)) dim(x) <- dim. if (!is.null(rows)) { x <- x[rows,,drop=FALSE] idxs <- idxs[rows] } dim. <- dim(x) # Argument 'idxs': idxs <- rep(idxs, length.out=dim.[1L]) # Columns of interest cols <- 0:(dim.[2L]-1L) cols <- cols[idxs] # Calculate column-based indices idxs <- dim.[1L] * cols + seq_len(dim.[1L]) cols <- NULL # Not needed anymore x[idxs] } colCollapse <- function(x, idxs, cols=NULL, dim.=dim(x), ...) { # Apply subset if (is.vector(x)) dim(x) <- dim. if (!is.null(cols)) { x <- x[,cols,drop=FALSE] idxs <- idxs[cols] } dim. <- dim(x) # Argument 'idxs': idxs <- rep(idxs, length.out=dim.[2L]) # Rows of interest rows <- seq_len(dim.[1L]) rows <- rows[idxs] # Calculate column-based indices idxs <- dim.[1L] * 0:(dim.[2L]-1L) + rows rows <- NULL # Not needed anymore x[idxs] } ############################################################################ # HISTORY: # 2015-05-29 [DJ] # o Supported subsetted computation. # 2014-12-19 [HB] # o CLEANUP: Made col- and rowCollapse() plain R functions. # 2014-11-15 # o SPEEDUP: Made calculation of colOffsets faster. # o SPEEDUP: Now colCollapse(x) no longer utilizes rowCollapse(t(x)). # 2014-06-02 # o Made rowCollapse() an S3 method (was S4). # 2013-11-23 # o MEMORY: rowCollapse() does a better job cleaning out allocated # objects sooner. # 2008-06-13 # o BUG FIX: rowCollapse(x) was broken and returned the wrong elements. # 2008-04-13 # o Added Rdocs. # o Added colCollapse(). # 2007-10-21 # o Created. ############################################################################ matrixStats/R/weightedMean.R0000644000175100001440000000553012736212733015551 0ustar hornikusers############################################################################/** # @RdocFunction weightedMean # # \encoding{latin1} # # @title "Weighted Arithmetic Mean" # # @synopsis # # \description{ # Computes the weighted sample mean of a numeric vector. # } # # \arguments{ # \item{x}{a @numeric @vector containing the values whose weighted mean is # to be computed.} # \item{w}{a vector of weights the same length as \code{x} giving the weights # to use for each element of \code{x}. Negative weights are treated # as zero weights. Default value is equal weight to all values.} # \item{idxs}{A @vector indicating subset of elements # to operate over. If @NULL, no subsetting is done.} # \item{na.rm}{a logical value indicating whether @NA values in # \code{x} should be stripped before the computation proceeds, # or not. If @NA, no check at all for @NAs is done. # Default value is @NA (for efficiency).} # \item{refine}{If @TRUE and \code{x} is @numeric, then extra effort is # used to calculate the average with greater numerical precision, # otherwise not.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric scalar. # If \code{x} is of zero length, then \code{NaN} is returned, # which is consistent with @see "base::mean". # } # # @examples "../incl/weightedMean.Rex" # # \section{Missing values}{ # This function handles missing values consistently # @see "stats::weighted.mean". More precisely, # if \code{na.rm=FALSE}, then any missing values in either \code{x} # or \code{w} will give result \code{NA_real_}. # If \code{na.rm=TRUE}, then all \code{(x,w)} data points for which # \code{x} is missing are skipped. Note that if both \code{x} and # \code{w} are missing for a data points, then it is also skipped # (by the same rule). However, if only \code{w} is missing, then # the final results will always be \code{NA_real_} regardless of # \code{na.rm}. # } # # \seealso{ # @see "base::mean" and @see "stats::weighted.mean". # } # # @author # # @keyword "univar" # @keyword "robust" #*/############################################################################ weightedMean <- function(x, w=NULL, idxs=NULL, na.rm=FALSE, refine=FALSE, ...) { # Argument 'refine': refine <- as.logical(refine) # Argument 'w': if (is.null(w)) { ## We won't fall back to stats::mean(), because it's has some overhead ## and it doesn't support refine=FALSE. w <- rep(1, times=length(x)) } else { w <- as.numeric(w) } .Call(C_weightedMean, x, w, idxs, na.rm, refine) } # weightedMean() ############################################################################### # HISTORY: # 2015-05-25 [DJ] # o Supported subsetted computation. # 2014-12-08 # o Created. ############################################################################### matrixStats/R/rowProds.R0000644000175100001440000001104712726216471014771 0ustar hornikusers###########################################################################/** # @RdocFunction rowProds # @alias rowProds # @alias colProds # @alias product # # @title "Calculates the product for each row (column) in a matrix" # # \description{ # @get "title". # } # # \usage{ # @usage rowProds # @usage colProds # @usage product # } # # \arguments{ # \item{x}{A @numeric NxK @matrix.} # \item{idxs, rows, cols}{A @vector indicating subset of elements (or rows and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{na.rm}{If @TRUE, missing values are ignored, otherwise not.} # \item{method}{A @character string specifying how each product # is calculated.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric @vector of length N (K). # } # # \details{ # If \code{method="expSumLog"}, then then @see "product" function # is used, which calculates the produce via the logarithmic transform # (treating negative values specially). This improves the precision # and lowers the risk for numeric overflow. # If \code{method="direct"}, the direct product is calculated via # the @see "base::prod" function. # } # # \section{Missing values}{ # Note, if \code{method="expSumLog"}, \code{na.rm=FALSE}, and \code{x} # contains missing values (@NA or @NaN), then the calculated value # is also missing value. # Note that it depends on platform whether @NaN or @NA is returned # when an @NaN exists, cf. @see "base::is.nan". # } # # @author "HB" # # @keyword array # @keyword iteration # @keyword robust # @keyword univar #*/########################################################################### rowProds <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, method=c("direct", "expSumLog"), ...) { # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] # Preallocate result (zero:ed by default) n <- nrow(x) y <- double(length=n) # Nothing todo? if (n == 0L) return(y) # Argument 'method': method <- method[1L] # How to calculate product? if (method == "expSumLog") { prod <- product } else if (method == "direct") { } else { stop("Unknown value of argument 'method': ", method) } for (ii in seq_len(n)) { y[ii] <- prod(x[ii,,drop=TRUE], na.rm=na.rm) } y; } # rowProds() colProds <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, method=c("direct", "expSumLog"), ...) { # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] # Preallocate result (zero:ed by default) n <- ncol(x) y <- double(length=n) # Nothing todo? if (n == 0L) return(y) # Argument 'method': method <- method[1L] # How to calculate product? if (method == "expSumLog") { prod <- product } else if (method == "direct") { } else { stop("Unknown value of argument 'method': ", method) } for (ii in seq_len(n)) { y[ii] <- prod(x[,ii,drop=TRUE], na.rm=na.rm) } y } # colProds() ############################################################################ # HISTORY: # 2015-06-03 [DJ] # o Supported subsetted computation. # 2014-11-15 [HB] # o SPEEDUP: No longer using match.arg() due to its overhead. # 2014-06-04 [HB] # o Now col- and rowProds() utilizes new product() function. # o Added argument 'method' to col- and rowProds(). # 2014-06-02 [HB] # o Now rowProds() uses rowCounts(x) when 'x' is logical. # o Now rowProds() avoids subsetting rows unless needed. # 2014-03-31 [HB] # o BUG FIX: rowProds() would throw "Error in rowSums(isNeg) : 'x' must # be an array of at least two dimensions" on matrices where all rows # contained at least on zero. Thanks to Roel Verbelen at KU Leuven # for the report. # 2013-11-23 [HB] # o MEMORY: rowProbs() does a better job cleaning out allocated # objects sooner. # 2012-06-25 [HB] # o GENERALIZATION: Now row- and colProds() handles missing values. # o BUG FIX: In certain cases, row- and colProds() would return NA instead # of 0 for some elements. Thanks Brenton Kenkel at University of # Rochester for reporting on this. # 2008-07-30 [HB] # o Now it is only rows without zeros for which the calculation is # actually performed. # 2008-03-26 [HB] # o Created. ############################################################################ matrixStats/R/x_OP_y.R0000644000175100001440000000502512736212756014351 0ustar hornikusers############################################################################/** # @RdocFunction x_OP_y # @alias x_OP_y # @alias t_tx_OP_y # # @title "Fast calculation of 'z <- x OP y' and 'z <- t(t(x) OP y)'" # # \usage{ # @usage x_OP_y # @usage t_tx_OP_y # } # # \description{ # @get "title", where OP can be +, -, *, and /. # For + and *, na.rm=TRUE will drop missing values first. # } # # \arguments{ # \item{x}{A @numeric NxK @matrix.} # \item{y}{A @numeric @vector of length L.} # \item{OP}{A @character specifying which operator to use.} # \item{xrows, xcols}{A @vector indicating subset of rows (and/or columns) # to operate over 'x'. If @NULL, no subsetting is done.} # \item{idxs}{A @vector indicating subset of elements # to operate over 'y'. If @NULL, no subsetting is done.} # \item{commute}{If @TRUE, 'y OP x' ('t(y OP t(x))') is calculated, # otherwise 'x OP y' ('t(t(x) OP y)').} # \item{na.rm}{If @TRUE, missing values are ignored, otherwise not.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric NxK @matrix. # } # # \section{Missing values}{ # If \code{na.rm=TRUE}, then missing values are "dropped" before applying # the operator to each pair of values. For instance, if \code{x[1,1]} is # a missing value, then the result of \code{x[1,1] + y[1]} equals # \code{y[1]}. If also \code{y[1]} is a missing value, then the result # is a missing value. This only applies to additions and multiplications. # For subtractions and divisions, argument \code{na.rm} is ignored. # } # # @examples "../incl/x_OP_y.Rex" # # @author # # @keyword internal #*/############################################################################ x_OP_y <- function(x, y, OP, xrows=NULL, xcols=NULL, yidxs=NULL, commute=FALSE, na.rm=FALSE) { commute <- as.logical(commute) na.rm <- as.logical(na.rm) op <- charmatch(OP, c("+", "-", "*", "/"), nomatch=0L) stopifnot(op > 0L) .Call(C_x_OP_y, x, y, dim(x), op, xrows, xcols, yidxs, commute, na.rm, TRUE, FALSE) } # x_OP_y() t_tx_OP_y <- function(x, y, OP, xrows=NULL, xcols=NULL, yidxs=NULL, commute=FALSE, na.rm=FALSE) { commute <- as.logical(commute) na.rm <- as.logical(na.rm) op <- charmatch(OP, c("+", "-", "*", "/"), nomatch=0L) stopifnot(op > 0L) .Call(C_x_OP_y, x, y, dim(x), op, xrows, xcols, yidxs, commute, na.rm, TRUE, TRUE) } # t_tx_OP_y() ############################################################################ # HISTORY: # 2014-11-24 [HB] # o Created. ############################################################################ matrixStats/R/validateIndices.R0000644000175100001440000000156212736212715016241 0ustar hornikusers############################################################################/** # @RdocFunction validateIndices # # @title "Validate indices" # # \description{ # Computes validated positive indices from given indices. # } # # \arguments{ # \item{idxs}{A @integer @vector. If @NULL, all indices are considered.} # \item{maxIdx}{The possible max index.} # \item{allowOutOfBound}{Allow positive out of bound to indicate @NA.} # } # # \usage{ # @usage validateIndices # } # # \value{ # Returns a validated integers list indicating the indices. # } # # @examples "../incl/validateIndices.Rex" # # @keyword internal #*/############################################################################ validateIndices <- function(idxs=NULL, maxIdx, allowOutOfBound=TRUE) { ans <- .Call(C_validate, idxs, maxIdx, allowOutOfBound) if (is.null(ans)) ans <- seq_len(maxIdx) ans } matrixStats/R/rowRanges.R0000644000175100001440000000647412736212570015126 0ustar hornikusers###########################################################################/** # @RdocFunction rowRanges # @alias colRanges # @alias rowMins # @alias rowMaxs # @alias colMins # @alias colMaxs # # @title "Gets the range of values in each row (column) of a matrix" # # \description{ # @get "title". # } # # \usage{ # @usage rowRanges # @usage colRanges # @usage rowMins # @usage colMins # @usage rowMaxs # @usage colMaxs # } # # \arguments{ # \item{x}{A @numeric NxK @matrix.} # \item{rows, cols}{A @vector indicating subset of rows (and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{na.rm}{If @TRUE, @NAs are excluded first, otherwise not.} # \item{dim.}{An @integer @vector of length two specifying the # dimension of \code{x}, also when not a @matrix.} # \item{...}{Not used.} # } # # \value{ # \code{rowRanges()} (\code{colRanges()}) returns a # @numeric Nx2 (Kx2) @matrix, where # N (K) is the number of rows (columns) for which the ranges are # calculated. # # \code{rowMins()/rowMaxs()} (\code{colMins()/colMaxs()}) returns a # @numeric @vector of length N (K). # } # # @author "HB" # # \seealso{ # @see "rowOrderStats" and @see "base::pmin.int". # } # # @keyword array # @keyword iteration # @keyword robust # @keyword univar #*/########################################################################### rowRanges <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(x), ...) { dim. <- as.integer(dim.) na.rm <- as.logical(na.rm) .Call(C_rowRanges, x, dim., rows, cols, 2L, na.rm, TRUE) } rowMins <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(x), ...) { dim. <- as.integer(dim.) na.rm <- as.logical(na.rm) .Call(C_rowRanges, x, dim., rows, cols, 0L, na.rm, TRUE) } rowMaxs <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(x), ...) { dim. <- as.integer(dim.) na.rm <- as.logical(na.rm) .Call(C_rowRanges, x, dim., rows, cols, 1L, na.rm, TRUE) } colRanges <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(x), ...) { dim. <- as.integer(dim.) na.rm <- as.logical(na.rm) .Call(C_colRanges, x, dim., rows, cols, 2L, na.rm, TRUE) } colMins <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(x), ...) { dim. <- as.integer(dim.) na.rm <- as.logical(na.rm) .Call(C_colRanges, x, dim., rows, cols, 0L, na.rm, TRUE) } colMaxs <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(x), ...) { dim. <- as.integer(dim.) na.rm <- as.logical(na.rm) .Call(C_colRanges, x, dim., rows, cols, 1L, na.rm, TRUE) } ############################################################################ # HISTORY: # 2015-05-25 [DJ] # o Supported subsetted computation. # 2014-12-17 [HB] # o CLEANUP: Made col- and rowRanges() plain R functions. # 2014-11-16 # o SPEEDUP: Implemented in native code. # 2013-07-28 # o SPEEDUP: Made (col|row)Mins() and (col|row)Maxs() faster. # o BUG FIX: rowRanges(x) on an Nx0 matrix 'x' would give an error. # Ditto for colRanges(x). # 2009-02-01 # o BUG FIX: colRanges(x) would give an error if nrow(x) == 0. # 2008-03-25 # o Since colOrderStats() cannot handle missing values we use the slower # colRanges() for the case when na.rm=TRUE. # o Added {row|col}{Min|Max}s(). # o Created {row|col}Ranges() for scratch. Handles NAs. ############################################################################ matrixStats/R/sumOver.R0000644000175100001440000000536212736212647014617 0ustar hornikusers############################################################################/** # @RdocFunction sumOver # # @title "Fast sum over subset of vector elements" # # @synopsis # # \description{ # Computes the sum of all or a subset of values. # } # # \arguments{ # \item{x}{A @numeric @vector of length N.} # \item{idxs}{A @vector indicating subset of elements # to operate over. If @NULL, no subsetting is done.} # \item{na.rm}{If @TRUE, missing values are skipped, otherwise not.} # \item{mode}{A @character string specifying the data type of the # return value. Default is to use the same mode as argument # \code{x}.} # \item{...}{Not used.} # } # # \value{ # Returns a scalar of the data type specified by argument \code{mode}. # If \code{mode="integer"}, then integer overflow occurs if the # \emph{sum} is outside the range of defined integer values. # } # # \details{ # \code{sumOver(x, idxs)} gives equivalent results as # \code{sum(x[idxs])}, but is faster and more memory efficient # since it avoids the actual subsetting which requires copying # of elements and garbage collection thereof. # # Furthermore, \code{sumOver(x, mode="double")} is equivalent to # \code{sum(as.numeric(x))}, but is much more memory efficient when # \code{x} is an @integer vector. # } # # @examples "../incl/sumOver.Rex" # # \seealso{ # @see "base::sum". # To efficiently average over a subset, see @see "meanOver". # } # # @author # # @keyword univar # @keyword internal #*/############################################################################ sumOver <- function(x, idxs=NULL, na.rm=FALSE, mode=typeof(x), ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'x': if (!is.numeric(x)) { stop("Argument 'x' is not numeric: ", mode(x)); } n <- length(x); # Argument 'na.rm': if (!is.logical(na.rm)) { stop("Argument 'na.rm' is not logical: ", mode(na.rm)); } # Argument 'mode': mode <- mode[1L] modeI <- charmatch(mode, c("integer", "double"), nomatch=0L) if (modeI == 0L) { stop("Unknown value of argument 'mode': ", mode) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Summing # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - .Call(C_sumOver, x, idxs, na.rm, modeI); } # sumOver() ############################################################################ # HISTORY: # 2015-05-31 [DJ] # o Supported subsetted computation. # 2014-11-15 [HB] # o SPEEDUP: No longer using match.arg() due to its overhead. # 2014-11-02 [HB] # o Created. ############################################################################ matrixStats/R/rowWeightedMeans.R0000644000175100001440000001572212726216471016432 0ustar hornikusers###########################################################################/** # @RdocFunction rowWeightedMeans # @alias colWeightedMeans # # @title "Calculates the weighted means for each row (column) in a matrix" # # \description{ # @get "title". # } # # \usage{ # @usage rowWeightedMeans # @usage colWeightedMeans # } # # \arguments{ # \item{x}{A @numeric NxK @matrix.} # \item{w}{A @numeric @vector of length K (N).} # \item{rows, cols}{A @vector indicating subset of rows (and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{na.rm}{If @TRUE, missing values are excluded from the calculation, # otherwise not.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric @vector of length N (K). # } # # \details{ # The implementations of these methods are optimized for both speed # and memory. # If no weights are given, the corresponding # \code{rowMeans()}/\code{colMeans()} is used. # } # # @examples "../incl/rowWeightedMeans.Rex" # # @author "HB" # # \seealso{ # See \code{rowMeans()} and \code{colMeans()} in @see "base::colSums" # for non-weighted means. # See also @see "stats::weighted.mean". # } # # @keyword array # @keyword iteration # @keyword robust # @keyword univar #*/########################################################################### rowWeightedMeans <- function(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'w': hasWeights <- !is.null(w); if (hasWeights) { n <- ncol(x); if (length(w) != n) { stop("The length of argument 'w' is does not match the number of column in 'x': ", length(w), " != ", n); } if (!is.numeric(w)) { stop("Argument 'w' is not numeric: ", mode(w)); } if (any(!is.na(w) & w < 0)) { stop("Argument 'w' has negative weights."); } } # Apply subset on x if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] # Apply subset on w if (!is.null(w) && !is.null(cols)) w <- w[cols] if (hasWeights) { # Allocate results m <- nrow(x); if (m == 0L) return(double(0L)); # Drop entries with zero weight? ...but keep NAs idxs <- which(is.na(w) | w != 0); nw <- length(idxs); if (nw == 0L) { return(rep(NaN, times=m)); } else if (nw < n) { w <- w[idxs]; x <- x[,idxs,drop=FALSE]; } idxs <- NULL; # Not needed anymore # Has missing values? if (na.rm) { # Really? na.rm <- anyMissing(x); } if (na.rm) { # Indices of missing values nas <- which(is.na(x)); # Weight matrix W <- matrix(w, nrow=nrow(x), ncol=ncol(x), byrow=TRUE); w <- NULL; # Not needed anymore W[nas] <- NA; wS <- rowSums(W, na.rm=TRUE); # Standarized weights summing to one w/out missing values W[nas] <- 0; W <- W / wS; x[nas] <- 0; nas <- NULL; # Not needed anymore x <- W * x; W <- NULL; # Not needed anymore } else { wS <- sum(w); # Standardize weights summing to one. w <- w / wS; # Weighted values ## SLOW: for (rr in 1:m) x[rr,] <- w * x[rr,,drop=TRUE]; ## FAST: x <- t_tx_OP_y(x, w, OP="*", na.rm=FALSE) w <- NULL; # Not needed anymore } # Here we know there are no missing value in the new 'x' res <- rowSums(x, na.rm=FALSE); } else { res <- rowMeans(x, na.rm=na.rm); } res; } # rowWeightedMeans() colWeightedMeans <- function(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'w': hasWeights <- !is.null(w); if (hasWeights) { n <- nrow(x); if (length(w) != n) { stop("The length of argument 'w' is does not match the number of rows in 'x': ", length(w), " != ", n); } if (!is.numeric(w)) { stop("Argument 'w' is not numeric: ", mode(w)); } if (any(!is.na(w) & w < 0)) { stop("Argument 'w' has negative weights."); } } # Apply subset on x if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] # Apply subset on w if (!is.null(w) && !is.null(rows)) w <- w[rows] if (hasWeights) { # Allocate results m <- ncol(x); if (m == 0L) return(double(0L)); # Drop entries with zero weight? ...but keep NAs idxs <- which(is.na(w) | w != 0); nw <- length(idxs); if (nw == 0L) { return(rep(NaN, times=m)); } else if (nw < n) { w <- w[idxs]; x <- x[idxs,,drop=FALSE]; } idxs <- NULL; # Not needed anymore # Has missing values? if (na.rm) { # Really? na.rm <- anyMissing(x); } if (na.rm) { # Indices of missing values nas <- which(is.na(x)); # Weight matrix W <- matrix(w, nrow=nrow(x), ncol=ncol(x), byrow=FALSE); w <- NULL; # Not needed anymore W[nas] <- NA; wS <- colSums(W, na.rm=TRUE); # Standarized weights summing to one w/out missing values W[nas] <- 0; for (cc in 1:m) { W[,cc] <- W[,cc,drop=TRUE] / wS[cc]; } x[nas] <- 0; nas <- NULL; # Not needed anymore x <- W * x; W <- NULL; # Not needed anymore } else { wS <- sum(w); # Standardize weights summing to one. w <- w / wS; # Weighted values x <- w*x; ## SLIGHTLY SLOWER: x <- x_OP_y(x, w, OP="*"); w <- NULL; # Not needed anymore } # Here we know there are no missing value in the new 'x' res <- colSums(x, na.rm=FALSE); } else { res <- colMeans(x, na.rm=na.rm); } res; } # colWeightedMeans() ############################################################################## # HISTORY: # 2015-05-31 [DJ] # o Supported subsetted computation. # 2014-12-19 [HB] # o CLEANUP: Made col- and rowWeightedMeans() plain R functions. # 2013-11-29 # o BUG FIX: (col|row)WeightedMeans() with all zero weights gave an # invalid result. # 2013-11-23 # o MEMORY: Now (col|row)WeightedMeans() clean out allocated objects sooner. # 2010-02-03 # o BUG FIX: (col|row)WeightedMeans(..., na.rm=TRUE) would incorrectly treat # missing values as zeros. Thanks Pierre Neuvial for reporting this. # 2008-02-01 # o Added special implementation for column version. # o Added Rdoc comments. # o Created. ############################################################################## matrixStats/R/meanOver.R0000644000175100001440000000534512736212321014721 0ustar hornikusers############################################################################/** # @RdocFunction meanOver # # @title "Fast averaging over subset of vector elements" # # @synopsis # # \description{ # Computes the sample mean of all or a subset of values. # } # # \arguments{ # \item{x}{A @numeric @vector of length N.} # \item{idxs}{A @vector indicating subset of elements # to operate over. If @NULL, no subsetting is done.} # \item{na.rm}{If @TRUE, missing values are skipped, otherwise not.} # \item{refine}{If @TRUE and \code{x} is @numeric, then extra effort is # used to calculate the average with greater numerical precision, # otherwise not.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric scalar. # } # # \details{ # \code{meanOver(x, idxs)} gives equivalent results as # \code{mean(x[idxs])}, but is faster and more memory efficient # since it avoids the actual subsetting which requires copying # of elements and garbage collection thereof. # # If \code{x} is @numeric and \code{refine=TRUE}, then a two-pass scan # is used to calculate the average. The first scan calculates the total # sum and divides by the number of (non-missing) values. In the second # scan, this average is refined by adding the residuals towards the first # average. The @see "base::mean" uses this approach. # \code{meanOver(..., refine=FALSE)} is almost twice as fast as # \code{meanOver(..., refine=TRUE)}. # } # # @examples "../incl/meanOver.Rex" # # \seealso{ # @see "base::mean". # To efficiently sum over a subset, see @see "sumOver". # } # # @author # # @keyword univar # @keyword internal #*/############################################################################ meanOver <- function(x, idxs=NULL, na.rm=FALSE, refine=TRUE, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'x': if (!is.numeric(x)) { stop("Argument 'x' is not numeric: ", mode(x)); } # Argument 'na.rm': if (!is.logical(na.rm)) { stop("Argument 'na.rm' is not logical: ", mode(na.rm)); } # Argument 'refine': if (!is.logical(refine)) { stop("Argument 'refine' is not logical: ", mode(refine)); } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Averaging # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - .Call(C_meanOver, x, idxs, na.rm, refine); } # meanOver() ############################################################################ # HISTORY: # 2015-05-28 [DJ] # o Supported subsetted computation. # 2014-11-02 [HB] # o Created. ############################################################################ matrixStats/R/indexByRow.R0000644000175100001440000000254412736212277015247 0ustar hornikusers###########################################################################/** # @RdocFunction indexByRow # # @title "Translates matrix indices by rows into indices by columns" # # \description{ # @get "title". # } # # \usage{ # @usage indexByRow # } # # \arguments{ # \item{dim}{A @numeric @vector of length two specifying the length # of the "template" matrix.} # \item{idxs}{A @vector of indices. If @NULL, all indices are returned.} # \item{...}{Not use.} # } # # \value{ # Returns an @integer @vector of indices. # } # # @examples "../incl/indexByRow.Rex" # # @author "HB" # # @keyword iteration # @keyword logic #*/########################################################################### indexByRow <- function(dim, idxs=NULL, ...) { if (is.matrix(dim)) { # BACKWARD COMPATIBILITY: Keep for a while, but deprecate # in the future. dim <- dim(dim) } else { dim <- as.integer(dim) } if (!is.null(idxs)) idxs <- as.integer(idxs) .Call(C_indexByRow, dim, idxs) } ############################################################################## # HISTORY: # 2014-11-09 # o Now indexByRow() is a plain R function (was a generic function). # o Implemented in C. # 2014-05-23 # o CLEANUP: Made indexByRow() an S3 rather than S4 generic. # 2007-04-12 # o Created. ############################################################################## matrixStats/R/binCounts.R0000644000175100001440000001043612760445566015126 0ustar hornikusers############################################################################/** # @RdocFunction binCounts # # @title "Fast element counting in non-overlapping bins" # # @synopsis # # \description{ # Counts the number of elements in non-overlapping bins # } # # \arguments{ # \item{x}{A @numeric @vector of K positions for to be binned and counted.} # \item{idxs}{A @vector indicating subset of elements # to operate over. If @NULL, no subsetting is done.} # \item{bx}{A @numeric @vector of B+1 ordered positions specifying # the B > 0 bins \code{[bx[1],bx[2])}, \code{[bx[2],bx[3])}, ..., # \code{[bx[B],bx[B+1])}.} # \item{right}{If @TRUE, the bins are right-closed (left open), # otherwise left-closed (right open).} # \item{...}{Not used.} # } # # \value{ # Returns an @integer @vector of length B with non-negative integers. # } # # \details{ # \code{binCounts(x, bx, right=TRUE)} gives equivalent results as # \code{rev(binCounts(-x, bx=rev(-bx), right=FALSE))}, but is # faster and more memory efficient. # } # # \section{Missing and non-finite values}{ # Missing values in \code{x} are ignored/dropped. # Missing values in \code{bx} are not allowed and gives an error. # } # # \seealso{ # An alternative for counting occurrences within bins is # @see "graphics::hist", e.g. \code{hist(x, breaks=bx, plot=FALSE)$counts}. # That approach is ~30-60\% slower than \code{binCounts(..., right=TRUE)}. # # To count occurrences of indices \code{x} (positive @integers) in # \code{[1,B]}, use \code{tabulate(x, nbins=B)}, where \code{x} does # \emph{not} have to be sorted first. # For details, see @see "base::tabulate". # # To average values within bins, see @see "binMeans". # } # # @author "HB" # # @keyword "univar" #*/############################################################################ binCounts <- function(x, idxs=NULL, bx, right=FALSE, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'x': if (!is.numeric(x)) { stop("Argument 'x' is not numeric: ", mode(x)); } # Argument 'bx': if (!is.numeric(bx)) { stop("Argument 'bx' is not numeric: ", mode(bx)); } if (any(is.infinite(bx))) { stop("Argument 'bx' must not contain Inf values."); } if (is.unsorted(bx)) { stop("Argument 'bx' is not ordered."); } # Apply subset if (!is.null(idxs)) x <- x[idxs] # Argument 'right': right <- as.logical(right); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Preprocessing of x # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Drop missing values keep <- which(!is.na(x)); if (length(keep) < length(x)) { x <- x[keep]; } keep <- NULL; # Not needed anymore # Order x (by increasing x). # If 'x' is already sorted, the overhead of (re)sorting is # relatively small. x <- sort.int(x, method="quick"); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Bin # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - x <- as.numeric(x); bx <- as.numeric(bx); .Call(C_binCounts, x, bx, right); } # binCounts() ############################################################################ # HISTORY: # 2015-05-28 [DJ] # o Supported subsetted computation. # 2014-12-29 [HB] # o SPEEDUP: Now binCounts() and binMeans() uses is.unsorted() instead # of o <- order(); any(diff(o) != 1L). # 2014-12-17 [HB] # o CLEANUP: Made binCounts() and binMeans() plain R functions. # 2013-11-24 [HB] # o DOCUMENTATION: Added reference to base::tabulate(). # 2013-11-23 [HB] # o MEMORY: binCounts() cleans out more temporary variables as soon as # possible such that the garbage collector can remove them sooner. # 2012-05-10 [HB] # o DOCUMENTATION: Now help(binCounts) cross references hist(), which is # almost as fast. Thanks Ilari Scheinin (Finland) for pointing this out. # o SPEEDUP: Now binMeans() and binCounts() use Hoare's Quicksort # method for sorting 'x'. # 2012-10-03 [HB] # o Created. ############################################################################ matrixStats/R/rowOrderStats.R0000644000175100001440000000543112736212530015765 0ustar hornikusers###########################################################################/** # @RdocFunction rowOrderStats # @alias colOrderStats # # @title "Gets an order statistic for each row (column) in a matrix" # # \description{ # @get "title". # } # # \usage{ # @usage rowOrderStats # @usage colOrderStats # } # # \arguments{ # \item{x}{A @numeric NxK @matrix.} # \item{rows, cols}{A @vector indicating subset of rows (and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{which}{An @integer index in [1,K] ([1,N]) indicating which # order statistic to be returned.} # \item{dim.}{An @integer @vector of length two specifying the # dimension of \code{x}, also when not a @matrix.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric @vector of length N (K). # } # # \details{ # The implementation of \code{rowOrderStats()} is optimized for both # speed and memory. # To avoid coercing to @doubles (and hence memory allocation), there # is a unique implementation for @integer matrices. # } # # \section{Missing values}{ # This method does \emph{not} handle missing values, that is, the result # corresponds to having \code{na.rm=FALSE} (if such an argument would # be available). # } # # \author{ # The native implementation of \code{rowOrderStats()} was adopted # by Henrik Bengtsson from Robert Gentleman's \code{rowQ()} # in the \pkg{Biobase} package. # } # # \seealso{ # See \code{rowMeans()} in @see "base::colSums". # } # # @keyword array # @keyword iteration # @keyword robust # @keyword univar #*/########################################################################### rowOrderStats <- function(x, rows=NULL, cols=NULL, which, dim.=dim(x), ...) { dim. <- as.integer(dim.) # Check missing values if (anyMissing(x)) { stop("Argument 'x' must not contain missing value"); } which <- as.integer(which) .Call(C_rowOrderStats, x, dim., rows, cols, which); } colOrderStats <- function(x, rows=NULL, cols=NULL, which, dim.=dim(x), ...) { dim. <- as.integer(dim.) # Check missing values if (anyMissing(x)) { stop("Argument 'x' must not contain missing value"); } which <- as.integer(which) .Call(C_colOrderStats, x, dim., rows, cols, which); } ############################################################################ # HISTORY: # 2015-06-03 [DJ] # o Supported subsetted computation. # 2014-12-19 [HB] # o CLEANUP: Made col- and rowOrderStats() plain R functions. # 2014-11-16 # o SPEEDUP: Now colOrderStats() also is implemented in native code. # 2008-03-25 # o Added colOrderStats(). # o Renamed from rowQuantiles() to rowOrderStats(), especially because it # is not returning quantiles like quantile(). # o Created (again?) ############################################################################ matrixStats/R/rowCumsums.R0000644000175100001440000000531612736212436015336 0ustar hornikusers###########################################################################/** # @RdocFunction rowCumsums # @alias rowCumsums # @alias colCumsums # @alias rowCumprods # @alias colCumprods # @alias rowCummins # @alias colCummins # @alias rowCummaxs # @alias colCummaxs # # @title "Cumulative sums, products, minima and maxima for each row (column) in a matrix" # # \description{ # @get "title". # } # # \usage{ # @usage rowCumsums # @usage colCumsums # @usage rowCumprods # @usage colCumprods # @usage rowCummins # @usage colCummins # @usage rowCummaxs # @usage colCummaxs # } # # \arguments{ # \item{x}{A @numeric NxK @matrix.} # \item{rows, cols}{A @vector indicating subset of elements (or rows and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{dim.}{An @integer @vector of length two specifying the # dimension of \code{x}, also when not a @matrix.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric NxK @matrix of the same mode as \code{x}. # } # # @examples "../incl/rowCumsums.Rex" # # @author "HB" # # \seealso{ # See @see "base::cumsum", @see "base::cumprod", # @see "base::cummin", and @see "base::cummax". # } # # @keyword array # @keyword iteration # @keyword univar #*/########################################################################### rowCumsums <- function(x, rows=NULL, cols=NULL, dim.=dim(x), ...) { dim <- as.integer(dim.); .Call(C_rowCumsums, x, dim, rows, cols, TRUE) } colCumsums <- function(x, rows=NULL, cols=NULL, dim.=dim(x), ...) { dim <- as.integer(dim.); .Call(C_rowCumsums, x, dim, rows, cols, FALSE) } rowCumprods <- function(x, rows=NULL, cols=NULL, dim.=dim(x), ...) { dim <- as.integer(dim.); .Call(C_rowCumprods, x, dim, rows, cols, TRUE) } colCumprods <- function(x, rows=NULL, cols=NULL, dim.=dim(x), ...) { dim <- as.integer(dim.); .Call(C_rowCumprods, x, dim, rows, cols, FALSE) } rowCummins <- function(x, rows=NULL, cols=NULL, dim.=dim(x), ...) { dim <- as.integer(dim.); .Call(C_rowCummins, x, dim, rows, cols, TRUE) } colCummins <- function(x, rows=NULL, cols=NULL, dim.=dim(x), ...) { dim <- as.integer(dim.); .Call(C_rowCummins, x, dim, rows, cols, FALSE) } rowCummaxs <- function(x, rows=NULL, cols=NULL, dim.=dim(x), ...) { dim <- as.integer(dim.); .Call(C_rowCummaxs, x, dim, rows, cols, TRUE) } colCummaxs <- function(x, rows=NULL, cols=NULL, dim.=dim(x), ...) { dim <- as.integer(dim.); .Call(C_rowCummaxs, x, dim, rows, cols, FALSE) } ############################################################################ # HISTORY: # 2015-05-27 [DJ] # o Supported subsetted computation. # 2014-11-26 [HB] # o Created. ############################################################################ matrixStats/R/benchmark.R0000644000175100001440000000131612726216471015102 0ustar hornikusersbenchmark <- function(fcn, tags=NULL, path=NULL, workdir="reports", envir=parent.frame(), ...) { requireNamespace("R.rsp") || stop("R.rsp not installed.") if (is.function(fcn)) { fcn <- deparse(substitute(fcn)) } if (is.null(path)) { path <- system.file("benchmarking", package="matrixStats") } fullname <- paste(c(fcn, tags), collapse=",") filename <- sprintf("%s.md.rsp", fullname) pathname <- file.path(path, filename) R.rsp::rfile(pathname, workdir=workdir, envir=envir, ...) } # benchmark() ############################################################################ # HISTORY: # 2014-06-02 # o Created. ############################################################################ matrixStats/R/rowAvgsPerColSet.R0000644000175100001440000001413712726216471016366 0ustar hornikusers###########################################################################/** # @RdocFunction rowAvgsPerColSet # @alias colAvgsPerRowSet # # @title "Applies a row-by-row (column-by-column) averaging function to equally-sized subsets of matrix columns (rows)" # # \description{ # @get "title". # Each subset is averaged independently of the others. # } # # \usage{ # @usage rowAvgsPerColSet # @usage colAvgsPerRowSet # } # # \arguments{ # \item{X}{A @numeric NxM @matrix.} # \item{W}{An optional @numeric NxM @matrix of weights.} # \item{rows, cols}{A @vector indicating subset of rows (and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{S}{An @integer KxJ @matrix specifying the J subsets. Each # column holds K column (row) indices for the corresponding subset.} # \item{FUN}{The row-by-row (column-by-column) @function used to average # over each subset of \code{X}. This function must accept a @numeric # NxK (KxM) @matrix and the @logical argument \code{na.rm} (which is # automatically set), and return a @numeric @vector of length N (M).} # \item{...}{Additional arguments passed to then \code{FUN} @function.} # \item{tFUN}{If @TRUE, the NxK (KxM) @matrix passed to \code{FUN()} # is transposed first.} # } # # \value{ # Returns a @numeric JxN (MxJ) @matrix, # where row names equal \code{rownames(X)} (\code{colnames(S)}) # and column names \code{colnames(S)} (\code{colnames(X)}). # } # # \details{ # If argument \code{S} is a single column vector with indices # \code{1:N}, then \code{rowAvgsPerColSet(X, S=S, FUN=rowMeans)} # gives the same result as \code{rowMeans(X)}. # Analogously, for \code{rowAvgsPerColSet()}. # } # # @examples "../incl/rowAvgsPerColSet.Rex" # # @author "HB" # # @keyword internal # @keyword utilities #*/########################################################################### rowAvgsPerColSet <- function(X, W=NULL, rows=NULL, S, FUN=rowMeans, ..., tFUN=FALSE) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'X': if (!is.matrix(X)) { stop("Argument 'X' is not a matrix: ", class(X)[1L]); } dimX <- dim(X); # Argument 'W': hasW <- !is.null(W); if (hasW) { if (!is.matrix(W)) { stop("Argument 'W' is not a matrix: ", class(W)[1L]); } if (any(dim(W) != dimX)) { stop("Argument 'W' does not have the same dimension as 'X': ", paste(dim(W), collapse="x"), " != ", paste(dimX, collapse="x")); } if (!is.numeric(W)) { stop("Argument 'W' is not numeric: ", mode(W)); } } # Argument 'S': if (!is.matrix(S)) { stop("Argument 'S' is not a matrix: ", class(S)[1L]); } nbrOfSets <- ncol(S); setNames <- colnames(S); # Argument 'FUN': if (!is.function(FUN)) { stop("Argument 'FUN' is not a function: ", mode(S)); } # Apply subset if (!is.null(rows)) { X <- X[rows,,drop=FALSE] if (hasW) W <- W[rows,,drop=FALSE] dimX <- dim(X) } # Argument 'tFUN': tFUN <- as.logical(tFUN); # Check if missing values have to be excluded while averaging na.rm <- (anyMissing(X) || anyMissing(S)); # Record names of dimension rownamesX <- rownames(X); dimnames(X) <- NULL; # Average in sets of columns of X. Z <- apply(S, MARGIN=2L, FUN=function(jj) { # Extract set of columns from X jj <- jj[is.finite(jj)]; Zjj <- X[,jj,drop=FALSE]; jj <- NULL; # Not needed anymore if (tFUN) { Zjj <- t(Zjj); } # Average by weights if (hasW) { Wjj <- W[,jj,drop=FALSE]; Zjj <- FUN(Zjj, W=Wjj, ..., na.rm=na.rm); Wjj <- NULL; # Not needed anymore } else { Zjj <- FUN(Zjj, ..., na.rm=na.rm); } # Sanity check stopifnot(length(Zjj) == dimX[1L]); # Return set average Zjj; }); # Sanity check stopifnot(dim(Z) == c(dimX[1L], nbrOfSets)); # Set names rownames(Z) <- rownamesX; colnames(Z) <- setNames; Z; } # rowAvgsPerColSet() colAvgsPerRowSet <- function(X, W=NULL, cols=NULL, S, FUN=colMeans, tFUN=FALSE, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'X': if (!is.matrix(X)) { stop("Argument 'X' is not a matrix: ", class(X)[1L]); } # Argument 'W': # Argument 'S': if (!is.matrix(S)) { stop("Argument 'S' is not a matrix: ", class(S)[1L]); } # Argument 'FUN': if (!is.function(FUN)) { stop("Argument 'FUN' is not a function: ", mode(S)); } # Apply subset if (!is.null(cols)) { X <- X[,cols,drop=FALSE] if (is.null(W)) W <- W[,cols,drop=FALSE] } # Argument 'tFUN': tFUN <- as.logical(tFUN); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Transpose # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - tX <- t(X); if (is.null(W)) { tW <- NULL; } else { tW <- t(W); } # ... tZ <- rowAvgsPerColSet(X=tX, W=tW, S=S, FUN=FUN, tFUN=!tFUN, ...); tX <- tW <- NULL; # Not needed anymore # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Transpose back # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Z <- t(tZ); tZ <- NULL; # Not needed anymore Z; } # colAvgsPerRowSet() ############################################################################## # HISTORY: # 2015-05-28 [DJ] # o Supported subsetted computation. # 2014-12-17 [HB] # o CLEANUP: Made col- and rowAvgsPerColSet() plain R functions. # 2013-11-23 # o MEMORY: rowAvgsPerColSet() and colAvgsPerRowSet() do a better job # cleaning out allocated objects sooner. # 2011-11-29 # o Added rowAvgsPerColSet() and colAvgsPerRowSet(). # o Created from blockAvg() in the aroma.cn.eval package. ############################################################################## matrixStats/R/allocMatrix.R0000644000175100001440000000302312736212057015421 0ustar hornikusers############################################################################/** # @RdocFunction allocMatrix # @alias allocVector # @alias allocArray # # @title "Allocates an empty vector, matrix or array" # # \usage{ # @usage allocVector # @usage allocMatrix # @usage allocArray # } # # \description{ # @get title faster than the corresponding function in R. # } # # \arguments{ # \item{length, nrow, ncol, dim}{@numerics specifying the dimension of # the created @vector, @matrix or @array.} # \item{value}{A @numeric scalar that all elements will have as value.} # \item{...}{Not used.} # } # # \value{ # Returns a @vector, @matrix and @array respectively of the same data # type as \code{value}. # } # # @author "HB" # # \seealso{ # See also @vector, @matrix and @array. # } # # @keyword programming # @keyword internal #*/############################################################################ allocVector <- function(length, value=0.0, ...) { length <- as.integer(length) .Call(C_allocVector2, length, value) } # allocVector() allocMatrix <- function(nrow, ncol, value=0.0, ...) { nrow <- as.integer(nrow) ncol <- as.integer(ncol) .Call(C_allocMatrix2, nrow, ncol, value) } # allocMatrix() allocArray <- function(dim, value=0.0, ...) { dim <- as.integer(dim) .Call(C_allocArray2, dim, value) } # allocArray() ############################################################################ # HISTORY: # 2014-11-08 [HB] # o Created. ############################################################################ matrixStats/R/rowWeightedMedians.R0000644000175100001440000001145012726216471016741 0ustar hornikusers###########################################################################/** # @RdocFunction rowWeightedMedians # @alias colWeightedMedians # # @title "Calculates the weighted medians for each row (column) in a matrix" # # \description{ # @get "title". # } # # \usage{ # @usage rowWeightedMedians # @usage colWeightedMedians # } # # \arguments{ # \item{x}{A @numeric NxK @matrix.} # \item{w}{A @numeric @vector of length K (N).} # \item{rows, cols}{A @vector indicating subset of rows (and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{na.rm}{If @TRUE, missing values are excluded from the calculation, # otherwise not.} # \item{...}{Additional arguments passed to @see "weightedMedian".} # } # # \value{ # Returns a @numeric @vector of length N (K). # } # # \details{ # The implementations of these methods are optimized for both speed # and memory. # If no weights are given, the corresponding # @see "rowMedians"/\code{colMedians()} is used. # } # # \examples{ # @include "../incl/rowWeightedMedians.Rex" # } # # @author "HB" # # \seealso{ # See @see "rowMedians" and \code{colMedians()} for non-weighted medians. # Internally, @see "weightedMedian" is used. # } # # @keyword array # @keyword iteration # @keyword robust # @keyword univar #*/########################################################################### rowWeightedMedians <- function(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'w': hasWeights <- !is.null(w); if (hasWeights) { n <- ncol(x); if (length(w) != n) { stop("The length of argument 'w' is does not match the number of column in 'x': ", length(w), " != ", n); } if (!is.numeric(w)) { stop("Argument 'w' is not numeric: ", mode(w)); } if (any(!is.na(w) & w < 0)) { stop("Argument 'w' has negative weights."); } } # Apply subset on x if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] # Apply subset on w if (!is.null(w) && !is.null(cols)) w <- w[cols] if (hasWeights) { # Allocate results m <- nrow(x); if (m == 0L) return(double(0L)); res <- apply(x, MARGIN=1L, FUN=function(x) { weightedMedian(x, w=w, na.rm=na.rm, ...); }); w <- NULL; # Not needed anymore } else { res <- rowMedians(x, na.rm=na.rm); } res; } # rowWeightedMedians() colWeightedMedians <- function(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'w': hasWeights <- !is.null(w); if (hasWeights) { n <- nrow(x); if (length(w) != n) { stop("The length of argument 'w' is does not match the number of rows in 'x': ", length(w), " != ", n); } if (!is.numeric(w)) { stop("Argument 'w' is not numeric: ", mode(w)); } if (any(!is.na(w) & w < 0)) { stop("Argument 'w' has negative weights."); } } # Apply subset on x if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] # Apply subset on w if (!is.null(w) && !is.null(rows)) w <- w[rows] if (hasWeights) { # Allocate results m <- ncol(x); if (m == 0L) return(double(0L)); res <- apply(x, MARGIN=2L, FUN=function(x) { weightedMedian(x, w=w, na.rm=na.rm, ...); }); w <- NULL; # Not needed anymore } else { res <- colMedians(x, na.rm=na.rm); } res; } ############################################################################## # HISTORY: # 2015-05-31 [DJ] # o Supported subsetted computation. # 2014-12-19 [HB] # o CLEANUP: Made col- and rowWeightedMedians() plain R functions. # 2013-11-23 # o MEMORY: Now (col|row)WeightedMedians() clean out allocated objects sooner. # 2012-04-16 # o Now {col|row}WeightedMedians() no longer require aroma.light, because # weightedMedian() is now in this package. # 2009-06-17 # o Updated the Rdoc example to run conditionally on aroma.light, which is # only a suggested package - not a required one. This in order to prevent # R CMD check to fail on CRAN (as currently done on their OSX servers). # 2008-02-02 # o Created from rowWeightedMeans.matrix.R. ############################################################################## matrixStats/R/rowMedians.R0000644000175100001440000000522112776226773015272 0ustar hornikusers###########################################################################/** # @RdocFunction rowMedians # @alias colMedians # # @title "Calculates the median for each row (column) in a matrix" # # \description{ # @get "title". # } # # \usage{ # @usage rowMedians # @usage colMedians # } # # \arguments{ # \item{x}{A @numeric NxK @matrix.} # \item{rows, cols}{A @vector indicating subset of rows (and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{na.rm}{If @TRUE, @NAs are excluded first, otherwise not.} # \item{dim.}{An @integer @vector of length two specifying the # dimension of \code{x}, also when not a @matrix.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric @vector of length N (K). # } # # \details{ # The implementation of \code{rowMedians()} and \code{colMedians()} # is optimized for both speed and memory. # To avoid coercing to @doubles (and hence memory allocation), there # is a special implementation for @integer matrices. # That is, if \code{x} is an @integer @matrix, then # \code{rowMedians(as.double(x))} (\code{rowMedians(as.double(x))}) # would require three times the memory of \code{rowMedians(x)} # (\code{colMedians(x)}), but all this is avoided. # } # # @author "HB, HJ" # # \seealso{ # See @see "rowMedians" and \code{colMedians()} for weighted medians. # For mean estimates, see \code{rowMeans()} in @see "base::colSums". # } # # @keyword array # @keyword iteration # @keyword robust # @keyword univar #*/########################################################################### rowMedians <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(x), ...) { dim. <- as.integer(dim.) na.rm <- as.logical(na.rm); hasNAs <- TRUE; # Add as an argument? /2007-08-24 .Call(C_rowMedians, x, dim., rows, cols, na.rm, hasNAs, TRUE); } colMedians <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(x), ...) { dim. <- as.integer(dim.) na.rm <- as.logical(na.rm); hasNAs <- TRUE; # Add as an argument? /2007-08-24 .Call(C_rowMedians, x, dim., rows, cols, na.rm, hasNAs, FALSE); } ############################################################################ # HISTORY: # 2015-07-09 # o Turned into plain functions. # 2015-05-28 [DJ] # o Supported subsetted computation. # 2011-10-13 [HJ] # o In the past, colMedians(x) was accomplished as rowMedians(t(x)); # it is now done directly. # 2008-03-25 # o Added colMedians() - a wrapper around rowMedians() for now. # o Turned into a S4 method as it used to be in Biobase. # 2007-08-14 # o Added argument 'hasNA'. # 2005-11-25 # o Created. ############################################################################ matrixStats/R/rowLogSumExps.R0000644000175100001440000000537412736212472015754 0ustar hornikusers###########################################################################/** # @RdocFunction rowLogSumExps # @alias colLogSumExps # \alias{rowLogSumExps,matrix-method} # \alias{colLogSumExps,matrix-method} # # @title "Accurately computes the logarithm of the sum of exponentials across rows or columns" # # \description{ # @get "title". # } # # \usage{ # @usage rowLogSumExps # @usage colLogSumExps # } # # \arguments{ # \item{lx}{A @numeric NxK @matrix. # Typically \code{lx} are \eqn{log(x)} values.} # \item{rows, cols}{A @vector indicating subset of rows (and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{na.rm}{If @TRUE, any missing values are ignored, otherwise not.} # \item{dim.}{An @integer @vector of length two specifying the # dimension of \code{x}, also when not a @matrix.} # \item{...}{Not used.} # } # # \value{ # A @numeric @vector of length N (K). # } # # \section{Benchmarking}{ # These methods are implemented in native code and have been optimized # for speed and memory. # } # # \author{ # Native implementation by Henrik Bengtsson. # Original R code by Nakayama ??? (Japan). # } # # \seealso{ # To calculate the same on vectors, @see "logSumExp". # } # # @keyword array #*/########################################################################### rowLogSumExps <- function(lx, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(lx), ...) { dim. <- as.integer(dim.) hasNA <- TRUE; res <- .Call(C_rowLogSumExps, lx, dim., rows, cols, as.logical(na.rm), as.logical(hasNA), TRUE); # Preserve names names <- rownames(lx); if (!is.null(names)) { names(res) <- names; } res; } # rowLogSumExps() colLogSumExps <- function(lx, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(lx), ...) { dim. <- as.integer(dim.) hasNA <- TRUE; res <- .Call(C_rowLogSumExps, lx, dim., rows, cols, as.logical(na.rm), as.logical(hasNA), FALSE); # Preserve names names <- colnames(lx); if (!is.null(names)) { names(res) <- names; } res; } # rowLogSumExps() ############################################################################ # HISTORY: # 2015-05-28 [DJ] # o Supported subsetted computation. # 2013-04-30 [HB] # o SPEEDUP: (col|row)LogSumExps() are now implemented natively. # o Renamed to (col|row)LogSumExps(). # 2013-04-29 [HB] # o Added rowSumsInLogspace(). # o Renamed to colSumsInLogspace() which utilizes logSumExp(). # 2013-04-24 [HB] # o Added colSumsP() adopted from log.colSums.exp() code contributed # by Nakayama ??? (Japan) on 2013-01-08. # o Created. ############################################################################ matrixStats/R/rowVars.R0000644000175100001440000001115612736212622014611 0ustar hornikusers###########################################################################/** # @RdocFunction rowVars # @alias rowVars # @alias colVars # \alias{rowVars,matrix-method} # \alias{colVars,matrix-method} # # @title "Variance estimates for each row (column) in a matrix" # # \description{ # @get "title". # } # # \usage{ # @usage rowVars # @usage colVars # } # # \arguments{ # \item{x}{A @numeric NxK @matrix.} # \item{rows, cols}{A @vector indicating subset of rows (and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{center}{(optional) The center, defaults to the row means.} # \item{na.rm}{If @TRUE, @NAs are excluded first, otherwise not.} # \item{dim.}{An @integer @vector of length two specifying the # dimension of \code{x}, also when not a @matrix.} # \item{...}{Additional arguments passed to \code{rowMeans()} and # \code{rowSums()}.} # } # # \value{ # Returns a @numeric @vector of length N (K). # } # # @examples "../incl/rowMethods.Rex" # # @author "HB" # # \seealso{ # See \code{rowMeans()} and \code{rowSums()} in @see "base::colSums". # } # # @keyword array # @keyword iteration # @keyword robust # @keyword univar #*/########################################################################### rowVars <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, center=NULL, dim.=dim(x), ...) { dim. <- as.integer(dim.) if (is.null(center)) { na.rm <- as.logical(na.rm) hasNAs <- TRUE sigma2 <- .Call(C_rowVars, x, dim., rows, cols, na.rm, hasNAs, TRUE); return(sigma2) } # Apply subset on 'x' if (is.vector(x)) dim(x) <- dim. if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] dim. <- dim(x) # Apply subset on 'center' if (!is.null(rows)) center <- center[rows] ncol <- ncol(x); # Nothing to do? if (ncol <= 1L) { x <- rep(NA_real_, times=nrow(x)); return(x); } if (na.rm) { # Count number of missing values in each row nNA <- rowCounts(x, value=NA_real_, na.rm=FALSE); # Number of non-missing values n <- ncol - nNA; hasNA <- any(nNA > 0L); if (hasNA) { # Set NA estimates for rows with less than two observations n[n <= 1L] <- NA_integer_; } else { # No need to check for missing values below na.rm <- FALSE; } } else { # Assuming no missing values n <- ncol; } # Spread x <- x*x; x <- rowMeans(x, na.rm=na.rm); # Variance x <- (x - center^2); x * (n/(n-1)); } colVars <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, center=NULL, dim.=dim(x), ...) { dim. <- as.integer(dim.) if (is.null(center)) { dim. <- as.integer(dim.) na.rm <- as.logical(na.rm) hasNAs <- TRUE sigma2 <- .Call(C_rowVars, x, dim., rows, cols, na.rm, hasNAs, FALSE); return(sigma2) } # Apply subset on 'x' if (is.vector(x)) dim(x) <- dim. if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] dim. <- dim(x) # Apply subset on 'center' if (!is.null(cols)) center <- center[cols] nrow <- nrow(x); # Nothing to do? if (nrow <= 1L) { x <- rep(NA_real_, times=ncol(x)); return(x); } if (na.rm) { # Count number of missing values in each column nNA <- colCounts(x, value=NA_real_, na.rm=FALSE); # Number of non-missing values n <- nrow - nNA; hasNA <- any(nNA > 0L); if (hasNA) { # Set NA estimates for rows with less than two observations n[n <= 1L] <- NA_integer_; } else { # No need to check for missing values below na.rm <- FALSE; } } else { # Assuming no missing values n <- nrow; } # Spread x <- x*x; x <- colMeans(x, na.rm=na.rm); # Variance x <- (x - center^2); x * (n/(n-1)); } ############################################################################ # HISTORY: # 2015-02-09 [HB] # o Now using na.rm=FALSE as the default. # 2014-06-02 [HB] # o Now rowVars() are utilizing rowCounts() instead of rowSums(). # o SPEEDUP: Made colVars() and colSds() significantly faster and # rowVars() and rowSds() a slightly bit faster. # o Now using NA_integer_ instead of NA. # 2008-03-26 [HB] # o Added argument 'center=NULL', cf. base::mad(). # o Created from genefilter::rowVars() by Wolfgang Huber. ############################################################################ matrixStats/R/product.R0000644000175100001440000000060012736212332014614 0ustar hornikusersproduct <- function(x, idxs=NULL, na.rm=FALSE, ...) { .Call(C_productExpSumLog, x, idxs, as.logical(na.rm), TRUE); } # product() ############################################################################ # HISTORY: # 2015-06-03 [DJ] # o Supported subsetted computation. # 2014-06-04 [HB] # o Created. ############################################################################ matrixStats/R/weightedMedian.R0000644000175100001440000001710712736212740016067 0ustar hornikusers############################################################################/** # @RdocFunction weightedMedian # # \encoding{latin1} # # @title "Weighted Median Value" # # @synopsis # # \description{ # Computes a weighted median of a numeric vector. # } # # \arguments{ # \item{x}{a @numeric @vector containing the values whose weighted median is # to be computed.} # \item{w}{a vector of weights the same length as \code{x} giving the weights # to use for each element of \code{x}. Negative weights are treated # as zero weights. Default value is equal weight to all values.} # \item{idxs}{A @vector indicating subset of elements # to operate over. If @NULL, no subsetting is done.} # \item{na.rm}{a logical value indicating whether @NA values in # \code{x} should be stripped before the computation proceeds, # or not. If @NA, no check at all for @NAs is done. # Default value is @NA (for efficiency).} # \item{interpolate}{If @TRUE, linear interpolation is used to get a # consistent estimate of the weighted median.} # \item{ties}{If \code{interpolate == FALSE}, # a character string specifying how to solve ties between two # \code{x}'s that are satisfying the weighted median criteria. # Note that at most two values can satisfy the criteria. # When \code{ties} is \code{"min"}, the smaller value of the two # is returned and when it is \code{"max"}, the larger value is # returned. # If \code{ties} is \code{"mean"}, the mean of the two values is # returned. # Finally, if \code{ties} is \code{"weighted"} (or @NULL) a # weighted average of the two are returned, where the weights are # weights of all values \code{x[i] <= x[k]} and \code{x[i] >= x[k]}, # respectively.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric scalar. # } # # \details{ # For the \code{n} elements \code{x = c(x[1], x[2], ..., x[n])} with positive # weights \code{w = c(w[1], w[2], ..., w[n])} such that \code{sum(w) = S}, # the \emph{weighted median} is defined as the element \code{x[k]} for which # the total weight of all elements \code{x[i] < x[k]} is less or equal to # \code{S/2} and for which the total weight of all elements \code{x[i] > x[k]} # is less or equal to \code{S/2} (c.f. [1]). # # If \code{w} is missing then all elements of \code{x} are given the same # positive weight. If all weights are zero, @NA_real_ is returned. # # If one or more weights are \code{Inf}, it is the same as these weights # have the same weight and the others has zero. This makes things easier for # cases where the weights are result of a division with zero. # # The weighted median solves the following optimization problem: # # \deqn{\alpha^* = \arg_\alpha \min \sum_{k=1}{K} w_k |x_k-\alpha|} # where \eqn{x=(x_1,x_2,\ldots,x_K)} are scalars and # \eqn{w=(w_1,w_2,\ldots,w_K)} are the corresponding "weights" for # each individual \eqn{x} value. # } # # @examples "../incl/weightedMedian.Rex" # # \seealso{ # @see "stats::median", @see "base::mean" and @see "weightedMean". # } # # \references{ # [1] T.H. Cormen, C.E. Leiserson, R.L. Rivest, Introduction to Algorithms, # The MIT Press, Massachusetts Institute of Technology, 1989. # } # # \author{ # Henrik Bengtsson and Ola Hossjer, Centre for Mathematical # Sciences, Lund University. # Thanks to Roger Koenker, Econometrics, University of Illinois, for # the initial ideas. # } # # @keyword "univar" # @keyword "robust" #*/############################################################################ weightedMedian <- function(x, w=NULL, idxs=NULL, na.rm=FALSE, interpolate=is.null(ties), ties=NULL, ...) { # Argument 'x': # Argument 'w': if (is.null(w)) { w <- rep(1, times=length(x)) } else { w <- as.double(w) } # Argument 'na.rm': na.rm <- as.logical(na.rm) if (is.na(na.rm)) na.rm <- FALSE # Argument 'interpolate': interpolate <- as.logical(interpolate) # Argument 'ties': if (is.null(ties)) { tiesC <- 1L } else { if (ties == "weighted") { tiesC <- 1L } else if (ties == "min") { tiesC <- 2L } else if (ties == "max") { tiesC <- 4L } else if (ties == "mean") { tiesC <- 8L } else if (ties == "both") { .Defunct("As of matrixStats (> 0.12.2), weightedMedian(..., interpolate=FALSE, ties=\"both\") is no longer supported. Use ties=\"min\" and then ties=\"max\" to achieve the same result.") } else { stop("Unknown value on 'ties': ", ties) } } .Call(C_weightedMedian, x, w, idxs, na.rm, interpolate, tiesC) } # weightedMedian() ############################################################################### # HISTORY: # 2015-05-31 [DJ] # o Supported subsetted computation. # 2015-01-26 # o CLEANUP: Drop old internally-renamed .weightedMedian(). # 2015-01-01 # o Dropped support for weightedMedian(..., ties="both"). # o BUG FIX: weightedMedian(..., ties="both") would give "Error in # .subset(x, k, k + 1L) : incorrect number of dimensions" if there # was a tie. # 2014-06-03 # o SPEEDUP: Made weightedMedian() a plain function (was an S3 method). # 2013-11-23 # o MEMORY: Now weightedMad() cleans out allocated objects sooner. # 2013-09-26 # o Now utilizing anyMissing(). # 2012-09-10 # o Replaced an .Internal(psort(...)) call with new .psortKM(). # 2012-04-16 # o Added local function qsort() to weightedMedian(), which was adopted # from calculateResidualSet() for ProbeLevelModel in aroma.affymatrix 2.5.0. # o Added local function psortGet() to weightedMedian(). # 2011-04-08 # o Now weightedMedian() returns NA:s of the same mode as argument 'x'. # 2006-04-21 # o Now negative weights are not check for, but instead treated as zero # weights. This was done to minimize the overhead of the function. # o Replace all "[[" and "[" with .subset2() and .subset() to minimize # overhead from method dispatching. # o Remove all calls to rm(). # o It is now possible to specify via na.rm=NA that there are no NAs. # o Small speed up, especially when all weights were the same. # 2006-01-31 # o Rdoc bug fix: 'reference' to 'references'. # 2005-07-26 # o Argument 'interpolate' defaults to TRUE only if 'ties' is NULL. # 2005-06-03 # o Renamed from weighted. median() to weightedMedian(). # o Made into a default function. # o Now using setMethodS3(). # 2003-02-01 # o Update the Rdoc with comments about the method and interpolate argument. # 2002-06-27 # * Reduced memory usage a little bit by calling rm() when possible; minimized # the risk for automatic garbage collecting. # * Improved speed when looking for largest index k s.t. wcum[k] < wmid. # * Improved speed by making use of the R v1.5.0 internal quick sort. # * Made na.rm=FALSE by default for efficiency. # 2002-02-28 # * Calls plain median(x) in two cases: i) all weights are equal, ii) some of # the weights are Inf's. See code for more information. # 2002-02-14 # * BUG FIX: If interpolate==TRUE and sum(lows) == 0 then use k <- 1. # * Added the interpolation version of the weighted median for consistent # estimates. # 2002-02-07 # * Optimized the code for speed. # * Added support for zero and Inf weights. # * Added the 'ties' argument. # * Created! # * Thanks to the following people for helping me out with this one: # - David Brahm, brahm@alum.mit.edu # - David Eppstein, eppstein@ics.uci.edu # - Frank E Harrell Jr, fharrell@virginia.edu # - Markus Jantti, markus.jantti@iki.fi # - Roger Koenker, roger@ysidro.econ.uiuc.edu ############################################################################### matrixStats/R/rowMads.R0000644000175100001440000000716512736212506014570 0ustar hornikusersrowMads <- function(x, rows=NULL, cols=NULL, center=NULL, constant=1.4826, na.rm=FALSE, dim.=dim(x), centers=NULL, ...) { ## BACKWARD COMPATIBILITY: ## - Added to matrixStats 0.14.0. ## - Defunct in matrixStats (>= 0.15.0) if (!is.null(centers)) { center <- centers .Defunct(msg="Argument 'centers' for matrixStats::rowMads() has been renamed to 'center'. Please update code accordingly.") } if (is.null(center)) { dim. <- as.integer(dim.) na.rm <- as.logical(na.rm) constant = as.numeric(constant) hasNAs <- TRUE x <- .Call(C_rowMads, x, dim., rows, cols, constant, na.rm, hasNAs, TRUE) } else { # Apply subset on 'x' if (is.vector(x)) dim(x) <- dim. if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] dim. <- dim(x) # Apply subset on 'center' if (!is.null(rows)) center <- center[rows] x <- x - center if (is.null(dim(x))) dim(x) <- dim. # prevent from dim dropping x <- abs(x) x <- rowMedians(x, na.rm=na.rm, ...) x <- constant*x } x } # rowMads() colMads <- function(x, rows=NULL, cols=NULL, center=NULL, constant=1.4826, na.rm=FALSE, dim.=dim(x), centers=NULL, ...) { ## BACKWARD COMPATIBILITY: ## - Added to matrixStats 0.14.0. ## - Defunct in matrixStats (>= 0.15.0) if (!is.null(centers)) { center <- centers .Defunct(msg="Argument 'centers' for matrixStats::colMads() has been renamed to 'center'. Please update code accordingly.") } if (is.null(center)) { dim. <- as.integer(dim.) na.rm <- as.logical(na.rm) constant = as.numeric(constant) hasNAs <- TRUE x <- .Call(C_rowMads, x, dim., rows, cols, constant, na.rm, hasNAs, FALSE) } else { # Apply subset on 'x' if (is.vector(x)) dim(x) <- dim. if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] dim. <- dim(x) # Apply subset on 'center' if (!is.null(cols)) center <- center[cols] ## SLOW: # for (cc in seq_len(ncol(x))) { # x[,cc] <- x[,cc] - center[cc] # } ## FAST: x <- t_tx_OP_y(x, center, OP="-", na.rm=FALSE) x <- abs(x) x <- colMedians(x, na.rm=na.rm, ...) x <- constant*x } x } # colMads() ############################################################################ # HISTORY: # 2015-05-30 [DJ] # o Supported subsetted computation. # 2015-02-10 [HB] # o CONSISTENCY: Renamed argument 'centers' of col- and rowMads() to # 'center'. This is consistent with col- and rowVars(). Added # backward compatibility code/test for the migration. # 2014-11-17 [HB] # o SPEEDUP: Implemented (col|row)Mads(..., centers=NULL) in native code. # 2012-03-19 [HJ] # o Changed default value of centers to row/colMedians(x,...) # 2012-03-04 [HC] # o BUG FIX: colMads() would return the incorrect estimates. This bug # was introduced in matrixStats v0.4.0 (2011-11-11). # 2011-11-11 [HB] # o Dropped the previously introduced expansion of 'center' in rowMads() # and colMads(). It added unnecessary overhead if not needed. # 2011-10-13 [HJ] # o Implemented colMads() as rowMads() by using the improved colMedians(). # o Now rowMads() expands 'center' to a matrix of the same dimensions as # 'x'. This is not actually necessary, but it enforces that 'x' must be # a matrix. # 2008-03-26 [HB] # o Created. ############################################################################ matrixStats/R/varDiff.R0000644000175100001440000002311312726216471014530 0ustar hornikusers###########################################################################/** # @RdocFunction varDiff # @alias sdDiff # @alias madDiff # @alias iqrDiff # @alias colVarDiffs # @alias rowVarDiffs # @alias colSdDiffs # @alias rowSdDiffs # @alias colMadDiffs # @alias rowMadDiffs # @alias colIQRDiffs # @alias rowIQRDiffs # # @title "Estimation of scale based on sequential-order differences" # # \description{ # @get "title", corresponding to the scale estimates provided by # @see "stats::var", @see "stats::sd", @see "stats::mad" and # @see "stats::IQR". # } # # \usage{ # @usage varDiff # @usage colVarDiffs # @usage rowVarDiffs # # @usage sdDiff # @usage colSdDiffs # @usage rowSdDiffs # # @usage madDiff # @usage colMadDiffs # @usage rowMadDiffs # # @usage iqrDiff # @usage colIQRDiffs # @usage rowIQRDiffs # } # # \arguments{ # \item{x}{A @numeric @vector of length N or a @numeric NxK @matrix.} # \item{idxs, rows, cols}{A @vector indicating subset of elements (or rows and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{na.rm}{If @TRUE, @NAs are excluded, otherwise not.} # \item{diff}{The positional distance of elements for which the # difference should be calculated.} # \item{trim}{A @double in [0,1/2] specifying the fraction of # observations to be trimmed from each end of (sorted) \code{x} # before estimation.} # \item{constant}{A scale factor adjusting for asymptotically # normal consistency.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric @vector of length 1, length N, or length K. # } # # @author "HB" # # \seealso{ # For the corresponding non-differentiated estimates, see # @see "stats::var", @see "stats::sd", @see "stats::mad" and # @see "stats::IQR". # Internally, @see "diff2" is used which is a faster version # of @see "base::diff". # } # # \details{ # Note that n-order difference MAD estimates, just like the ordinary # MAD estimate by @see "stats::mad", apply a correction factor such # that the estimates are consistent with the standard deviation # under Gaussian distributions. # # The interquartile range (IQR) estimates does \emph{not} apply such # a correction factor. If asymptotically normal consistency is wanted, # the correction factor for IQR estimate is \code{1 / (2 * qnorm(3/4))}, # which is half of that used for MAD estimates, which is # \code{1 / qnorm(3/4)}. This correction factor needs to be applied # manually, i.e. there is no \code{constant} argument for the IQR # functions. # } # # \references{ # [1] J. von Neumann et al., \emph{The mean square successive difference}. # Annals of Mathematical Statistics, 1941, 12, 153-162.\cr # } # # @keyword iteration # @keyword robust # @keyword univar #*/########################################################################### varDiff <- function(x, idxs=NULL, na.rm=FALSE, diff=1L, trim=0, ...) { # Apply subset if (!is.null(idxs)) x <- x[idxs] if (na.rm) x <- x[!is.na(x)] # Nothing to do? n <- length(x) if (n <= 1L) return(NA_real_) # Calculate differences? if (diff > 0L) { x <- diff2(x, differences=diff) # Nothing to do? n <- length(x) if (n == 1L) return(NA_real_) } # Trim? if (trim > 0 && n > 0L) { if (anyMissing(x)) return(NA_real_) lo <- floor(n*trim)+1 hi <- (n+1)-lo partial <- unique(c(lo, hi)) x <- sort.int(x, partial=partial) x <- x[lo:hi] } # Estimate var <- var(x, na.rm=FALSE) x <- NULL # Not needed anymore # Correction for the differentiation var / (2^diff) } # varDiff() sdDiff <- function(x, idxs=NULL, na.rm=FALSE, diff=1L, trim=0, ...) { # Apply subset if (!is.null(idxs)) x <- x[idxs] if (na.rm) x <- x[!is.na(x)] # Nothing to do? n <- length(x) if (n <= 1L) return(NA_real_) # Calculate differences? if (diff > 0L) { x <- diff2(x, differences=diff) # Nothing to do? n <- length(x) if (n == 1L) return(NA_real_) } # Trim? if (trim > 0 && n > 0L) { if (anyMissing(x)) return(NA_real_) lo <- floor(n*trim)+1 hi <- (n+1)-lo partial <- unique(c(lo, hi)) x <- sort.int(x, partial=partial) x <- x[lo:hi] } # Estimate sd <- sd(x, na.rm=FALSE) x <- NULL # Not needed anymore # Correction for the differentiation sd / (sqrt(2)^diff) } # sdDiff() madDiff <- function(x, idxs=NULL, na.rm=FALSE, diff=1L, trim=0, constant=1.4826, ...) { # Apply subset if (!is.null(idxs)) x <- x[idxs] if (na.rm) x <- x[!is.na(x)] # Nothing to do? n <- length(x) if (n <= 0L) return(NA_real_) # Calculate differences? if (diff > 0L) { x <- diff2(x, differences=diff) # Nothing to do? n <- length(x) if (n == 1L) return(NA_real_) } # Trim? if (trim > 0 && n > 0L) { if (anyMissing(x)) return(NA_real_) lo <- floor(n*trim)+1 hi <- (n+1)-lo partial <- unique(c(lo, hi)) x <- sort.int(x, partial=partial) x <- x[lo:hi] } # Estimate sd <- mad(x, na.rm=FALSE, constant=constant, ...) x <- NULL # Not needed anymore # Correction for the differentiation sd / (sqrt(2)^diff) } # madDiff() iqrDiff <- function(x, idxs=NULL, na.rm=FALSE, diff=1L, trim=0, ...) { # Apply subset if (!is.null(idxs)) x <- x[idxs] if(na.rm) { x <- x[!is.na(x)] } else if (anyMissing(x)) { return(NA_real_) } # At this point, there should be no missing values # Nothing to do? n <- length(x) if (n == 0L) { return(NA_real_) } else if (n == 1L) { return(0) } # Calculate differences? if (diff > 0L) { x <- diff2(x, differences=diff) # Nothing to do? n <- length(x) if (n == 1L) return(0) } # Trim? if (trim > 0 && n > 0L) { lo <- floor(n*trim)+1 hi <- (n+1)-lo partial <- unique(c(lo, hi)) x <- sort.int(x, partial=partial) x <- x[lo:hi] } # Estimate qs <- quantile(x, probs=c(0.25, 0.75), na.rm=FALSE, names=FALSE, ...) x <- NULL # Not needed anymore iqr <- (qs[2L] - qs[1L]) # Correction for the differentiation iqr / (sqrt(2)^diff) } # iqrDiff() rowVarDiffs <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, diff=1L, trim=0, ...) { # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] apply(x, MARGIN=1L, FUN=varDiff, na.rm=na.rm, diff=diff, trim=trim, ...) } colVarDiffs <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, diff=1L, trim=0, ...) { # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] apply(x, MARGIN=2L, FUN=varDiff, na.rm=na.rm, diff=diff, trim=trim, ...) } rowSdDiffs <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, diff=1L, trim=0, ...) { # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] apply(x, MARGIN=1L, FUN=sdDiff, na.rm=na.rm, diff=diff, trim=trim, ...) } colSdDiffs <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, diff=1L, trim=0, ...) { # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] apply(x, MARGIN=2L, FUN=sdDiff, na.rm=na.rm, diff=diff, trim=trim, ...) } rowMadDiffs <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, diff=1L, trim=0, ...) { # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] apply(x, MARGIN=1L, FUN=madDiff, na.rm=na.rm, diff=diff, trim=trim, ...) } colMadDiffs <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, diff=1L, trim=0, ...) { # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] apply(x, MARGIN=2L, FUN=madDiff, na.rm=na.rm, diff=diff, trim=trim, ...) } rowIQRDiffs <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, diff=1L, trim=0, ...) { # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] apply(x, MARGIN=1L, FUN=iqrDiff, na.rm=na.rm, diff=diff, trim=trim, ...) } colIQRDiffs <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, diff=1L, trim=0, ...) { # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] apply(x, MARGIN=2L, FUN=iqrDiff, na.rm=na.rm, diff=diff, trim=trim, ...) } ############################################################################ # HISTORY: # 2015-06-03 [DJ] # o Supported subsetted computation. # 2015-01-16 # o Added iqrDiff() and (col|row)IQRDiffs(). # 2014-14-19 # o Added (col|row)(Var|Sd|Mad)Diffs() for completeness. # 2014-11-10 # o Turned *Diff() into a function. # 2014-05-24 # o Turned *Diff() into an S3 method (was S4). # 2014-04-26 # o Added argument 'trim' to madDiff(), sdDiff() and varDiff(). # 2013-11-23 # o MEMORY: Now *Diff() cleans out allocated objects sooner. # 2012-07-17 # o Added the reference to von Neumann et al. (1941). # 2009-02-02 # o Added Rdoc comments. # 2008-04-13 # o Added varDiff(), sdDiff() and madDiff(). # 2008-04-10 [on UA930 SFO-LHR] # o Created. ############################################################################ matrixStats/R/rowTabulates.R0000644000175100001440000001177012726216471015631 0ustar hornikusers###########################################################################/** # @RdocFunction rowTabulates # @alias colTabulates # # @title "Tabulates the values in a matrix by row (column)" # # \description{ # @get "title". # } # # \usage{ # @usage rowTabulates # @usage colTabulates # } # # \arguments{ # \item{x}{An @integer or @raw NxK @matrix.} # \item{rows, cols}{A @vector indicating subset of rows (and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{values}{An @vector of J values of count. If @NULL, all (unique) # values are counted.} # \item{...}{Not used.} # } # # \value{ # Returns a NxJ (KxJ) @matrix where # N (K) is the number of row (column) @vectors tabulated and # J is the number of values counted. # } # # @examples "../incl/rowTabulates.Rex" # # @author "HB" # # @keyword utilities #*/########################################################################### rowTabulates <- function(x, rows=NULL, cols=NULL, values=NULL, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'x': if (is.integer(x)) { } else if (is.raw(x)) { } else { stop("Argument 'x' is not of type integer or raw: ", class(x)[1]); } # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] # Argument 'values': if (is.null(values)) { values <- as.vector(x); values <- unique(values); if (is.raw(values)) { values <- as.integer(values); values <- sort(values); # WORKAROUND: Cannot use "%#x" because it gives an error OSX with # R v2.9.0 devel (2009-01-13 r47593b) at R-forge. /HB 2009-06-20 names <- sprintf("%x", values); names <- paste("0x", names, sep=""); values <- as.raw(values); } else { values <- sort(values); names <- as.character(values); } } else { if (is.raw(values)) { names <- sprintf("%x", as.integer(values)); names <- paste("0x", names, sep=""); } else { names <- as.character(values); } } nbrOfValues <- length(values); counts <- matrix(0L, nrow=nrow(x), ncol=nbrOfValues); colnames(counts) <- names; for (kk in seq_len(nbrOfValues)) { counts[,kk] <- rowCounts(x, value=values[kk], ...); } counts; } colTabulates <- function(x, rows=NULL, cols=NULL, values=NULL, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'x': if (is.integer(x)) { } else if (is.raw(x)) { } else { stop("Argument 'x' is not of type integer or raw: ", class(x)[1]); } # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] # Argument 'values': if (is.null(values)) { values <- as.vector(x); values <- unique(values); if (is.raw(values)) { values <- as.integer(values); values <- sort(values); # WORKAROUND: Cannot use "%#x" because it gives an error OSX with # R v2.9.0 devel (2009-01-13 r47593b) at R-forge. /HB 2009-06-20 names <- sprintf("%x", values); names <- paste("0x", names, sep=""); values <- as.raw(values); } else { values <- sort(values); names <- as.character(values); } } else { if (is.raw(values)) { names <- sprintf("%x", as.integer(values)); names <- paste("0x", names, sep=""); } else { names <- as.character(values); } } transpose <- FALSE if (transpose) { ## nbrOfValues <- length(values); ## counts <- matrix(0L, nrow=nbrOfValues, ncol=ncol(x)); ## rownames(counts) <- names; ## for (kk in seq_len(nbrOfValues)) { ## counts[kk,] <- colCounts(x, value=values[kk], ...); ## } } else { nbrOfValues <- length(values); counts <- matrix(0L, nrow=ncol(x), ncol=nbrOfValues); colnames(counts) <- names; for (kk in seq_len(nbrOfValues)) { counts[,kk] <- colCounts(x, value=values[kk], ...); } } counts; } ############################################################################ # HISTORY: # 2015-05-30 [DJ] # o Supported subsetted computation. # 2014-12-19 [HB] # o CLEANUP: Made col- and rowTabulates() plain R functions. # 2014-11-16 # o Now colTabulates(x) no longer calls rowTabulates(t(x)). # 2014-06-02 # o Made rowTabulates() an S3 method (was S4). # o SPEEDUP: Now rowTabulates() utilizes rowCounts(). # 2009-06-20 # WORKAROUND: Cannot use "%#x" in rowTabulates() when creating the column # names of the result matrix. It gav an error OSX with R v2.9.0 devel # (2009-01-13 r47593b) current the OSX server at R-forge. # 2009-02-02 # o Fixed Rdoc comments. # 2008-07-01 # o Created. ############################################################################ matrixStats/R/weightedMad.R0000644000175100001440000001376312726216471015403 0ustar hornikusers############################################################################/** # @RdocFunction weightedMad # @alias rowWeightedMads # @alias colWeightedMads # # @title "Weighted Median Absolute Deviation (MAD)" # # \usage{ # @usage weightedMad # @usage colWeightedMads # @usage rowWeightedMads # } # # \description{ # Computes a weighted MAD of a numeric vector. # } # # \arguments{ # \item{x}{a @numeric @vector containing the values whose weighted MAD is # to be computed.} # \item{w}{a vector of weights the same length as \code{x} giving the weights # to use for each element of \code{x}. Negative weights are treated # as zero weights. Default value is equal weight to all values.} # \item{idxs, rows, cols}{A @vector indicating subset of elements (or rows and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{na.rm}{a logical value indicating whether @NA values in # \code{x} should be stripped before the computation proceeds, # or not. If @NA, no check at all for @NAs is done. # Default value is @NA (for efficiency).} # \item{constant}{A @numeric scale factor, cf. @see "stats::mad".} # \item{center}{Optional @numeric scalar specifying the center # location of the data. If @NULL, it is estimated from data.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric scalar. # } # # \section{Missing values}{ # Missing values are dropped at the very beginning, if argument # \code{na.rm} is @TRUE, otherwise not. # } # # @examples "../incl/weightedMad.Rex" # # \seealso{ # For the non-weighted MAD, see @see "stats::mad". # Internally @see "weightedMedian" is used to # calculate the weighted median. # } # # @author "HB" # # @keyword "univar" # @keyword "robust" #*/############################################################################ weightedMad <- function(x, w=NULL, idxs=NULL, na.rm=FALSE, constant=1.4826, center=NULL, ...) { # No weights? Fall back to non-weighted method. if (is.null(w)) { if (is.null(center)) center <- median(x, na.rm=na.rm) return(mad(x, center=center, constant=constant, na.rm=na.rm, ...)) } # Argument 'x': n <- length(x); # Argument 'w': if (length(w) != n) { stop("The number of elements in arguments 'w' and 'x' does not match: ", length(w), " != ", n); } else if (!is.null(idxs)) { # Apply subset on w w <- w[idxs] } # Apply subset on x if (!is.null(idxs)) { x <- x[idxs] n <- length(x) } # Argument 'na.rm': naValue <- NA; storage.mode(naValue) <- storage.mode(x); # Remove values with zero (and negative) weight. This will: # 1) take care of the case when all weights are zero, # 2) it will most likely speed up the sorting. tmp <- (w > 0); if (!all(tmp)) { x <- .subset(x, tmp); w <- .subset(w, tmp); n <- length(x); } tmp <- NULL; # Not needed anymore # Drop missing values? if (na.rm) { keep <- which(!is.na(x) & !is.na(w)); x <- .subset(x, keep); w <- .subset(w, keep); n <- length(x); keep <- NULL; # Not needed anymore } else if (anyMissing(x)) { return(naValue); } # Are any weights Inf? Then treat them with equal weight and all others # with weight zero. tmp <- is.infinite(w); if (any(tmp)) { keep <- tmp; x <- .subset(x, keep); n <- length(x); w <- rep(1, times=n); keep <- NULL; # Not needed anymore } tmp <- NULL; # Not needed anymore # Are there any values left to calculate the weighted median of? # This is consistent with how stats::mad() works. if (n == 0L) { return(naValue); } else if (n == 1L) { zeroValue <- 0; storage.mode(zeroValue) <- storage.mode(x); return(zeroValue); } # Estimate the mean? if (is.null(center)) { center <- weightedMedian(x, w=w, na.rm=NA); } # Estimate the standard deviation x <- abs(x - center); sigma <- weightedMedian(x, w=w, na.rm=NA); x <- w <- NULL; # Not needed anymore # Rescale for normal distributions sigma <- constant * sigma; sigma; } # weightedMad() rowWeightedMads <- function(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) { # Apply subset on x if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] # Apply subset on w if (!is.null(w) && !is.null(cols)) w <- w[cols] apply(x, MARGIN=1L, FUN=weightedMad, w=w, na.rm=na.rm, ...) } # rowWeightedMads() colWeightedMads <- function(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) { # Apply subset on x if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] # Apply subset on w if (!is.null(w) && !is.null(rows)) w <- w[rows] apply(x, MARGIN=2L, FUN=weightedMad, w=w, na.rm=na.rm, ...) } # colWeightedMads() ############################################################################ # HISTORY: # 2015-05-31 [DJ] # o Supported subsetted computation. # 2014-11-10 # o Turned weightedMad() into a plain function. # 2013-11-23 # o MEMORY: Now weightedMad() cleans out allocated objects sooner. # 2013-09-26 # o Now utilizing anyMissing(). # 2012-03-22 # o Added an Rdoc example, which also serves as a redundancy test. # o SPEEDUP: Now weightedMad() lets weightedMedian() know that there are # now missing values remaining. # o Now weightedMad() is smarter about returning early, e.g. if missing # values are not removed, there are none or only one value left. # o Added validation of argument 'w' for weightedMad(). # o Added Rdoc comments to weightedMad(). # o Made weightedMad() into a default method. # 2009-05-13 # o Added weightedMad(). # o Created. ############################################################################ matrixStats/R/rowCounts.R0000644000175100001440000002246612736212406015157 0ustar hornikusers###########################################################################/** # @RdocFunction rowCounts # @alias colCounts # @alias count # @alias allValue # @alias anyValue # @alias rowAnys # @alias colAnys # @alias rowAlls # @alias colAlls # # @title "Counts the number of TRUE values in each row (column) of a matrix" # # \description{ # @get "title". # } # # \usage{ # @usage count # @usage rowCounts # @usage colCounts # @usage rowAlls # @usage colAlls # @usage allValue # @usage rowAnys # @usage colAnys # @usage anyValue # } # # \arguments{ # \item{x}{An NxK @matrix or an N*K @vector.} # \item{idxs, rows, cols}{A @vector indicating subset of elements (or rows and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{value}{A value to search for.} # \item{na.rm}{If @TRUE, @NAs are excluded first, otherwise not.} # \item{dim.}{An @integer @vector of length two specifying the # dimension of \code{x}, also when not a @matrix.} # \item{...}{Not used.} # } # # \value{ # \code{rowCounts()} (\code{colCounts()}) returns an @integer @vector # of length N (K). # The other methods returns a @logical @vector of length N (K). # } # # \details{ # @include "../incl/rowNNN_by_vector.Rd" # } # # @examples "../incl/rowCounts.Rex" # # @author "HB" # # @keyword array # @keyword logic # @keyword iteration # @keyword univar #*/########################################################################### rowCounts <- function(x, rows=NULL, cols=NULL, value=TRUE, na.rm=FALSE, dim.=dim(x), ...) { # Argument 'x': if (is.matrix(x)) { } else if (is.vector(x)) { } else { stop("Argument 'x' must be a matrix or a vector: ", mode(x)[1L]) } # Argument 'dim.': dim. <- as.integer(dim.) # Argument 'value': if (length(value) != 1L) { stop("Argument 'value' has to be a single value: ", length(value)) } # Coerce 'value' to matrix storage.mode(value) <- storage.mode(x) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Count # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (is.numeric(x) || is.logical(x)) { na.rm <- as.logical(na.rm) hasNAs <- TRUE counts <- .Call(C_rowCounts, x, dim., rows, cols, value, 2L, na.rm, hasNAs) } else { if (is.vector(x)) dim(x) <- dim. # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] dim. <- dim(x) if (is.na(value)) { counts <- apply(x, MARGIN=1L, FUN=function(x) sum(is.na(x))) } else { counts <- apply(x, MARGIN=1L, FUN=function(x) sum(x == value, na.rm=na.rm)) } } as.integer(counts) } # rowCounts() colCounts <- function(x, rows=NULL, cols=NULL, value=TRUE, na.rm=FALSE, dim.=dim(x), ...) { # Argument 'x': if (is.matrix(x)) { } else if (is.vector(x)) { } else { stop("Argument 'x' must be a matrix or a vector: ", mode(x)[1L]) } # Argument 'dim.': dim. <- as.integer(dim.) # Argument 'value': if (length(value) != 1L) { stop("Argument 'value' has to be a single value: ", length(value)) } # Coerce 'value' to matrix storage.mode(value) <- storage.mode(x) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Count # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (is.numeric(x) || is.logical(x)) { na.rm <- as.logical(na.rm) hasNAs <- TRUE counts <- .Call(C_colCounts, x, dim., rows, cols, value, 2L, na.rm, hasNAs) } else { if (is.vector(x)) dim(x) <- dim. # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] dim. <- dim(x) if (is.na(value)) { counts <- apply(x, MARGIN=2L, FUN=function(x) sum(is.na(x))) } else { counts <- apply(x, MARGIN=2L, FUN=function(x) sum(x == value, na.rm=na.rm)) } } as.integer(counts) } # colCounts() count <- function(x, idxs=NULL, value=TRUE, na.rm=FALSE, ...) { # Argument 'x': if (!is.vector(x)) { stop("Argument 'x' must be a vector: ", mode(x)[1L]) } # Argument 'value': if (length(value) != 1L) { stop("Argument 'value' has to be a single value: ", length(value)) } # Coerce 'value' to matrix storage.mode(value) <- storage.mode(x) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Count # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (is.numeric(x) || is.logical(x)) { na.rm <- as.logical(na.rm) hasNAs <- TRUE counts <- .Call(C_count, x, idxs, value, 2L, na.rm, hasNAs) } else { # Apply subset if (!is.null(idxs)) x <- x[idxs] if (is.na(value)) { counts <- sum(is.na(x)) } else { counts <- sum(x == value, na.rm=na.rm) } } as.integer(counts) } # count() rowAlls <- function(x, rows=NULL, cols=NULL, value=TRUE, na.rm=FALSE, dim.=dim(x), ...) { if (is.numeric(x) || is.logical(x)) { na.rm <- as.logical(na.rm) hasNAs <- TRUE counts <- .Call(C_rowCounts, x, dim., rows, cols, value, 0L, na.rm, hasNAs) as.logical(counts) } else { if (is.vector(x)) dim(x) <- dim. # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] dim. <- dim(x) if (is.na(value)) { rowAlls(is.na(x), na.rm=na.rm, dim.=dim., ...) } else { rowAlls(x == value, na.rm=na.rm, dim.=dim., ...) } } } colAlls <- function(x, rows=NULL, cols=NULL, value=TRUE, na.rm=FALSE, dim.=dim(x), ...) { if (is.numeric(x) || is.logical(x)) { na.rm <- as.logical(na.rm) hasNAs <- TRUE counts <- .Call(C_colCounts, x, dim., rows, cols, value, 0L, na.rm, hasNAs) as.logical(counts) } else { if (is.vector(x)) dim(x) <- dim. # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] dim. <- dim(x) if (is.na(value)) { colAlls(is.na(x), na.rm=na.rm, dim.=dim., ...) } else { colAlls(x == value, na.rm=na.rm, dim.=dim., ...) } } } allValue <- function(x, idxs=NULL, value=TRUE, na.rm=FALSE, ...) { if (is.numeric(x) || is.logical(x)) { na.rm <- as.logical(na.rm) hasNAs <- TRUE counts <- .Call(C_count, x, idxs, value, 0L, na.rm, hasNAs) as.logical(counts) } else { # Apply subset if (!is.null(idxs)) x <- x[idxs] if (is.na(value)) { allValue(is.na(x), na.rm=na.rm, ...) } else { allValue(x == value, na.rm=na.rm, ...) } } } rowAnys <- function(x, rows=NULL, cols=NULL, value=TRUE, na.rm=FALSE, dim.=dim(x), ...) { if (is.numeric(x) || is.logical(x)) { na.rm <- as.logical(na.rm) hasNAs <- TRUE counts <- .Call(C_rowCounts, x, dim., rows, cols, value, 1L, na.rm, hasNAs) as.logical(counts) } else { if (is.vector(x)) dim(x) <- dim. # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] dim. <- dim(x) if (is.na(value)) { rowAnys(is.na(x), na.rm=na.rm, dim.=dim., ...) } else { rowAnys(x == value, na.rm=na.rm, dim.=dim., ...) } } } colAnys <- function(x, rows=NULL, cols=NULL, value=TRUE, na.rm=FALSE, dim.=dim(x), ...) { if (is.numeric(x) || is.logical(x)) { na.rm <- as.logical(na.rm) hasNAs <- TRUE counts <- .Call(C_colCounts, x, dim., rows, cols, value, 1L, na.rm, hasNAs) as.logical(counts) } else { if (is.vector(x)) dim(x) <- dim. # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] dim. <- dim(x) if (is.na(value)) { colAnys(is.na(x), na.rm=na.rm, dim.=dim., ...) } else { colAnys(x == value, na.rm=na.rm, dim.=dim., ...) } } } anyValue <- function(x, idxs=NULL, value=TRUE, na.rm=FALSE, ...) { if (is.numeric(x) || is.logical(x)) { na.rm <- as.logical(na.rm) hasNAs <- TRUE counts <- .Call(C_count, x, idxs, value, 1L, na.rm, hasNAs) as.logical(counts) } else { # Apply subset if (!is.null(idxs)) x <- x[idxs] if (is.na(value)) { anyValue(is.na(x), na.rm=na.rm, ...) } else { anyValue(x == value, na.rm=na.rm, ...) } } } ############################################################################ # HISTORY: # 2015-05-26 [DJ] # o Supported subsetted computation. # 2014-11-14 [HB] # o SPEEDUP: Now colCounts() is implemented in native code. # o CLEANUP: Now (col|row)Count(x) when x is logical utilizes the # same code as as.integer(x). # o As a part of transitioning to plain functions, rowCounts() for matrix # was turned into a default method that understands matrix inputs. # It also understand vector input, if argument 'dim' is given. # 2014-06-02 [HB] # o Made rowCounts() an S3 method (was S4). # o Added argument 'value' to col- and rowCounts(). # 2008-03-25 [HB] # o Created. ############################################################################ matrixStats/R/psortKM.R0000644000175100001440000000056012736212337014545 0ustar hornikusers.psortKM <- function(x, k=length(x), m=1L, ...) { .Call(C_psortKM, as.numeric(x), k=as.integer(k), m=as.integer(m)); } # .psortKM() ############################################################################ # HISTORY: # 2012-09-10 # o Added internal .psortKM() method. # o Created. ############################################################################ matrixStats/R/rowDiffs.R0000644000175100001440000000320512736212447014732 0ustar hornikusers###########################################################################/** # @RdocFunction rowDiffs # @alias colDiffs # # @title "Calculates difference for each row (column) in a matrix" # # \description{ # @get "title". # } # # \usage{ # @usage rowDiffs # @usage colDiffs # } # # \arguments{ # \item{x}{A @numeric NxK @matrix.} # \item{rows, cols}{A @vector indicating subset of rows (and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{lag}{An @integer specifying the lag.} # \item{differences}{An @integer specifying the order of difference.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric Nx(K-1) or (N-1)xK @matrix. # } # # @examples "../incl/rowDiffs.Rex" # # @author "HB" # # \seealso{ # See also @see "diff2". # } # # @keyword array # @keyword iteration # @keyword robust # @keyword univar #*/########################################################################### rowDiffs <- function(x, rows=NULL, cols=NULL, lag=1L, differences=1L, ...) { .Call(C_rowDiffs, x, dim(x), rows, cols, as.integer(lag), as.integer(differences), TRUE) } colDiffs <- function(x, rows=NULL, cols=NULL, lag=1L, differences=1L, ...) { .Call(C_rowDiffs, x, dim(x), rows, cols, as.integer(lag), as.integer(differences), FALSE) } ############################################################################ # HISTORY: # 2015-05-30 [DJ] # o Supported subsetted computation. # 2014-11-15 [HB] # o SPEEDUP: Now colDiffs(x) no longer uses rowDiffs(t(x)). # 2008-03-26 [HB] # o Created. ############################################################################ matrixStats/R/logSumExp.R0000644000175100001440000000670012736212313015065 0ustar hornikusers###########################################################################/** # @RdocFunction logSumExp # # @title "Accurately computes the logarithm of the sum of exponentials" # # \description{ # @get "title", that is, \eqn{log(sum(exp(lx)))}. # If \eqn{lx = log(x)}, then this is equivalently to calculating # \eqn{log(sum(x))}. # # This function, which avoid numerical underflow, is often used when # computing the logarithm of the sum of small numbers (\eqn{|x| << 1}) # such as probabilities. # } # # @synopsis # # \arguments{ # \item{lx}{A @numeric @vector. # Typically \code{lx} are \eqn{log(x)} values.} # \item{idxs}{A @vector indicating subset of elements # to operate over. If @NULL, no subsetting is done.} # \item{na.rm}{If @TRUE, any missing values are ignored, otherwise not.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric scalar. # } # # \details{ # This is function is more accurate than \code{log(sum(exp(lx)))} # when the values of \eqn{x = exp(lx)} are \eqn{|x| << 1}. # The implementation of this function is based on the observation that # \deqn{ # log(a + b) # = [ la = log(a), lb = log(b) ] # = log( exp(la) + exp(lb) ) # = la + log ( 1 + exp(lb - la) ) # } # Assuming \eqn{la > lb}, then \eqn{|lb - la| < |lb|}, and it is # less likely that the computation of \eqn{1 + exp(lb - la)} will # not underflow/overflow numerically. Because of this, the overall # result from this function should be more accurate. # Analogously to this, the implementation of this function finds the # maximum value of \code{lx} and subtracts it from the remaining values # in \code{lx}. # } # # \section{Benchmarking}{ # This method is optimized for correctness, that avoiding underflowing. # It is implemented in native code that is optimized for speed and memory. # } # # @examples "../incl/logSumExp.Rex" # # @author "HB" # # \seealso{ # To compute this function on rows or columns of a matrix, # see @see "rowLogSumExps". # # For adding \emph{two} double values in native code, R provides # the C function \code{logspace_add()} [1]. # For properties of the log-sum-exponential function, see [2]. # } # # \references{ # [1] R Core Team, \emph{Writing R Extensions}, v3.0.0, April 2013. \cr # [2] Laurent El Ghaoui, # \emph{Hyper-Textbook: Optimization Models and Applications}, # University of California at Berkeley, August 2012. # (Chapter 'Log-Sum-Exp (LSE) Function and Properties', # \url{http://inst.eecs.berkeley.edu/~ee127a/book/login/def_lse_fcn.html}) # \cr # [3] R-help thread \emph{logsumexp function in R}, 2011-02-17. # \url{https://stat.ethz.ch/pipermail/r-help/2011-February/269205.html}\cr # } # #*/########################################################################### logSumExp <- function(lx, idxs=NULL, na.rm=FALSE, ...) { hasNA <- TRUE; .Call(C_logSumExp, as.numeric(lx), idxs, as.logical(na.rm), as.logical(hasNA)); } # logSumExp() ############################################################################## # HISTORY: # 2015-05-28 [DJ] # o Supported subsetted computation. # 2013-04-30 [HB] # o Added native implementation. # o Renamed to logSumExp(), because that seems to be the naming convention # elsewhere, e.g. Python. # 2013-04-29 [HB] # o Added sumInLogspace(). # o Created. ############################################################################## matrixStats/R/binMeans.R0000644000175100001440000001306312736212261014700 0ustar hornikusers############################################################################/** # @RdocFunction binMeans # # @title "Fast mean calculations in non-overlapping bins" # # @synopsis # # \description{ # Computes the sample means in non-overlapping bins # } # # \arguments{ # \item{y}{A @numeric @vector of K values to calculate means on.} # \item{x}{A @numeric @vector of K positions for to be binned.} # \item{idxs}{A @vector indicating subset of elements # to operate over. If @NULL, no subsetting is done.} # \item{bx}{A @numeric @vector of B+1 ordered positions specifying # the B > 0 bins \code{[bx[1],bx[2])}, \code{[bx[2],bx[3])}, ..., # \code{[bx[B],bx[B+1])}.} # \item{na.rm}{If @TRUE, missing values in \code{y} are dropped # before calculating the mean, otherwise not.} # \item{count}{If @TRUE, the number of data points in each bins is # returned as attribute \code{count}, which is an @integer @vector # of length B.} # \item{right}{If @TRUE, the bins are right-closed (left open), # otherwise left-closed (right open).} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric @vector of length B. # } # # \details{ # \code{binMeans(x, bx, right=TRUE)} gives equivalent results as # \code{rev(binMeans(-x, bx=sort(-bx), right=FALSE))}, but is faster. # } # # \section{Missing and non-finite values}{ # Data points where either of \code{y} and \code{x} is missing are # dropped (and therefore are also not counted). # Non-finite values in \code{y} are not allowed and gives an error. # Missing values in \code{bx} are not allowed and gives an error. # } # # \section{Empty bins}{ # Empty bins will get value @NaN. # } # # @examples "../incl/binMeans.Rex" # # \seealso{ # @see "binCounts". # @see "stats::aggregate" and @see "base::mean". # } # # \references{ # [1] R-devel thread \emph{Fastest non-overlapping binning mean function # out there?} on Oct 3, 2012\cr # } # # \author{ # Henrik Bengtsson with initial code contributions by Martin Morgan [1]. # } # # @keyword "univar" #*/############################################################################ binMeans <- function(y, x, idxs=NULL, bx, na.rm=TRUE, count=TRUE, right=FALSE, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'y': if (!is.numeric(y)) { stop("Argument 'y' is not numeric: ", mode(y)); } if (any(is.infinite(y))) { stop("Argument 'y' must not contain Inf values."); } n <- length(y); # Argument 'x': if (!is.numeric(x)) { stop("Argument 'x' is not numeric: ", mode(x)); } if (length(x) != n) { stop("Argument 'y' and 'x' are of different lengths: ", length(y), " != ", length(x)); } # Argument 'bx': if (!is.numeric(bx)) { stop("Argument 'bx' is not numeric: ", mode(bx)); } if (any(is.infinite(bx))) { stop("Argument 'bx' must not contain Inf values."); } if (is.unsorted(bx)) { stop("Argument 'bx' is not ordered."); } # Argument 'na.rm': if (!is.logical(na.rm)) { stop("Argument 'na.rm' is not logical: ", mode(na.rm)); } # Argument 'count': if (!is.logical(count)) { stop("Argument 'count' is not logical: ", mode(count)); } # Apply subset if (!is.null(idxs)) { x <- x[idxs] y <- y[idxs] } # Argument 'right': right <- as.logical(right); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Preprocessing of (x,y) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Drop missing values in 'x' keep <- which(!is.na(x)); if (length(keep) < n) { x <- x[keep]; y <- y[keep]; n <- length(y); } keep <- NULL; # Not needed anymore # Drop missing values in 'y'? if (na.rm) { keep <- which(!is.na(y)); if (length(keep) < n) { x <- x[keep]; y <- y[keep]; } keep <- NULL; # Not needed anymore } # Order (x,y) by increasing x. # If 'x' is already sorted, the overhead of (re)sorting is # relatively small. x <- sort.int(x, method="quick", index.return=TRUE); y <- y[x$ix]; x <- x$x; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Bin # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - y <- as.numeric(y); x <- as.numeric(x); bx <- as.numeric(bx); count <- as.logical(count); .Call(C_binMeans, y, x, bx, count, right); } # binMeans() ############################################################################ # HISTORY: # 2015-05-28 [DJ] # o Supported subsetted computation. # 2014-12-29 [HB] # o SPEEDUP: Now binCounts() and binMeans() uses is.unsorted() instead # of o <- order(); any(diff(o) != 1L). # 2014-12-17 [HB] # o CLEANUP: Made binCounts() and binMeans() plain R functions. # 2013-11-23 [HB] # o MEMORY: binMeans() cleans out more temporary variables as soon as # possible such that the garbage collector can remove them sooner. # 2013-05-10 [HB] # o SPEEDUP: Now binMeans() and binCounts() use Hoare's Quicksort # method for sorting 'x'. # 2012-10-04 [HB in Anahola] # o Added argument 'na.rm' to binMeans(). # o Updated Rdocs. # 2012-10-03 [HB] # o Added binMeans() based on native code adopted from code by # Martin Morgan, Fred Hutchinson Cancer Research Center, Seattle. # o Created. ############################################################################ matrixStats/R/999.package.R0000644000175100001440000000126312726216471015075 0ustar hornikusers#########################################################################/** # @RdocPackage matrixStats # # \description{ # @eval "packageDescription('matrixStats')$Description" # } # # \section{Installation}{ # To install this package, please do: # \preformatted{ # install.packages("matrixStats") # } # } # # \section{Vignettes}{ # For an overview of the package, see the '\href{../doc/index.html}{vignettes}'; # \enumerate{ # \item Summary of functions. # } # } # # \section{How to cite this package}{ # @eval "x <- citation('matrixStats'); format(x, 'textVersion')" # } # # @author "*" #*/######################################################################### matrixStats/R/weightedVar.R0000644000175100001440000001456412726216471015432 0ustar hornikusers############################################################################/** # @RdocFunction weightedVar # @alias weightedSd # @alias colWeightedVars # @alias rowWeightedVars # @alias colWeightedSds # @alias rowWeightedSds # # @title "Weighted variance and weighted standard deviation" # # \usage{ # @usage weightedVar # @usage colWeightedVars # @usage rowWeightedVars # # @usage weightedSd # @usage colWeightedSds # @usage rowWeightedSds # } # # # \description{ # Computes a weighted variance / standard deviation of a numeric # vector or across rows or columns of a matrix. # } # # \arguments{ # \item{x}{a @numeric @vector containing the values whose # weighted variance is to be computed.} # \item{w}{a vector of weights the same length as \code{x} giving the weights # to use for each element of \code{x}. Negative weights are treated # as zero weights. Default value is equal weight to all values.} # \item{idxs, rows, cols}{A @vector indicating subset of elements (or rows and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{na.rm}{a logical value indicating whether @NA values in # \code{x} should be stripped before the computation proceeds, # or not. If @NA, no check at all for @NAs is done. # Default value is @NA (for efficiency).} # \item{center}{Optional @numeric scalar specifying the center # location of the data. If @NULL, it is estimated from data.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric scalar. # } # # \section{Missing values}{ # Missing values are dropped at the very beginning, if argument # \code{na.rm} is @TRUE, otherwise not. # } # # \section{Weighted variance}{ # The weights used by the weighted variance (and standard deviation) # estimator should be consider so called \emph{frequency weights} such # that \code{weightedVar(c(2,4,5), w=c(2,1,3)) == var(c(2, 2, 4, 5, 5, 5))}. # Note that this means that the estimate is \emph{not} invariant # to a scale factor on the weights, e.g. passing normalized weights # will not give the same estimate as non-normalized weights. # } # # \seealso{ # For the non-weighted variance, see @see "stats::var". # } # # @author "HB" # # @keyword "univar" # @keyword "robust" #*/############################################################################ weightedVar <- function(x, w=NULL, idxs=NULL, na.rm=FALSE, center=NULL, ...) { # Argument 'x': n <- length(x); # Argument 'w': if (is.null(w)) { w <- rep(1, times=n) } else if (length(w) != n) { stop("The number of elements in arguments 'w' and 'x' does not match: ", length(w), " != ", n); } else if (!is.null(idxs)) { # Apply subset on 'w' w <- w[idxs] } # Apply subset on 'x' if (!is.null(idxs)) { x <- x[idxs] n <- length(x) } # Argument 'na.rm': # Argument 'method': method <- list(...)$method ## Backward compatible but incorrect estimate? ## See https://github.com/HenrikBengtsson/matrixStats/issues/72 use_0.14.2 <- (identical(method, "0.14.2")) naValue <- NA; storage.mode(naValue) <- storage.mode(x); # Remove values with zero (and negative) weight. This will: # 1) take care of the case when all weights are zero, # 2) it will most likely speed up the sorting. tmp <- (w > 0); if (!all(tmp)) { x <- .subset(x, tmp); w <- .subset(w, tmp); n <- length(x); } tmp <- NULL; # Not needed anymore # Drop missing values? if (na.rm) { keep <- which(!is.na(x) & !is.na(w)); x <- .subset(x, keep); w <- .subset(w, keep); n <- length(x); keep <- NULL; # Not needed anymore } else if (anyMissing(x)) { return(naValue); } # Are any weights Inf? Then treat them with equal weight and all others # with weight zero. tmp <- is.infinite(w); if (any(tmp)) { keep <- tmp; x <- .subset(x, keep); n <- length(x); w <- rep(1, times=n); keep <- NULL; # Not needed anymore } tmp <- NULL; # Not needed anymore # Are there any values left to calculate the weighted variance of? # This is consistent with how stats::var() works. if (n <= 1L) return(naValue) # Standardize weights to sum to one wsum <- sum(w) w <- w / wsum # Estimate the mean? if (is.null(center)) { center <- sum(w*x); } # Estimate the variance x <- x - center; # Residuals x <- x^2; # Squared residuals ## Correction factor lambda <- wsum / (wsum - 1) if (use_0.14.2) lambda <- n / (n-1L) sigma2 <- lambda * sum(w*x) x <- w <- NULL; # Not needed anymore sigma2; } # weightedVar() weightedSd <- function(...) { sqrt(weightedVar(...)) } # weightedSd() rowWeightedVars <- function(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) { # Apply subset on 'x' if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] # Apply subset on 'w' if (!is.null(w) && !is.null(cols)) w <- w[cols] apply(x, MARGIN=1L, FUN=weightedVar, w=w, na.rm=na.rm, ...) } # rowWeightedVars() colWeightedVars <- function(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) { # Apply subset on 'x' if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] # Apply subset on 'w' if (!is.null(w) && !is.null(rows)) w <- w[rows] apply(x, MARGIN=2L, FUN=weightedVar, w=w, na.rm=na.rm, ...) } # colWeightedVars() rowWeightedSds <- function(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) { sqrt(rowWeightedVars(x=x, w=w, rows=rows, cols=cols, na.rm=na.rm, ...)) } # rowWeightedSds() colWeightedSds <- function(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) { sqrt(colWeightedVars(x=x, w=w, rows=rows, cols=cols, na.rm=na.rm, ...)) } # colWeightedSds() ############################################################################ # HISTORY: # 2015-06-03 [DJ] # o Supported subsetted computation. # 2014-11-10 # o Turned weightedSd() and weightedVar() into plain function. # 2014-03-26 # o Created from weightedMad.R. ############################################################################ matrixStats/R/rowSds.R0000644000175100001440000000417112726216471014433 0ustar hornikusers###########################################################################/** # @RdocFunction rowSds # @alias rowSds # @alias colSds # @alias rowMads # @alias colMads # \alias{rowSds,matrix-method} # \alias{colSds,matrix-method} # # @title "Standard deviation estimates for each row (column) in a matrix" # # \description{ # @get "title". # } # # \usage{ # @usage rowSds # @usage colSds # @usage rowMads # @usage colMads # } # # \arguments{ # \item{x}{A @numeric NxK @matrix.} # \item{rows, cols}{A @vector indicating subset of rows (and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{center}{A optional @numeric @vector of length N (K) with centers. # By default, they are calculated using @see "rowMedians".} # \item{constant}{A scale factor. See @see "stats::mad" for details.} # \item{na.rm}{If @TRUE, missing values are removed first, otherwise not.} # \item{dim.}{An @integer @vector of length two specifying the # dimension of \code{x}, also when not a @matrix.} # \item{...}{Additional arguments passed to @see "rowVars" and # @see "rowMedians", respectively.} # \item{centers}{(deprectated) use \code{center} instead.} # } # # \value{ # Returns a @numeric @vector of length N (K). # } # # @author "HB" # # \seealso{ # @see "stats::sd", @see "stats::mad" and \code{\link[stats:cor]{var}}. # @see "rowIQRs". # } # # @keyword array # @keyword iteration # @keyword robust # @keyword univar #*/########################################################################### rowSds <- function(x, rows=NULL, cols=NULL, ...) { x <- rowVars(x, rows=rows, cols=cols, ...); sqrt(x); } colSds <- function(x, rows=NULL, cols=NULL, ...) { x <- colVars(x, rows=rows, cols=cols, ...); sqrt(x); } ############################################################################ # HISTORY: # 2012-03-19 [HC] # o Changed description of centers argument to rowMads and colMads # 2008-03-26 [HB] # o Created from genefilter::rowVars() by Wolfgang Huber. ############################################################################ matrixStats/R/zzz.R0000644000175100001440000000062312726216471014005 0ustar hornikusers.onUnload <- function (libpath) { library.dynam.unload("matrixStats", libpath) } .onAttach <- function(libname, pkgname) { ## covr: skip=5 pd <- utils::packageDescription(pkgname) msg <- sprintf("%s v%s", pkgname, pd$Version) if (!is.null(pd$Date)) msg <- sprintf("%s (%s)", msg, pd$Date) msg <- sprintf("%s successfully loaded. See ?%s for help.", msg, pkgname) pkgStartupMessage(msg) } matrixStats/R/anyMissing.R0000644000175100001440000000546312776230100015266 0ustar hornikusers###########################################################################/** # @RdocFunction anyMissing # \alias{colAnyMissings} # \alias{rowAnyMissings} # # @title "Checks if there are any missing values in an object or not" # # \description{ # @get "title". # } # # \usage{ # @usage anyMissing # @usage colAnyMissings # @usage rowAnyMissings # } # # \arguments{ # \item{x}{A @vector, a @list, a @matrix, a @data.frame, or @NULL.} # \item{idxs, rows, cols}{A @vector indicating subset of elements (or rows and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{...}{Not used.} # } # # \value{ # Returns @TRUE if a missing value was detected, otherwise @FALSE. # } # # \details{ # The implementation of this method is optimized for both speed and memory. # The method will return @TRUE as soon as a missing value is detected. # } # # \examples{ # x <- rnorm(n=1000) # x[seq(300,length(x),by=100)] <- NA # stopifnot(anyMissing(x) == any(is.na(x))) # } # # @author "HB" # # \seealso{ # Starting with R v3.1.0, there is \code{anyNA()} in the \pkg{base}, # which provides the same functionality as \code{anyMissing()}. # } # # @keyword iteration # @keyword logic #*/########################################################################### anyMissing <- function(x, idxs=NULL, ...) { ## All list or a data.frame? if (is.list(x)) { for (kk in seq_along(x)) { if (.Call(C_anyMissing, x[[kk]], idxs)) return(TRUE) } return(FALSE) } else { ## All other data types .Call(C_anyMissing, x, idxs) } } colAnyMissings <- function(x, rows=NULL, cols=NULL, ...) { colAnys(x, rows, cols, value=NA, ...) } rowAnyMissings <- function(x, rows=NULL, cols=NULL, ...) { rowAnys(x, rows, cols, value=NA, ...) } ############################################################################ # HISTORY: # 2015-05-26 [DJ] # o Supported subsetted computation. # 2015-02-10 # o CLEANUP: anyMissing() is no longer an S4 generic, cf. base::anyNA(). # 2015-01-20 # o CLEANUP: In the effort of migrating anyMissing() into a plain R # function, specific anyMissing() implementations for data.frame:s and # and list:s were dropped and is now handled by anyMissing() for "ANY". # 2014-12-08 # o Added (col|row)AnyMissings(). # 2013-09-26 # o Added help reference to base::anyNA(). # 2013-01-13 # o Added anyMissing() for raw, which always returns FALSE. # 2008-03-25 # o Added anyMissing() for matrices, data.frames, lists and NULL. # o Added anyMissing() for numeric, logical, complex and character. # o Made anyMissing() into an S4 method. # 2007-08-14 # o Created. See also R-devel thread "hasNA()/anyNA()?" on 2007-08-13. ############################################################################ matrixStats/R/rowIQRs.R0000644000175100001440000000576112726216471014526 0ustar hornikusers###########################################################################/** # @RdocFunction rowIQRs # @alias colIQRs # @alias iqr # # @title "Estimates of the interquartile range for each row (column) in a matrix" # # \description{ # @get "title". # } # # \usage{ # @usage rowIQRs # @usage colIQRs # @usage iqr # } # # \arguments{ # \item{x}{A @numeric NxK @matrix.} # \item{idxs, rows, cols}{A @vector indicating subset of elements (or rows and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{na.rm}{If @TRUE, missing values are dropped first, otherwise not.} # \item{...}{Additional arguments passed to @see "rowQuantiles" # (\code{colQuantiles()}).} # } # # \value{ # Returns a @numeric @vector of length N (K). # } # # \section{Missing values}{ # Contrary to @see "stats::IQR", which gives an error if there are missing # values and \code{na.rm=FALSE}, \code{iqr()} and its corresponding row and # column-specific functions return @NA_real_. # } # # @examples "../incl/rowIQRs.Rex" # # @author "HB" # # \seealso{ # See @see "stats::IQR". # See @see "rowSds". # } # # @keyword array # @keyword iteration # @keyword robust # @keyword univar #*/########################################################################### rowIQRs <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, ...) { Q <- rowQuantiles(x, rows=rows, cols=cols, probs=c(0.25, 0.75), na.rm=na.rm, drop=FALSE, ...) ans <- Q[,2L,drop=TRUE] - Q[,1L,drop=TRUE] # Remove attributes attributes(ans) <- NULL ans } colIQRs <- function(x, rows=NULL, cols=NULL, na.rm=FALSE, ...) { Q <- colQuantiles(x, rows=rows, cols=cols, probs=c(0.25, 0.75), na.rm=na.rm, drop=FALSE, ...) ans <- Q[,2L,drop=TRUE] - Q[,1L,drop=TRUE] # Remove attributes attributes(ans) <- NULL ans } iqr <- function(x, idxs=NULL, na.rm=FALSE, ...) { # Apply subset if (!is.null(idxs)) x <- x[idxs] if(na.rm) { x <- x[!is.na(x)] } else if (anyMissing(x)) { return(NA_real_) } # At this point, there should be no missing values # Nothing to do? n <- length(x) if (n == 0L) { return(NA_real_) } else if (n == 1L) { return(0) } q <- quantile(x, probs=c(0.25, 0.75), names=FALSE, na.rm=FALSE, ...) q[2L] - q[1L] } ############################################################################ # HISTORY: # 2015-05-30 [DJ] # o Supported subsetted computation. # 2015-01-16 # o Now iqr(..., na.rm=FALSE) returns NA_real_ if there are missing values. # 2015-01-11 # o Now iqr() no longer returns a named value. # 2014-12-19 # o Added iqr(). # 2011-11-25 # o Added help and example to rowIQRs() and colIQRs(). # o BUG FIX: rowIQRs() and colIQRs() would return the 25% and the 75% # quantiles, not the difference between them. Thanks Pierre Neuvial # at CNRS, Evry, France for the report. # 2008-03-26 [HB] # o Created. ############################################################################ matrixStats/R/rowQuantiles.R0000644000175100001440000001633412726216471015653 0ustar hornikusers###########################################################################/** # @RdocFunction rowQuantiles # @alias colQuantiles # # @title "Estimates quantiles for each row (column) in a matrix" # # \description{ # @get "title". # } # # \usage{ # @usage rowQuantiles # @usage colQuantiles # } # # \arguments{ # \item{x}{A @numeric NxK @matrix with N >= 0.} # \item{rows, cols}{A @vector indicating subset of rows (and/or columns) # to operate over. If @NULL, no subsetting is done.} # \item{probs}{A @numeric @vector of J probabilities in [0,1].} # \item{na.rm}{If @TRUE, @NAs are excluded first, otherwise not.} # \item{type}{An @integer specify the type of estimator. # See @see "stats::quantile" for more details.} # \item{...}{Additional arguments passed to @see "stats::quantile".} # \item{drop}{If TRUE, singleton dimensions in the result are dropped, # otherwise not.} # } # # \value{ # Returns a @numeric NxJ (KxJ) @matrix, where # N (K) is the number of rows (columns) for which the J quantiles are # calculated. # } # # @examples "../incl/rowQuantiles.Rex" # # @author "HB" # # \seealso{ # @see "stats::quantile". # } # # @keyword array # @keyword iteration # @keyword robust # @keyword univar #*/########################################################################### rowQuantiles <- function(x, rows=NULL, cols=NULL, probs=seq(from=0, to=1, by=0.25), na.rm=FALSE, type=7L, ..., drop=TRUE) { # Argument 'probs': if (anyMissing(probs)) { stop("Argument 'probs' contains missing values") } eps <- 100 * .Machine$double.eps if (any((probs < -eps | probs > 1 + eps))) { stop("Argument 'probs' is out of range [0-eps,1+eps]") } # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] # Argument 'x': nrow <- nrow(x) ncol <- ncol(x) if (nrow > 0L && ncol > 0L) { naRows <- rowAnyMissings(x) hasNA <- any(naRows) if (!hasNA) na.rm <- FALSE if (!hasNA && type == 7L) { n <- ncol idxs <- 1 + (n-1) * probs idxs_lo <- floor(idxs) idxs_hi <- ceiling(idxs) partial <- sort(unique(c(idxs_lo, idxs_hi))) xp <- apply(x, MARGIN=1L, FUN=sort, partial=partial) if (is.null(dim(xp))) dim(xp) <- c(1L, length(xp)) q <- apply(xp, MARGIN=2L, FUN=.subset, idxs_lo) if (is.null(dim(q))) dim(q) <- c(1L, length(q)) # Adjust idxs_adj <- which(idxs > idxs_lo) if (length(idxs_adj) > 0L) { qL <- q[idxs_adj,,drop=FALSE] idxs_hi <- idxs_hi[idxs_adj] qH <- apply(xp, MARGIN=2L, FUN=.subset, idxs_hi) w <- (idxs - idxs_lo)[idxs_adj] q[idxs_adj,] <- (1-w)*qL + w*qH # Not needed anymore xp <- qL <- qH <- NULL } # Backward compatibility q <- t(q) } else { # Allocate result naValue <- NA_real_ storage.mode(naValue) <- storage.mode(x) q <- matrix(naValue, nrow=nrow, ncol=length(probs)) # For each row... rows <- seq_len(nrow) # Rows with NAs should return all NAs (so skip those) if (hasNA && !na.rm) rows <- rows[!naRows] for (kk in rows) { xkk <- x[kk,] if (na.rm) xkk <- xkk[!is.na(xkk)] q[kk,] <- quantile(xkk, probs=probs, na.rm=FALSE, type=type, ...) } } # if (type ...) } else { naValue <- NA_real_ storage.mode(naValue) <- storage.mode(x) q <- matrix(naValue, nrow=nrow, ncol=length(probs)) } # Add names digits <- max(2L, getOption("digits")) colnames(q) <- sprintf("%.*g%%", digits, 100*probs) # Drop singleton dimensions? if (drop) { q <- drop(q) } q } # rowQuantiles() colQuantiles <- function(x, rows=NULL, cols=NULL, probs=seq(from=0, to=1, by=0.25), na.rm=FALSE, type=7L, ..., drop=TRUE) { # Argument 'probs': if (anyMissing(probs)) { stop("Argument 'probs' contains missing values") } eps <- 100 * .Machine$double.eps if (any((probs < -eps | probs > 1 + eps))) { stop("Argument 'probs' is out of range [0-eps,1+eps]") } # Apply subset if (!is.null(rows) && !is.null(cols)) x <- x[rows,cols,drop=FALSE] else if (!is.null(rows)) x <- x[rows,,drop=FALSE] else if (!is.null(cols)) x <- x[,cols,drop=FALSE] # Argument 'x': nrow <- nrow(x) ncol <- ncol(x) if (nrow > 0L && ncol > 0L) { naCols <- colAnyMissings(x) hasNA <- any(naCols) if (!hasNA) na.rm <- FALSE if (!hasNA && type == 7L) { n <- nrow idxs <- 1 + (n-1) * probs idxs_lo <- floor(idxs) idxs_hi <- ceiling(idxs) partial <- sort(unique(c(idxs_lo, idxs_hi))) xp <- apply(x, MARGIN=2L, FUN=sort, partial=partial) if (is.null(dim(xp))) dim(xp) <- c(1L, length(xp)) q <- apply(xp, MARGIN=2L, FUN=.subset, idxs_lo) if (is.null(dim(q))) dim(q) <- c(1L, length(q)) # Adjust idxs_adj <- which(idxs > idxs_lo) if (length(idxs_adj) > 0L) { qL <- q[idxs_adj,,drop=FALSE] idxs_hi <- idxs_hi[idxs_adj] qH <- apply(xp, MARGIN=2L, FUN=.subset, idxs_hi) w <- (idxs - idxs_lo)[idxs_adj] q[idxs_adj,] <- (1-w)*qL + w*qH # Not needed anymore xp <- qL <- qH <- NULL } # Backward compatibility q <- t(q) } else { # Allocate result naValue <- NA_real_ storage.mode(naValue) <- storage.mode(x) q <- matrix(naValue, nrow=ncol, ncol=length(probs)) # For each column... cols <- seq_len(ncol) # Columns with NAs should return all NAs (so skip those) if (hasNA && !na.rm) cols <- cols[!naCols] for (kk in cols) { xkk <- x[,kk] if (na.rm) xkk <- xkk[!is.na(xkk)] q[kk,] <- quantile(xkk, probs=probs, na.rm=FALSE, type=type, ...) } } # if (type ...) } else { naValue <- NA_real_ storage.mode(naValue) <- storage.mode(x) q <- matrix(naValue, nrow=ncol, ncol=length(probs)) } # Add names digits <- max(2L, getOption("digits")) colnames(q) <- sprintf("%.*g%%", digits, 100*probs) # Drop singleton dimensions? if (drop) { q <- drop(q) } q } ############################################################################ # HISTORY: # 2015-05-29 [DJ] # o Supported subsetted computation. # 2015-01-26 # o CONSISTENCY: Now rowQuantiles(x, na.rm=TRUE) returns all NAs for rows # with missing values. Analogously for colQuantiles(). Previously, an # error was thrown saying missing values are not allowed. # 2014-11-18 [HB] # o SPEEDUP: Made (col|row)Quantiles(x) faster for 'x' without missing # values (and default type=7L quantiles). # 2014-11-16 [HB] # o SPEEDUP: colQuantiles(x) is no longer using colQuantiles(t(x)). # 2013-07-29 [HB] # o DOCUMENTATION: The dimension of the return value was swapped # in help("rowQuantiles"). Noticed by PL. # 2011-11-29 [HB] # o Added an Rdoc example. # 2010-10-06 [HB] # o Now the result of {row|col}Quantiles() contains column names. # 2008-03-26 [HB] # o Created. ############################################################################ matrixStats/vignettes/0000755000175100001440000000000012776336765014651 5ustar hornikusersmatrixStats/vignettes/matrixStats-methods.md.rsp0000644000175100001440000002061012776335301021742 0ustar hornikusers<%@meta language="R-vignette" content="-------------------------------- DIRECTIVES FOR R: %\VignetteIndexEntry{matrixStats: Summary of functions} %\VignetteAuthor{Henrik Bengtsson} %\VignetteKeyword{matrix} %\VignetteKeyword{vector} %\VignetteKeyword{apply} %\VignetteKeyword{rows} %\VignetteKeyword{columns} %\VignetteKeyword{memory} %\VignetteKeyword{speed} %\VignetteEngine{R.rsp::rsp} %\VignetteTangle{FALSE} --------------------------------------------------------------------"%> <% pkgName <- "matrixStats" library(pkgName, character.only=TRUE) ns <- getNamespace(pkgName) env <- as.environment(sprintf("package:%s", pkgName)) R.utils::use("R.utils") kable <- function(df, ...) { fcns <- as.character(df$Functions) fcns <- strsplit(fcns, split=",") fcns <- sapply(fcns, FUN=function(names) { names <- trim(names) ok <- sapply(names, FUN=exists, envir=ns, mode="function") names[ok] <- sprintf("%s()", names[ok]) names[!ok] <- sprintf("~~%s()~~", names[!ok]) names <- paste(names, collapse=", ") }) df$Functions <- fcns df$Example <- sprintf("`%s`", df$Example) print(knitr::kable(df, ..., format="markdown")) } # Find all functions all <- ls(envir=env) keep <- sapply(all, FUN=function(name) { is.function(get(name, envir=env)) }) all <- all[keep] keep <- !grepl("[.]([^.]*)$", all) all <- all[keep] # Hidden functions skip <- c("rowAvgsPerColSet", "colAvgsPerRowSet") skip <- c(skip, "allocArray", "allocMatrix", "allocVector") all <- setdiff(all, skip) # Column and row functions crfcns <- grep("^(col|row)", all, value=TRUE) # Vector functions vfcns <- setdiff(all, crfcns) %> # <%@meta name="title"%> <% pkg <- R.oo::Package(pkgName) %> <%@meta name="author"%> on <%=format(as.Date(pkg$date), format="%B %d, %Y")%> <% fcns <- crfcns base <- gsub("^(col|row)", "", fcns) groups <- tapply(fcns, base, FUN=list) stopifnot(all(sapply(groups, FUN=length) == 2L)) groups <- matrix(unlist(groups, use.names=FALSE), nrow=2L) %> <%--- ## Functions that apply to column and rows of matrices ``` <% print(fcns) %> ``` ---%> <% fcns <- vfcns %> <%--- ## Functions that apply to vectors ``` <% print(fcns) %> ``` ---%> ## Location and scale estimators <% tbl <- NULL row <- data.frame( "Estimator" = "Weighted sample mean", "Functions" = "weightedMean, colWeightedMeans, rowWeightedMeans", "Example" = "weightedMean(x, w); rowWeightedMeans(x, w)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Median", "Functions" = "median, colMedians, rowMedians", "Example" = "median(x); rowMedians(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Weighted median", "Functions" = "weightedMedian, colWeightedMedians, rowWeightedMedians", "Example" = "weightedMedian(x, w); rowWeightedMedians(x, w)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Sample variance", "Functions" = "var, colVars, rowVars", "Example" = "var(x); rowVars(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Weighted sample variance", "Functions" = "weightedVar, colWeightedVars, rowWeightedVars", "Example" = "weightedVar(x, w), rowWeightedVars(x, w)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Sample variance by n-order differences", "Functions" = "varDiff, colVarDiffs, rowVarDiffs", "Example" = "varDiff(x); rowVarDiffs(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Sample standard deviation", "Functions" = "sd, colSds, rowSds", "Example" = "sd(x); rowSds(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Weighted sample deviation", "Functions" = "weightedSd, colWeightedSds, rowWeightedSds", "Example" = "weightedSd(x, w), rowWeightedSds(x, w)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Sample standard deviation by n-order differences", "Functions" = "sdDiff, colSdDiffs, rowSdDiffs", "Example" = "sdDiff(x); rowSdDiffs(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Median absolute deviation (MAD)", "Functions" = "mad, colMads, rowMads", "Example" = "mad(x); rowMads(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Weighted median absolute deviation (MAD)", "Functions" = "weightedMad, colWeightedMads, rowWeightedMads", "Example" = "weightedMad(x, w), rowWeightedMads(x, w)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Median absolute deviation (MAD) by n-order differences", "Functions" = "madDiff, colMadDiffs, rowMadDiffs", "Example" = "madDiff(x); rowMadDiffs()" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Quantile", "Functions" = "quantile, colQuantiles, rowQuantiles", "Example" = "quantile(x, probs); rowQuantiles(x, probs)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Interquartile range (IQR)", "Functions" = "iqr, colIQRs, rowIQRs", "Example" = "iqr(x); rowIQRs(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Interquartile range (IQR) by n-order differences", "Functions" = "iqrDiff, colIQRDiffs, rowIQRDiffs", "Example" = "iqrDiff(x); rowIQRDiffs(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Range", "Functions" = "range, colRanges, rowRanges", "Example" = "range(x); rowRanges(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Minimum", "Functions" = "min, colMins, rowMins", "Example" = "min(x); rowMins(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Maximum", "Functions" = "max, colMaxs, rowMaxs", "Example" = "max(x); rowMaxs(x)" ) tbl <- rbind(tbl, row) %> <% kable(tbl) %> ## Testing for and counting values <% tbl <- NULL row <- data.frame( "Operator" = "Are there any missing values?", "Functions" = "anyMissing, colAnyMissings, rowAnyMissings", "Example" = "anyMissing(x); rowAnyMissings(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Operator" = "Does TRUE exists?", "Functions" = "any, colAnys, rowAnys", "Example" = "any(x); rowAnys(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Operator" = "Are all values TRUE?", "Functions" = "all, colAlls, rowAlls", "Example" = "all(x); rowAlls(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Operator" = "Does value exists?", "Functions" = "anyValue, colAnys, rowAnys", "Example" = "anyValue(x, value); rowAnys(x, value)" ) tbl <- rbind(tbl, row) row <- data.frame( "Operator" = "Do all elements have a given value?", "Functions" = "allValue, colAlls, rowAlls", "Example" = "allValue(x, value); rowAlls(x, value)" ) tbl <- rbind(tbl, row) row <- data.frame( "Operator" = "Number of occurrences of a value?", "Functions" = "count, colCounts, rowCounts", "Example" = "count(x, value); rowCounts(x, value)" ) tbl <- rbind(tbl, row) %> <% kable(tbl) %> ## Cumulative functions <% tbl <- NULL row <- data.frame( "Operator" = "Cumulative sum", "Functions" = "cumsum, colCumsums, rowCumsums", "Example" = "cumsum(x); rowCumsums(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Operator" = "Cumulative product", "Functions" = "cumprod, colCumprods, rowCumprods", "Example" = "cumprod(x); rowCumprods(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Operator" = "Cumulative minimum", "Functions" = "cummin, colCummins, rowCummins", "Example" = "cummin(x); rowCummins(x)" ) tbl <- rbind(tbl, row) row <- data.frame( "Operator" = "Cumulative maximum", "Functions" = "cummax, colCummaxs, rowCummaxs", "Example" = "cummax(x); rowCummaxs(x)" ) tbl <- rbind(tbl, row) %> <% kable(tbl) %> ## Binning <% tbl <- NULL row <- data.frame( "Estimator" = "Counts in disjoint bins", "Functions" = "binCounts", "Example" = "binCounts(x, bx)" ) tbl <- rbind(tbl, row) row <- data.frame( "Estimator" = "Sample means (and counts) in disjoint bins", "Functions" = "binMeans", "Example" = "binMeans(y, x, bx)" ) tbl <- rbind(tbl, row) %> <% kable(tbl) %> ## Miscellaneous <% tbl <- NULL row <- data.frame( "Operation" = "Lagged differences", "Functions" = c("diff2, colDiffs, rowDiffs"), "Example" = "diff2(x), rowDiffs(x)" ) tbl <- rbind(tbl, row) %> <% kable(tbl) %> ------------------------------------------------------------- <%=pkgName%> v<%=getVersion(pkg)%>. Release: [CRAN](https://cran.r-project.org/package=<%=pkgName%>), Development: [GitHub](<%=getUrl(pkg)%>). matrixStats/MD50000644000175100001440000004630512776367215013153 0ustar hornikusersb5ab115ff5f38e2bd9eedcb29d88d781 *DESCRIPTION c604577b73abd4bfde9d6e7552f3aa37 *NAMESPACE 4ed27b61313c25e9f31acfbc8404c15f *NEWS e11dfd66d9a104fbc5598c52bd2f516d *R/999.package.R 3cc50d956cc6a845d5386a4e8ea06ece *R/allocMatrix.R 7d373633f3dc9bf3f0b6a8b08cb0c27b *R/anyMissing.R 93d3ad6fb517ebf2f4011fd0f21903d9 *R/benchmark.R e3ae3830e20c35dbcafe6a026e52fe4e *R/binCounts.R a37b3b8ea90fa4d81d8439188826f93a *R/binMeans.R b1994e4710317d8f828610c147be6da7 *R/diff2.R 98fd9b9ef90e2315881e4c74e2c8590b *R/indexByRow.R 1e7bfcea0dfe76bb901073a1ed106c44 *R/logSumExp.R 77a76b16f37489c0f838144926178a85 *R/meanOver.R 51602457a9df5aecf0ef4755ec0cf546 *R/pkgStartupMessage.R e74c2d3468f31191441e4d1a368ae3cd *R/product.R 6eb84a86a710d0b627141afcbf04eb9d *R/psortKM.R 3c6795e8778233f69ea053dc11fe69f1 *R/rowAvgsPerColSet.R 82e58d3c03ebaf7db23a1bb48da96ce2 *R/rowCollapse.R 590ecd511648e7cb5dad6874b1c1fd9a *R/rowCounts.R 8a66c407186cabd792514122328681c9 *R/rowCumsums.R 2df27178608aea515e49a1e34ce51bea *R/rowDiffs.R 3d3f0b9e2504a11265c9583bb59741cc *R/rowIQRs.R 2a07f1737200dd66d4f5565ececf9168 *R/rowLogSumExps.R fcbd0b8cb6758c0f1414daf67899734d *R/rowMads.R c502f271bd28ff70b6e57bb0dd5b0545 *R/rowMedians.R a7914fdaa3520715682328a48f259ede *R/rowOrderStats.R f5d27a6569191c9e81cf99386612583f *R/rowProds.R fedc852d102a13caed0735ade1480665 *R/rowQuantiles.R 622523df70f2cc401e9acaa0f5591188 *R/rowRanges.R 5e108af6740fcdeeb86be46c65242cc9 *R/rowRanks.R 57574444be678eb148161e38d755d592 *R/rowSds.R 3596341e361f8688c21d605bf488e83f *R/rowTabulates.R 1b7a100557af783594aa6fccb23ebdfc *R/rowVars.R 1ee11cd765ee67bbd04e773838dee05a *R/rowWeightedMeans.R ed06e3f6dc54af19b1be1c2ee4535387 *R/rowWeightedMedians.R 78c84d787add73bc9c67fdd32d7aca29 *R/signTabulate.R 38ccd72fe8309638abb84ff2c8c032e3 *R/sumOver.R 3b32ce77e3cdf875a2fa81ea3de6a792 *R/validateIndices.R 63be4e9df657da216c4f9d462f790f1e *R/varDiff.R b31060ea1d16ce3db6d33d20fbc45b7b *R/weightedMad.R 6caa4110014fcfcfe46cac91c3698f85 *R/weightedMean.R 704299c273bb9b364596ef2dd37db5e0 *R/weightedMedian.R 203108f1a2e61ac621e36b82125f918b *R/weightedVar.R 0513998918dba294fc114f19fb198dba *R/x_OP_y.R c3bf2b5add1d21bb0c216e8bc99f85b1 *R/zzz.R 3e2f01e3bfa33741f1874a9190bfc73b *build/vignette.rds ca0c103b1be9f97f60c0623aea65f5c2 *inst/benchmarking/R/random-matrices.R 4a38dc3c38475a36786249204cf20588 *inst/benchmarking/R/random-vectors.R 7256378a1eb54d9d41e0d075a11b3612 *inst/benchmarking/allocMatrix.md.rsp e5a5e47c87dad6280a47d91e1cbdf46a *inst/benchmarking/allocVector.md.rsp c4e95805c1acbd37aab9d7ec7de023d7 *inst/benchmarking/anyMissing.md.rsp 0b3b5bb920ca72c04956d7213ddbc03f *inst/benchmarking/anyMissing_subset.md.rsp 330dac464260cce1864cacd0ecf73f26 *inst/benchmarking/binCounts.md.rsp ae0db7dd0c29aaddc41e4b14bb297eca *inst/benchmarking/binCounts_subset.md.rsp e03de8a101d01223da70134f4ee225ee *inst/benchmarking/binMeans.md.rsp c34a22a1ac9fe884b9ddb358bc242cd0 *inst/benchmarking/binMeans_subset.md.rsp 2955e6a91517fd43ad799e52824176ba *inst/benchmarking/colRowAlls.md.rsp 5cb5621a132efbe967842944a4ad3294 *inst/benchmarking/colRowAlls_subset.md.rsp 861057a45857a4626c6179dba6cadd0c *inst/benchmarking/colRowAnyMissings.md.rsp d5be8a669f03a51b541fc9c15026bde0 *inst/benchmarking/colRowAnyMissings_subset.md.rsp 98a3d92a9b8217cb3d4eb73c3ae91eed *inst/benchmarking/colRowAnys.md.rsp 6d5865fa623882e24059e835f05ed34b *inst/benchmarking/colRowAnys_subset.md.rsp 4c5523efdcf9c92b073a5ade831c331a *inst/benchmarking/colRowCounts.md.rsp 0279878860e7be5a4835b40ee062eabc *inst/benchmarking/colRowCounts_subset.md.rsp 82c16b2f37c797888940e4bd02d866ce *inst/benchmarking/colRowCummins.md.rsp a640f354f5bb8a166f1e692484c9a216 *inst/benchmarking/colRowCummins_subset.md.rsp aa66215e97c52a6d18f89775e3b27fca *inst/benchmarking/colRowCumprods.md.rsp bdd65c325caf8c054bf081acaccbeaf5 *inst/benchmarking/colRowCumprods_subset.md.rsp 590182276674b77503d28a6be8d1ccfa *inst/benchmarking/colRowCumsums.md.rsp dbe2f645c8391826c438c66d320b4bab *inst/benchmarking/colRowCumsums_subset.md.rsp 3c876337a72df11a91441bea1761f115 *inst/benchmarking/colRowDiffs.md.rsp e4a853b69cc4d354f529fe418d1e9889 *inst/benchmarking/colRowDiffs_subset.md.rsp a6523b452344ef2b78073d50fe9a534c *inst/benchmarking/colRowLogSumExps.md.rsp c87aa2afa7d90d4e3c141940e18ec479 *inst/benchmarking/colRowLogSumExps_subset.md.rsp f3baebca9ee8ad627605269e996dffb4 *inst/benchmarking/colRowMads.md.rsp fe63ec08f423b23170b772bd78355e22 *inst/benchmarking/colRowMads_subset.md.rsp eccd2ee0c78f2eb286aa4d110a0558a1 *inst/benchmarking/colRowMeans.md.rsp 68e0230c9aa586c216cf1a83094c3acc *inst/benchmarking/colRowMedians.md.rsp 8c7741b6e556b0f470b9c4abcd4db322 *inst/benchmarking/colRowMedians_subset.md.rsp f870694f8d86741fb3194661879db142 *inst/benchmarking/colRowMins.md.rsp 580fc985bb18c3edc76e7a5a1ef737da *inst/benchmarking/colRowMins_subset.md.rsp dd1f69270726961bc78cb2e723495610 *inst/benchmarking/colRowOrderStats.md.rsp 76d6bc53376574e3b375758459000938 *inst/benchmarking/colRowOrderStats_subset.md.rsp f0647a93c27525f62e8b5250001b2a37 *inst/benchmarking/colRowProds.md.rsp 8841d6d653b031d9fb6858f4a94fad29 *inst/benchmarking/colRowProds_subset.md.rsp 45789c523622419efbc47059b6574273 *inst/benchmarking/colRowQuantiles.md.rsp 7485e8b389405fc1c5271cab76fafc79 *inst/benchmarking/colRowQuantiles_subset.md.rsp 90e3f4c1d92a47457ca70bbc52c1c145 *inst/benchmarking/colRowRanges.md.rsp db95390755954a7bff98fc13756af9da *inst/benchmarking/colRowRanges_subset.md.rsp 07a64514251f5f55a62951a871e23d21 *inst/benchmarking/colRowRanks.md.rsp e9ea652a4b77c60765d596abe8209595 *inst/benchmarking/colRowRanks_subset.md.rsp 409e4c37addbf7a2c15cb0b276692ca7 *inst/benchmarking/colRowSums.md.rsp ac5da10e80f1ff0ca8722176b7b41cf9 *inst/benchmarking/colRowTabulates.md.rsp 162e881341626b7d3a3792be07ba8b60 *inst/benchmarking/colRowTabulates_subset.md.rsp 72cacc64cf3e106dadfe865650c0c57f *inst/benchmarking/colRowVars.md.rsp f3bf89d20cfd422b096287cdf518ef97 *inst/benchmarking/colRowVars_subset.md.rsp 7a8c58c7090624f9536603917180509f *inst/benchmarking/colRowWeightedMeans.md.rsp 8fc13c34938d570c54c5fce605c62264 *inst/benchmarking/colRowWeightedMeans_subset.md.rsp beb6d3d46a9903d3cdc37bba972030f4 *inst/benchmarking/colRowWeightedMedians.md.rsp 5112673ced8db5ba55a9fdcf99a6e2e6 *inst/benchmarking/colRowWeightedMedians_subset.md.rsp 27de27a4248555e4fad67ee53e8d622e *inst/benchmarking/count.md.rsp 6dadc4d7f3fd6783dd224e1ae8b07456 *inst/benchmarking/count_subset.md.rsp ab9f5049c780d39eca80e084f60cf68e *inst/benchmarking/includes/appendix.md.rsp 75a4dbe1cebc11442ce3f8626cb2f786 *inst/benchmarking/includes/footer.md.rsp 80fec8731611547e148434c4a8af80a7 *inst/benchmarking/includes/header.md.rsp d4fe14ce0a8fe23b829bd5d40ecb8638 *inst/benchmarking/includes/references.md.rsp 5e343c63df6b19f2e8f3d13784ac522d *inst/benchmarking/includes/results.md.rsp 1127a9839354eb9de5fd6641e0d2949e *inst/benchmarking/includes/setup.md.rsp 8744fca6100198e786187172e3e04beb *inst/benchmarking/index.md.rsp 672790929425f88eb1235e579c7ae487 *inst/benchmarking/indexByRow.md.rsp 713004c069039309a947587cae09b9c4 *inst/benchmarking/logSumExp.md.rsp 833c6522edfafaa26c83461f497ec6d2 *inst/benchmarking/logSumExp_subset.md.rsp 03268c8274ecc261af5a583484679b94 *inst/benchmarking/madDiff.md.rsp a475051a1015b1a017826038a9be9d5d *inst/benchmarking/madDiff_subset.md.rsp 20b9686d1ccade9286f73e8a9a80e96c *inst/benchmarking/meanOver.md.rsp 7968239a634758465d22210495f53c48 *inst/benchmarking/meanOver_subset.md.rsp 87bd2dbcf60d75ca69eaf37f52c4b436 *inst/benchmarking/product.md.rsp f9178f87274f6c9725cf8a19ae60fc22 *inst/benchmarking/product_subset.md.rsp 5e0977113a979580db793bce8c0bdbda *inst/benchmarking/sumOver.md.rsp a8c4feb13044c83d13f34d1206cf6dcb *inst/benchmarking/sumOver_subset.md.rsp 4438b091dd392c8a0c305cbf48e1b354 *inst/benchmarking/t_tx_OP_y.md.rsp c10e3a67c52dce50cb92f1c598aa5941 *inst/benchmarking/t_tx_OP_y_subset.md.rsp 2e5cf31ad74af1915311a48f5c83732a *inst/benchmarking/varDiff.md.rsp 2fbc4fc9f075e67fbdd734930b29e0f5 *inst/benchmarking/varDiff_subset.md.rsp 3e75a042f402c6d60fbdb4c236bdb0a9 *inst/benchmarking/weightedMean.md.rsp 432f1fcacc7b475a89d3b65211c27bf6 *inst/benchmarking/weightedMean_subset.md.rsp 8e98dd36afed6de19c3f9cee6fd6516f *inst/benchmarking/weightedMedian.md.rsp bee2599dcc692be01fc03af2d3329589 *inst/benchmarking/weightedMedian_subset.md.rsp 7b063158ba0bca2a2550bd7c4f9524c4 *inst/benchmarking/x_OP_y.md.rsp 1e256e0db41335d3e71c14b38498faa3 *inst/benchmarking/x_OP_y_subset.md.rsp 61ebcb748f8f5ff580836f2f0fd15770 *inst/doc/matrixStats-methods.html bbfe454400582a1e2ffeb88501432a41 *inst/doc/matrixStats-methods.md.rsp 3be5b91c497cfc857af456c5458bb6e0 *man/allocMatrix.Rd 1c9544e8bddfbeae142d7f1c055177d3 *man/anyMissing.Rd 2e8bb8b7f0351bc8d2be2e28c613cdf3 *man/binCounts.Rd 353efe3d59da3211a95bca113a49db09 *man/binMeans.Rd 40afa2038e0cf63282fcb55ad44b2f45 *man/diff2.Rd ffc33b174ed4c79d38db4df4eba34969 *man/indexByRow.Rd 6d8ed27a60fe5a42d057fb86a7614de2 *man/logSumExp.Rd 0a7f1e35cc6ca57657dcf4ebd97976d1 *man/matrixStats-package.Rd 937f5e3ffa36565eb6f3557821948f0e *man/meanOver.Rd 669e48a83c7256e705b7058deda57f25 *man/rowAvgsPerColSet.Rd c8ac4723c22ac671644c0bcf9c732b39 *man/rowCollapse.Rd e94e6a51491740ce9b2461bacce809a6 *man/rowCounts.Rd 30e9caa9973261ea484d605f5993a6b4 *man/rowCumsums.Rd a553031ea4928099ee6a639e325f5ca6 *man/rowDiffs.Rd 965a27159f303e2021fa1b26f8c66010 *man/rowIQRs.Rd 1cf5d195ab7ec1d7b7cfc50d8f14481d *man/rowLogSumExps.Rd 0e98aea37fc823abe037c253b6354005 *man/rowMedians.Rd e73dd3319e5094c467a3c27e6412e086 *man/rowOrderStats.Rd bd1ceb8afa1d2318ac67cd73fb35d37b *man/rowProds.Rd 4b7db85ae16623cc0e07d68b7d8196d4 *man/rowQuantiles.Rd 4b4b7e8d83e5bacf278c9ce35354721f *man/rowRanges.Rd 5f1afd83cc21e41038b6ce2ce9b7f124 *man/rowRanks.Rd f37f1d56e88e3cf9fc8c5925e4aa7085 *man/rowSds.Rd c020e22ba124eb934f3987f7db02516f *man/rowTabulates.Rd 70700c04b7b9d6305ea0e26e527cdd34 *man/rowVars.Rd a9754d655430f6cef9f8f5c217129834 *man/rowWeightedMeans.Rd ad6d5e215e230f45dd2e60dac862214a *man/rowWeightedMedians.Rd 26acb1953d4910b6d14b58ee2cab883a *man/signTabulate.Rd ca983b06e01b11343d9cf152e1dc5520 *man/sumOver.Rd 47b7656ce94b09fc7ec65d18a08fae53 *man/validateIndices.Rd d189daae1944168a3598590a855d2552 *man/varDiff.Rd 9a982aeeabe24fb34750b12feb28de73 *man/weightedMad.Rd b0419e38aa289474a0c8952613223e9b *man/weightedMean.Rd 7bac4dbc75b22f4c76f5046458a2623f *man/weightedMedian.Rd 4fa97b663c7333b1e6cf3b5f4daf39c3 *man/weightedVar.Rd b6a92abf167eca7196abbbf572cbaacd *man/xUNDERSCOREOPUNDERSCOREy.Rd ddead138f53958da47a56f4b98d48793 *src/allocMatrix2.c 3f1b76f446600a0a438d3f2b45afec34 *src/anyMissing.c 40437ef105eaa828e83005f0f989752c *src/anyMissing_TYPE-template.h d2aeb29505a48f297707c077a54056f1 *src/api.h 7eb8fcd98f39e47623a2b85583906632 *src/binCounts-BINBY-template.h 4ced9b328349ef007414044632c398ea *src/binCounts.c 7b85f0a78de3f299cb0ebee4e087cff0 *src/binMeans-BINBY-template.h b73e99b94350876ba274b7964499ec35 *src/binMeans.c fed4979b893193e20b382de4796d86d6 *src/colCounts.c a456f19c1131ec00db38d9538faa47b1 *src/colCounts_TYPE-template.h 2cca249b6bc2b7b1702a79f4c6f87481 *src/colOrderStats.c 2cace6e9752d1c3fceb446364672c5f3 *src/colOrderStats_TYPE-template.h 1a2b97311bc3195908624c74d4e303be *src/colRanges.c 7a8eacd6d469af845895e692eea1461f *src/colRanges_TYPE-template.h 9cf9ed35d9a5ef37fe3f509187b84693 *src/diff2.c adda98ed29ea20c19fab3b32cbe5aba6 *src/diff2_TYPE-template.h 122818a15b1026f4617413b21a43f1a1 *src/indexByRow.c 809233fa47aa54db52b66fb1eea85c3d *src/init.c 8a9a84e2a17cd5c7f3efb561d8160399 *src/logSumExp.c 815a557e788542b5cb2e21affb9ce81c *src/logSumExp_TYPE-template.h efa7fba525b03b9112b836774c581c60 *src/macros.h b15539c2415122ffaaed429e03e09852 *src/meanOver.c a3f988aa491acc57788165eea2b45dc7 *src/meanOver_TYPE-template.h 3562173a04dec8dc2ef5f761f25ee0ac *src/productExpSumLog.c f841fd14d23489d921b108a8830ff560 *src/productExpSumLog_TYPE-template.h b0e357e87f4826e34602a1e739b6c1b0 *src/psortKM.c f958b4fa57ffd0dddbd19778a1dbf15a *src/rowCounts.c f38f8e364c8ac0686b8d0e57f709f221 *src/rowCounts_TYPE-template.h bfdbf5b6a4ed0e9d874e1bc864c3daa2 *src/rowCumMinMaxs.c 446d93437d9f5fdbfa780f39572b3277 *src/rowCumMinMaxs_TYPE-template.h 079d94505a3b0ecad4df5cfe98c476fc *src/rowCumprods.c 2bd4d5fde6ea621e26dfe70d2fe3e5d4 *src/rowCumprods_TYPE-template.h 35126f2248d34e72ed8f2d79d2e41dcf *src/rowCumsums.c b29951982a6aa2767775f3c5bc1e488b *src/rowCumsums_TYPE-template.h ea798b7d2d0f5fe47e189b239793a998 *src/rowDiffs.c 6edc092fb0104e35a71584c3a610f086 *src/rowDiffs_TYPE-template.h d886814098acb4389862aebfb242ca49 *src/rowLogSumExp.c cd7d545bb46dd3051aeb693c3c3f5459 *src/rowLogSumExp_TYPE-template.h e8bd48dadcade80e8765514ce18246e6 *src/rowMads.c e73569085a7216d0498c8d88a62a4f3d *src/rowMads_TYPE-template.h d11e97082d070111157330f8c194895b *src/rowMedians.c 6617555e64ce3e6fcf23047d6a4da68e *src/rowMedians_TYPE-template.h ff135333000f622cc6eff32f3c8d1b26 *src/rowOrderStats.c b00c6008c627b0cc579a643caf73cac4 *src/rowOrderStats_TYPE-template.h 76b1b1f5cbf710003a17c33cf868bfa3 *src/rowRanges.c c2d68a65c23254440d4d7e6827645812 *src/rowRanges_TYPE-template.h 01513c8e6adfea96d9ffa781888551a3 *src/rowRanksWithTies.c c72c45c69688a93335445cdf629c62e1 *src/rowRanksWithTies_TYPE_TIES-template.h 47792eb63c2fbcacf83d9ff9f94c5ec1 *src/rowVars.c d4f3c791bde228a66379875260635e02 *src/rowVars_TYPE-template.h 8c38727295dafa8ce626f7c620a87efd *src/signTabulate.c 21740ef5700e782054b972147d6bf9a1 *src/signTabulate_TYPE-template.h 71c4608f92ee543b9f92fc985f21c0bc *src/sumOver.c d0cba4b63d854131ac180eef26ce43f9 *src/sumOver_TYPE-template.h f9642134425ed8ed448255a7d5d99e98 *src/templates-gen-matrix-vector.h 8994e310c537bcbfb2100a5f7871c08b *src/templates-gen-matrix.h 22a8632fdb3673e9038ac34c6359b415 *src/templates-gen-vector.h d61fe5990943d84359968910cfde4bb7 *src/templates-types.h 78bbf931f308f8cf910cf70f646d4597 *src/templates-types_undef.h 1fe96f23b52c292f78943b4980eb0f33 *src/types.h 955f37d5d36cf492863872a6bcdb845d *src/utils.h ce42159813094915b42c46b2ae31b4e9 *src/validateIndices.c 03505b1c819e0ea1d6a36b680ca9019c *src/validateIndices_TYPE-template.h 7560430fd6d4172e44c9707b2860cc75 *src/weightedMean.c 4c416712906e44769b9bd8e8f5c3878a *src/weightedMean_TYPE-template.h 6d8e9b6e14ca5c0bbe15995551d2b21f *src/weightedMedian.c a777067f6cda0aaf98f9e09d3d751523 *src/weightedMedian_TYPE-template.h c4fab99507d6672f89990bd5b1228eec *src/x_OP_y.c bde3599c9ca1c88a6268445726eb1fb5 *src/x_OP_y_TYPE-template.h 6b09aca9e9b79f8fcb85cdca51b305ad *tests/allocArray.R 0844bf090717347c46eeae7e7761fd31 *tests/allocMatrix.R 21b5cbdf48874928843e56f60ee8c449 *tests/allocVector.R f3f5505626d59ecc6c8d498bf6885de0 *tests/anyMissing.R 4a7bb2cb04f010879a878d121acab169 *tests/anyMissing_subset.R b90edee85808dbee0ef97af004352844 *tests/benchmark.R fd636ec43f81ad7f1ecef7bb12062450 *tests/binCounts.R f9a44bffc5f9ccfc8c80eb297ffa9dca *tests/binCounts_subset.R fbaa73cb2c1e60a132c7a04869fec67a *tests/binMeans,binCounts.R d006ea144c39a1ded48b185c27a23b65 *tests/binMeans,binCounts_subset.R 53679ebbeee7d4ef5e7217ff8076ddce *tests/count.R c86c27c46ded4c964c51d925ad18cc87 *tests/count_subset.R 851e382ed2c520ccfe239a1edb720200 *tests/diff2.R 51f01f331e0a04894a41e8dc7f93d162 *tests/diff2_subset.R 95b6d9d8def43a4a4fb9a1c3a88fc5a9 *tests/indexByRow.R f04b6bf679d3558a7cdb20f76f549136 *tests/logSumExp.R 3e38b40c6b252ead00a0c5ad938aeafd *tests/logSumExp_subset.R 361afd9da7276beab8ed7999d92c9e32 *tests/meanOver.R 48cd2fd7aa8ea8108c1a4dfbb3395623 *tests/meanOver_subset.R c46a6f5b67da1af6cddbe28735a12dab *tests/product.R e74c11f662a66dcd0f4ef24bddca14f5 *tests/product_subset.R e996793a93f55357ddea79aa865c45f3 *tests/psortKM.R 9fd8dd36b901e81ca0bd88d160e9c8c6 *tests/rowAllAnys.R f4dd0683da587b8cdedd911a90687cc2 *tests/rowAllAnys_subset.R 6b645494332ef6d9e128bfa98cc73960 *tests/rowAvgsPerColSet.R 7107bd861a6218d16d6678501865501e *tests/rowAvgsPerColSet_subset.R 1408f031afac3258638f8e91cbac75bd *tests/rowCollapse.R 5f618510f7593699d60845fcc9b566ea *tests/rowCollapse_subset.R 993e34a7064fe8bb019bef8288336231 *tests/rowCounts.R e3d919105ba90ab948195cf707886ade *tests/rowCounts_subset.R 960cca66a390770b02266743b0c226f8 *tests/rowCumMinMaxs.R 78d434238af7206e9b7de6ad8bf32c5b *tests/rowCumMinMaxs_subset.R f3151387cd1b7cacaa640f20479900ea *tests/rowCumprods.R 64748f9f27d0803cc18c8390614b02fd *tests/rowCumprods_subset.R 1f6d51577ed4ff03bfdf385fecd3afde *tests/rowCumsums.R db4dc1eae9501e1735562dd219a94253 *tests/rowCumsums_subset.R e20a6674d9c33035c2bed8b7eb4193a2 *tests/rowDiffs.R 38c1964ab422aed9156c26131696ff4e *tests/rowDiffs_subset.R 42ffd346935ba2bc2e592093a4a59315 *tests/rowIQRs.R a868f1913f2b7b583534852116357b8c *tests/rowIQRs_subset.R a90921df83d1d3607d47510f07ac8d2b *tests/rowLogSumExps.R 926d2a450d7ccc0e99a50389ca740519 *tests/rowLogSumExps_subset.R 5c85e4d3283d8e8e1edc574b16a129f4 *tests/rowMads.R 360e962f909b4acc1b86e231077b00e8 *tests/rowMads_subset.R 6b54c9571d7dd77fde12b381dfa11daa *tests/rowMedians.R 23352e23e1a585cef899e53228d580f4 *tests/rowMedians_subset.R c4707c1bfa1bb8d073de164f397132a1 *tests/rowOrderStats.R c589e51ad7704712cde8dd16f080cd0f *tests/rowOrderStats_subset.R b6e990720a7bb5c9127a03a806030726 *tests/rowProds.R 7c94c7ed129b5a041f0cf0fb4bf285e7 *tests/rowProds_subset.R b78bdc9f8774fe8a30f47a2d612837ee *tests/rowQuantiles.R 0a38dfc408b460501614e1b391f78963 *tests/rowQuantiles_subset.R 94497699b8fe53ef3daeb4962caf8f2f *tests/rowRanges.R 9ad4c12f8b6556f452979972b71234a0 *tests/rowRanges_subset.R 68c2084a00a6599961bfa2fc27f1b7d3 *tests/rowRanks.R 43b32eb5b6e022916fbd8ea2ea2d5afc *tests/rowRanks_subset.R 4ff6d1cdd2ff067b10aee97c39b763ff *tests/rowSds.R fa9021b34151326e93f9484b8e92d57d *tests/rowSds_subset.R b4fa113751df716127c5e3edda1e6153 *tests/rowTabulates.R d913262679503b9722606276f5223d7f *tests/rowTabulates_subset.R 8694849a466b91f95fe7540c24e6d78c *tests/rowVarDiffs.R ca43ea49b246472b55e8bc15dae0ec22 *tests/rowVarDiffs_mad,iqr_subset.R a9eed650af2102bd5742fbea8e9210e8 *tests/rowVarDiffs_var,sd_subset.R c4004d07adc3a794c4dcda9233734472 *tests/rowVars.R c775702a2952856e73750daac7ba0da5 *tests/rowVars_subset.R 8ddfe1ad30924b2da33e7c45f8742e19 *tests/rowWeightedMeans.R 6beba9f2e5a95325ba3aebc1481e77f0 *tests/rowWeightedMeans_subset.R 65d1b8c60a95d11c90a2ace6679017ec *tests/rowWeightedMedians.R a0e9efbf4a599717771c054c68d77c34 *tests/rowWeightedMedians_subset.R 6cb52743be8a1c13902b3edc97bd97ec *tests/rowWeightedVars.R fa860a2f62f77c6d4f585d4585ad8275 *tests/rowWeightedVars_subset.R 824536a7e210775091eddc2fc7f5aac1 *tests/signTabulate.R 6aed7672793881b0df65c92695d29955 *tests/signTabulate_subset.R 5b4ef49eff29f2789246f20e3489ce32 *tests/sumOver.R 4c504b76349c0afee64b69a881487985 *tests/sumOver_subset.R a16d0ec068b8b29924d3351eca67847d *tests/utils/validateIndicesFramework.R 32e4a296130b8b2af1bd8f6af6651ec6 *tests/validateIndices.R 8e4cf994f2ecd7f21347931fd03c4392 *tests/varDiff_etal.R 0be42af3fc41de0f804c2e607cf8278b *tests/varDiff_etal_subset.R f681d2b636aadef71934fb37eaa3fb2c *tests/weightedMean.R 55a0462e1371ba08af6ce93266b3ebbe *tests/weightedMean_subset.R f05f73f8b371c562d57e0769e2333c9e *tests/weightedMedian.R d9689ad97aedb2e97a9f35a7533fe74a *tests/weightedMedian_subset.R b5fc858bc7f231d614c641acbfb59dfc *tests/weightedVar.R 3c087daf3e872f218f43622d0655994a *tests/weightedVar_etal.R b33ddd505087ae96c1d13be1764063aa *tests/weightedVar_etal_subset.R 8a54c1e37d221d2ada391942bef3a760 *tests/x_OP_y.R 59961b71529f2db9795dc6875fc59eee *tests/x_OP_y_subset.R ae02684a77e833bf48a7a1e4b3b10dca *tests/zzz.package-unload.R bbfe454400582a1e2ffeb88501432a41 *vignettes/matrixStats-methods.md.rsp matrixStats/build/0000755000175100001440000000000012776336765013740 5ustar hornikusersmatrixStats/build/vignette.rds0000644000175100001440000000041612776336765016300 0ustar hornikusers‹mPËnÂ0tâR%.§ôšàŒ¸ôR•zµâˆä—l§4·~yaÓÄ –ÖÞµggÇóµ „¤„Ò„¤SºÂ-ÇxÂHHFæx®%ó¶ùÙ{æÝ«ÐÜ•’—Ö™ùr…ÜûVJf»B×EݪÊ7Z¹¨áùõÁKáz•«ÿz¸Ç•¥WïÅ$ÄäÓ-P¼¿þ‹ú§#$æ‡ê*¯màdƈn,2«.ðVZ´R¹ Hmpâ ä%§HÑì º£¶AÒ &“v€@öÙøKAß·»1M>â/ÞñßØ3Gõe°hÙ›ù‹Û Wìc%˜‹}\pæYY[ìïuŸ<‹ËF4matrixStats/DESCRIPTION0000644000175100001440000000300312776367215014335 0ustar hornikusersPackage: matrixStats Version: 0.51.0 Depends: R (>= 2.12.0) Suggests: base64enc, ggplot2, knitr, microbenchmark, R.devices, R.rsp VignetteBuilder: R.rsp Date: 2016-10-08 Title: Functions that Apply to Rows and Columns of Matrices (and to Vectors) Authors@R: c( person("Henrik", "Bengtsson", role=c("aut", "cre", "cph"), email="henrikb@braju.com"), person("Hector", "Corrada Bravo", role="ctb"), person("Robert", "Gentleman", role="ctb"), person("Ola", "Hossjer", role="ctb"), person("Harris", "Jaffee", role="ctb"), person("Dongcan", "Jiang", role="ctb"), person("Peter", "Langfelder", role="ctb")) Author: Henrik Bengtsson [aut, cre, cph], Hector Corrada Bravo [ctb], Robert Gentleman [ctb], Ola Hossjer [ctb], Harris Jaffee [ctb], Dongcan Jiang [ctb], Peter Langfelder [ctb] Maintainer: Henrik Bengtsson Description: High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian(). License: Artistic-2.0 LazyLoad: TRUE NeedsCompilation: yes ByteCompile: TRUE URL: https://github.com/HenrikBengtsson/matrixStats BugReports: https://github.com/HenrikBengtsson/matrixStats/issues Packaged: 2016-10-09 03:48:05 UTC; hb Repository: CRAN Date/Publication: 2016-10-09 09:15:25 matrixStats/man/0000755000175100001440000000000012776226773013411 5ustar hornikusersmatrixStats/man/rowOrderStats.Rd0000644000175100001440000000406012776330571016512 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowOrderStats.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowOrderStats} \alias{rowOrderStats} \alias{colOrderStats} \title{Gets an order statistic for each row (column) in a matrix} \description{ Gets an order statistic for each row (column) in a matrix. } \usage{ rowOrderStats(x, rows=NULL, cols=NULL, which, dim.=dim(x), ...) colOrderStats(x, rows=NULL, cols=NULL, which, dim.=dim(x), ...) } \arguments{ \item{x}{A \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}.} \item{rows, cols}{A \code{\link[base]{vector}} indicating subset of rows (and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{which}{An \code{\link[base]{integer}} index in [1,K] ([1,N]) indicating which order statistic to be returned.} \item{dim.}{An \code{\link[base]{integer}} \code{\link[base]{vector}} of length two specifying the dimension of \code{x}, also when not a \code{\link[base]{matrix}}.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N (K). } \details{ The implementation of \code{rowOrderStats()} is optimized for both speed and memory. To avoid coercing to \code{\link[base]{double}}s (and hence memory allocation), there is a unique implementation for \code{\link[base]{integer}} matrices. } \section{Missing values}{ This method does \emph{not} handle missing values, that is, the result corresponds to having \code{na.rm=FALSE} (if such an argument would be available). } \author{ The native implementation of \code{rowOrderStats()} was adopted by Henrik Bengtsson from Robert Gentleman's \code{rowQ()} in the \pkg{Biobase} package. } \seealso{ See \code{rowMeans()} in \code{\link[base]{colSums}}(). } \keyword{array} \keyword{iteration} \keyword{robust} \keyword{univar} matrixStats/man/rowWeightedMedians.Rd0000644000175100001440000000501312776330572017461 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowWeightedMedians.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowWeightedMedians} \alias{rowWeightedMedians} \alias{colWeightedMedians} \title{Calculates the weighted medians for each row (column) in a matrix} \description{ Calculates the weighted medians for each row (column) in a matrix. } \usage{ rowWeightedMedians(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) colWeightedMedians(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) } \arguments{ \item{x}{A \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}.} \item{w}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of length K (N).} \item{rows, cols}{A \code{\link[base]{vector}} indicating subset of rows (and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, missing values are excluded from the calculation, otherwise not.} \item{...}{Additional arguments passed to \code{\link{weightedMedian}}().} } \value{ Returns a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N (K). } \details{ The implementations of these methods are optimized for both speed and memory. If no weights are given, the corresponding \code{\link{rowMedians}}()/\code{colMedians()} is used. } \examples{ x <- matrix(rnorm(20), nrow=5, ncol=4) print(x) # Non-weighted row averages xM0 <- rowMedians(x) xM <- rowWeightedMedians(x) stopifnot(all.equal(xM, xM0)) # Weighted row averages (uniform weights) w <- rep(2.5, ncol(x)) xM <- rowWeightedMedians(x, w=w) stopifnot(all.equal(xM, xM0)) # Weighted row averages (excluding some columns) w <- c(1,1,0,1) xM0 <- rowMedians(x[,(w == 1),drop=FALSE]); xM <- rowWeightedMedians(x, w=w) stopifnot(all.equal(xM, xM0)) # Weighted row averages (excluding some columns) w <- c(0,1,0,0) xM0 <- rowMedians(x[,(w == 1),drop=FALSE]); xM <- rowWeightedMedians(x, w=w) stopifnot(all.equal(xM, xM0)) # Weighted averages by rows and columns w <- 1:4 xM1 <- rowWeightedMedians(x, w=w) xM2 <- colWeightedMedians(t(x), w=w) stopifnot(all.equal(xM2, xM1)) } \author{Henrik Bengtsson} \seealso{ See \code{\link{rowMedians}}() and \code{colMedians()} for non-weighted medians. Internally, \code{\link{weightedMedian}}() is used. } \keyword{array} \keyword{iteration} \keyword{robust} \keyword{univar} matrixStats/man/weightedMedian.Rd0000644000175100001440000001255212776330572016614 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % weightedMedian.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{weightedMedian} \alias{weightedMedian} \encoding{latin1} \title{Weighted Median Value} \usage{ weightedMedian(x, w=NULL, idxs=NULL, na.rm=FALSE, interpolate=is.null(ties), ties=NULL, ...) } \description{ Computes a weighted median of a numeric vector. } \arguments{ \item{x}{a \code{\link[base]{numeric}} \code{\link[base]{vector}} containing the values whose weighted median is to be computed.} \item{w}{a vector of weights the same length as \code{x} giving the weights to use for each element of \code{x}. Negative weights are treated as zero weights. Default value is equal weight to all values.} \item{idxs}{A \code{\link[base]{vector}} indicating subset of elements to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{na.rm}{a logical value indicating whether \code{\link[base]{NA}} values in \code{x} should be stripped before the computation proceeds, or not. If \code{\link[base]{NA}}, no check at all for \code{\link[base]{NA}}s is done. Default value is \code{\link[base]{NA}} (for efficiency).} \item{interpolate}{If \code{\link[base:logical]{TRUE}}, linear interpolation is used to get a consistent estimate of the weighted median.} \item{ties}{If \code{interpolate == FALSE}, a character string specifying how to solve ties between two \code{x}'s that are satisfying the weighted median criteria. Note that at most two values can satisfy the criteria. When \code{ties} is \code{"min"}, the smaller value of the two is returned and when it is \code{"max"}, the larger value is returned. If \code{ties} is \code{"mean"}, the mean of the two values is returned. Finally, if \code{ties} is \code{"weighted"} (or \code{\link[base]{NULL}}) a weighted average of the two are returned, where the weights are weights of all values \code{x[i] <= x[k]} and \code{x[i] >= x[k]}, respectively.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} scalar. } \details{ For the \code{n} elements \code{x = c(x[1], x[2], ..., x[n])} with positive weights \code{w = c(w[1], w[2], ..., w[n])} such that \code{sum(w) = S}, the \emph{weighted median} is defined as the element \code{x[k]} for which the total weight of all elements \code{x[i] < x[k]} is less or equal to \code{S/2} and for which the total weight of all elements \code{x[i] > x[k]} is less or equal to \code{S/2} (c.f. [1]). If \code{w} is missing then all elements of \code{x} are given the same positive weight. If all weights are zero, \code{\link[base]{NA}}_real_ is returned. If one or more weights are \code{Inf}, it is the same as these weights have the same weight and the others has zero. This makes things easier for cases where the weights are result of a division with zero. The weighted median solves the following optimization problem: \deqn{\alpha^* = \arg_\alpha \min \sum_{k=1}{K} w_k |x_k-\alpha|} where \eqn{x=(x_1,x_2,\ldots,x_K)} are scalars and \eqn{w=(w_1,w_2,\ldots,w_K)} are the corresponding "weights" for each individual \eqn{x} value. } \examples{ x <- 1:10 n <- length(x) m1 <- median(x) # 5.5 m2 <- weightedMedian(x) # 5.5 stopifnot(identical(m1, m2)) w <- rep(1, n) m1 <- weightedMedian(x, w) # 5.5 (default) m2 <- weightedMedian(x, ties="weighted") # 5.5 (default) m3 <- weightedMedian(x, ties="min") # 5 m4 <- weightedMedian(x, ties="max") # 6 stopifnot(identical(m1,m2)) # Pull the median towards zero w[1] <- 5 m1 <- weightedMedian(x, w) # 3.5 y <- c(rep(0,w[1]), x[-1]) # Only possible for integer weights m2 <- median(y) # 3.5 stopifnot(identical(m1,m2)) # Put even more weight on the zero w[1] <- 8.5 weightedMedian(x, w) # 2 # All weight on the first value w[1] <- Inf weightedMedian(x, w) # 1 # All weight on the last value w[1] <- 1 w[n] <- Inf weightedMedian(x, w) # 10 # All weights set to zero w <- rep(0, n) weightedMedian(x, w) # NA # Simple benchmarking bench <- function(N=1e5, K=10) { x <- rnorm(N) gc() t <- c() t[1] <- system.time(for (k in 1:K) median(x))[3] t[2] <- system.time(for (k in 1:K) weightedMedian(x))[3] t <- t / t[1] names(t) <- c("median", "weightedMedian") t } print(bench(N= 5, K=100)) print(bench(N= 50, K=100)) print(bench(N= 200, K=100)) print(bench(N= 1000, K=100)) print(bench(N= 10e3, K= 20)) print(bench(N=100e3, K= 20)) } \seealso{ \code{\link[stats]{median}}, \code{\link[base]{mean}}() and \code{\link{weightedMean}}(). } \references{ [1] T.H. Cormen, C.E. Leiserson, R.L. Rivest, Introduction to Algorithms, The MIT Press, Massachusetts Institute of Technology, 1989. } \author{ Henrik Bengtsson and Ola Hossjer, Centre for Mathematical Sciences, Lund University. Thanks to Roger Koenker, Econometrics, University of Illinois, for the initial ideas. } \keyword{univar} \keyword{robust} matrixStats/man/rowCollapse.Rd0000644000175100001440000000342212776330571016163 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowCollapse.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowCollapse} \alias{rowCollapse} \alias{colCollapse} \title{Extracts one cell per row (column) from a matrix} \description{ Extracts one cell per row (column) from a matrix. The implementation is optimized for memory and speed. } \usage{ rowCollapse(x, idxs, rows=NULL, dim.=dim(x), ...) colCollapse(x, idxs, cols=NULL, dim.=dim(x), ...) } \arguments{ \item{x}{An NxK \code{\link[base]{matrix}}.} \item{idxs}{An index \code{\link[base]{vector}} of (maximum) length N (K) specifying the columns (rows) to be extracted.} \item{rows, cols}{A \code{\link[base]{vector}} indicating subset of rows (and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{dim.}{An \code{\link[base]{integer}} \code{\link[base]{vector}} of length two specifying the dimension of \code{x}, also when not a \code{\link[base]{matrix}}.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{vector}} of length N (K). } \examples{ x <- matrix(1:27, ncol=3) y <- rowCollapse(x, 1) stopifnot(identical(y, x[,1])) y <- rowCollapse(x, 2) stopifnot(identical(y, x[,2])) y <- rowCollapse(x, c(1,1,1,1,1,3,3,3,3)) stopifnot(identical(y, c(x[1:5,1], x[6:9,3]))) y <- rowCollapse(x, 1:3) print(y) yT <- c(x[1,1],x[2,2],x[3,3],x[4,1],x[5,2],x[6,3],x[7,1],x[8,2],x[9,3]) stopifnot(identical(y, yT)) } \author{Henrik Bengtsson} \seealso{ \emph{Matrix indexing} to index elements in matrices and arrays, cf. \code{\link[base]{[}}(). } \keyword{utilities} matrixStats/man/xUNDERSCOREOPUNDERSCOREy.Rd0000644000175100001440000000473112776330572017561 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % x_OP_y.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{xUNDERSCOREOPUNDERSCOREy} \alias{xUNDERSCOREOPUNDERSCOREy} \alias{x_OP_y} \alias{t_tx_OP_y} \title{Fast calculation of 'z <- x OP y' and 'z <- t(t(x) OP y)'} \usage{ x_OP_y(x, y, OP, xrows=NULL, xcols=NULL, yidxs=NULL, commute=FALSE, na.rm=FALSE) t_tx_OP_y(x, y, OP, xrows=NULL, xcols=NULL, yidxs=NULL, commute=FALSE, na.rm=FALSE) } \description{ Fast calculation of 'z <- x OP y' and 'z <- t(t(x) OP y)', where OP can be +, -, *, and /. For + and *, na.rm=TRUE will drop missing values first. } \arguments{ \item{x}{A \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}.} \item{y}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of length L.} \item{OP}{A \code{\link[base]{character}} specifying which operator to use.} \item{xrows, xcols}{A \code{\link[base]{vector}} indicating subset of rows (and/or columns) to operate over 'x'. If \code{\link[base]{NULL}}, no subsetting is done.} \item{idxs}{A \code{\link[base]{vector}} indicating subset of elements to operate over 'y'. If \code{\link[base]{NULL}}, no subsetting is done.} \item{commute}{If \code{\link[base:logical]{TRUE}}, 'y OP x' ('t(y OP t(x))') is calculated, otherwise 'x OP y' ('t(t(x) OP y)').} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, missing values are ignored, otherwise not.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}. } \section{Missing values}{ If \code{na.rm=TRUE}, then missing values are "dropped" before applying the operator to each pair of values. For instance, if \code{x[1,1]} is a missing value, then the result of \code{x[1,1] + y[1]} equals \code{y[1]}. If also \code{y[1]} is a missing value, then the result is a missing value. This only applies to additions and multiplications. For subtractions and divisions, argument \code{na.rm} is ignored. } \examples{ x <- matrix(c(1,2,3,NA,5,6), nrow=3, ncol=2) # Add 'y' to each column y <- 1:2 z0 <- x + y z1 <- x_OP_y(x, y, OP="+") print(z1) stopifnot(all.equal(z1, z0)) # Add 'y' to each row y <- 1:3 z0 <- t(t(x) + y) z1 <- t_tx_OP_y(x, y, OP="+") print(z1) stopifnot(all.equal(z1, z0)) } \author{Henrik Bengtsson} \keyword{internal} matrixStats/man/binCounts.Rd0000644000175100001440000000421412776330571015635 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % binCounts.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{binCounts} \alias{binCounts} \title{Fast element counting in non-overlapping bins} \usage{ binCounts(x, idxs=NULL, bx, right=FALSE, ...) } \description{ Counts the number of elements in non-overlapping bins } \arguments{ \item{x}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of K positions for to be binned and counted.} \item{idxs}{A \code{\link[base]{vector}} indicating subset of elements to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{bx}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of B+1 ordered positions specifying the B > 0 bins \code{[bx[1],bx[2])}, \code{[bx[2],bx[3])}, ..., \code{[bx[B],bx[B+1])}.} \item{right}{If \code{\link[base:logical]{TRUE}}, the bins are right-closed (left open), otherwise left-closed (right open).} \item{...}{Not used.} } \value{ Returns an \code{\link[base]{integer}} \code{\link[base]{vector}} of length B with non-negative integers. } \details{ \code{binCounts(x, bx, right=TRUE)} gives equivalent results as \code{rev(binCounts(-x, bx=rev(-bx), right=FALSE))}, but is faster and more memory efficient. } \section{Missing and non-finite values}{ Missing values in \code{x} are ignored/dropped. Missing values in \code{bx} are not allowed and gives an error. } \seealso{ An alternative for counting occurrences within bins is \code{\link[graphics]{hist}}, e.g. \code{hist(x, breaks=bx, plot=FALSE)$counts}. That approach is ~30-60\% slower than \code{binCounts(..., right=TRUE)}. To count occurrences of indices \code{x} (positive \code{\link[base]{integer}}s) in \code{[1,B]}, use \code{tabulate(x, nbins=B)}, where \code{x} does \emph{not} have to be sorted first. For details, see \code{\link[base]{tabulate}}(). To average values within bins, see \code{\link{binMeans}}(). } \author{Henrik Bengtsson} \keyword{univar} matrixStats/man/rowProds.Rd0000644000175100001440000000430712776330571015513 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowProds.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowProds} \alias{rowProds} \alias{rowProds} \alias{colProds} \alias{product} \title{Calculates the product for each row (column) in a matrix} \description{ Calculates the product for each row (column) in a matrix. } \usage{ rowProds(x, rows=NULL, cols=NULL, na.rm=FALSE, method=c("direct", "expSumLog"), ...) colProds(x, rows=NULL, cols=NULL, na.rm=FALSE, method=c("direct", "expSumLog"), ...) product(x, idxs=NULL, na.rm=FALSE, ...) } \arguments{ \item{x}{A \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}.} \item{idxs, rows, cols}{A \code{\link[base]{vector}} indicating subset of elements (or rows and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, missing values are ignored, otherwise not.} \item{method}{A \code{\link[base]{character}} string specifying how each product is calculated.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N (K). } \details{ If \code{method="expSumLog"}, then then \code{\link{product}}() function is used, which calculates the produce via the logarithmic transform (treating negative values specially). This improves the precision and lowers the risk for numeric overflow. If \code{method="direct"}, the direct product is calculated via the \code{\link[base]{prod}}() function. } \section{Missing values}{ Note, if \code{method="expSumLog"}, \code{na.rm=FALSE}, and \code{x} contains missing values (\code{\link[base]{NA}} or \code{\link[base:is.finite]{NaN}}), then the calculated value is also missing value. Note that it depends on platform whether \code{\link[base:is.finite]{NaN}} or \code{\link[base]{NA}} is returned when an \code{\link[base:is.finite]{NaN}} exists, cf. \code{\link[base]{is.nan}}(). } \author{Henrik Bengtsson} \keyword{array} \keyword{iteration} \keyword{robust} \keyword{univar} matrixStats/man/binMeans.Rd0000644000175100001440000000545012776330571015430 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % binMeans.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{binMeans} \alias{binMeans} \title{Fast mean calculations in non-overlapping bins} \usage{ binMeans(y, x, idxs=NULL, bx, na.rm=TRUE, count=TRUE, right=FALSE, ...) } \description{ Computes the sample means in non-overlapping bins } \arguments{ \item{y}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of K values to calculate means on.} \item{x}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of K positions for to be binned.} \item{idxs}{A \code{\link[base]{vector}} indicating subset of elements to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{bx}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of B+1 ordered positions specifying the B > 0 bins \code{[bx[1],bx[2])}, \code{[bx[2],bx[3])}, ..., \code{[bx[B],bx[B+1])}.} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, missing values in \code{y} are dropped before calculating the mean, otherwise not.} \item{count}{If \code{\link[base:logical]{TRUE}}, the number of data points in each bins is returned as attribute \code{count}, which is an \code{\link[base]{integer}} \code{\link[base]{vector}} of length B.} \item{right}{If \code{\link[base:logical]{TRUE}}, the bins are right-closed (left open), otherwise left-closed (right open).} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length B. } \details{ \code{binMeans(x, bx, right=TRUE)} gives equivalent results as \code{rev(binMeans(-x, bx=sort(-bx), right=FALSE))}, but is faster. } \section{Missing and non-finite values}{ Data points where either of \code{y} and \code{x} is missing are dropped (and therefore are also not counted). Non-finite values in \code{y} are not allowed and gives an error. Missing values in \code{bx} are not allowed and gives an error. } \section{Empty bins}{ Empty bins will get value \code{\link[base:is.finite]{NaN}}. } \examples{ x <- 1:200 mu <- double(length(x)) mu[1:50] <- 5 mu[101:150] <- -5 y <- mu + rnorm(length(x)) # Binning bx <- c(0,50,100,150,200)+0.5 yS <- binMeans(y, x=x, bx=bx) plot(x,y) for (kk in seq_along(yS)) { lines(bx[c(kk,kk+1)], yS[c(kk,kk)], col="blue", lwd=2) } } \seealso{ \code{\link{binCounts}}(). \code{\link[stats]{aggregate}} and \code{\link[base]{mean}}(). } \references{ [1] R-devel thread \emph{Fastest non-overlapping binning mean function out there?} on Oct 3, 2012\cr } \author{ Henrik Bengtsson with initial code contributions by Martin Morgan [1]. } \keyword{univar} matrixStats/man/anyMissing.Rd0000644000175100001440000000322312776330571016011 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % anyMissing.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{anyMissing} \alias{anyMissing} \alias{colAnyMissings} \alias{rowAnyMissings} \title{Checks if there are any missing values in an object or not} \description{ Checks if there are any missing values in an object or not. } \usage{ anyMissing(x, idxs=NULL, ...) colAnyMissings(x, rows=NULL, cols=NULL, ...) rowAnyMissings(x, rows=NULL, cols=NULL, ...) } \arguments{ \item{x}{A \code{\link[base]{vector}}, a \code{\link[base]{list}}, a \code{\link[base]{matrix}}, a \code{\link[base]{data.frame}}, or \code{\link[base]{NULL}}.} \item{idxs, rows, cols}{A \code{\link[base]{vector}} indicating subset of elements (or rows and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{...}{Not used.} } \value{ Returns \code{\link[base:logical]{TRUE}} if a missing value was detected, otherwise \code{\link[base:logical]{FALSE}}. } \details{ The implementation of this method is optimized for both speed and memory. The method will return \code{\link[base:logical]{TRUE}} as soon as a missing value is detected. } \examples{ x <- rnorm(n=1000) x[seq(300,length(x),by=100)] <- NA stopifnot(anyMissing(x) == any(is.na(x))) } \author{Henrik Bengtsson} \seealso{ Starting with R v3.1.0, there is \code{anyNA()} in the \pkg{base}, which provides the same functionality as \code{anyMissing()}. } \keyword{iteration} \keyword{logic} matrixStats/man/logSumExp.Rd0000644000175100001440000000701412776330571015615 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % logSumExp.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{logSumExp} \alias{logSumExp} \title{Accurately computes the logarithm of the sum of exponentials} \description{ Accurately computes the logarithm of the sum of exponentials, that is, \eqn{log(sum(exp(lx)))}. If \eqn{lx = log(x)}, then this is equivalently to calculating \eqn{log(sum(x))}. This function, which avoid numerical underflow, is often used when computing the logarithm of the sum of small numbers (\eqn{|x| << 1}) such as probabilities. } \usage{ logSumExp(lx, idxs=NULL, na.rm=FALSE, ...) } \arguments{ \item{lx}{A \code{\link[base]{numeric}} \code{\link[base]{vector}}. Typically \code{lx} are \eqn{log(x)} values.} \item{idxs}{A \code{\link[base]{vector}} indicating subset of elements to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, any missing values are ignored, otherwise not.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} scalar. } \details{ This is function is more accurate than \code{log(sum(exp(lx)))} when the values of \eqn{x = exp(lx)} are \eqn{|x| << 1}. The implementation of this function is based on the observation that \deqn{ log(a + b) = [ la = log(a), lb = log(b) ] = log( exp(la) + exp(lb) ) = la + log ( 1 + exp(lb - la) ) } Assuming \eqn{la > lb}, then \eqn{|lb - la| < |lb|}, and it is less likely that the computation of \eqn{1 + exp(lb - la)} will not underflow/overflow numerically. Because of this, the overall result from this function should be more accurate. Analogously to this, the implementation of this function finds the maximum value of \code{lx} and subtracts it from the remaining values in \code{lx}. } \section{Benchmarking}{ This method is optimized for correctness, that avoiding underflowing. It is implemented in native code that is optimized for speed and memory. } \examples{ ## EXAMPLE #1 lx <- c(1000.01, 1000.02) y0 <- log(sum(exp(lx))) print(y0) ## Inf y1 <- logSumExp(lx) print(y1) ## 1000.708 ## EXAMPLE #2 lx <- c(-1000.01, -1000.02) y0 <- log(sum(exp(lx))) print(y0) ## -Inf y1 <- logSumExp(lx) print(y1) ## -999.3218 ## EXAMPLE #3 ## R-help thread 'Beyond double-precision?' on May 9, 2009. set.seed(1) x <- runif(50) ## The logarithm of the harmonic mean y0 <- log(1/mean(1/x)) print(y0) ## -1.600885 lx <- log(x) y1 <- log(length(x)) - logSumExp(-lx) print(y1) ## [1] -1.600885 # Sanity check stopifnot(all.equal(y1, y0)) } \author{Henrik Bengtsson} \seealso{ To compute this function on rows or columns of a matrix, see \code{\link{rowLogSumExps}}(). For adding \emph{two} double values in native code, R provides the C function \code{logspace_add()} [1]. For properties of the log-sum-exponential function, see [2]. } \references{ [1] R Core Team, \emph{Writing R Extensions}, v3.0.0, April 2013. \cr [2] Laurent El Ghaoui, \emph{Hyper-Textbook: Optimization Models and Applications}, University of California at Berkeley, August 2012. (Chapter 'Log-Sum-Exp (LSE) Function and Properties', \url{http://inst.eecs.berkeley.edu/~ee127a/book/login/def_lse_fcn.html}) \cr [3] R-help thread \emph{logsumexp function in R}, 2011-02-17. \url{https://stat.ethz.ch/pipermail/r-help/2011-February/269205.html}\cr } matrixStats/man/varDiff.Rd0000644000175100001440000000734112776330572015257 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % varDiff.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{varDiff} \alias{varDiff} \alias{sdDiff} \alias{madDiff} \alias{iqrDiff} \alias{colVarDiffs} \alias{rowVarDiffs} \alias{colSdDiffs} \alias{rowSdDiffs} \alias{colMadDiffs} \alias{rowMadDiffs} \alias{colIQRDiffs} \alias{rowIQRDiffs} \title{Estimation of scale based on sequential-order differences} \description{ Estimation of scale based on sequential-order differences, corresponding to the scale estimates provided by \code{\link[stats]{var}}, \code{\link[stats]{sd}}, \code{\link[stats]{mad}} and \code{\link[stats]{IQR}}. } \usage{ varDiff(x, idxs=NULL, na.rm=FALSE, diff=1L, trim=0, ...) colVarDiffs(x, rows=NULL, cols=NULL, na.rm=FALSE, diff=1L, trim=0, ...) rowVarDiffs(x, rows=NULL, cols=NULL, na.rm=FALSE, diff=1L, trim=0, ...) sdDiff(x, idxs=NULL, na.rm=FALSE, diff=1L, trim=0, ...) colSdDiffs(x, rows=NULL, cols=NULL, na.rm=FALSE, diff=1L, trim=0, ...) rowSdDiffs(x, rows=NULL, cols=NULL, na.rm=FALSE, diff=1L, trim=0, ...) madDiff(x, idxs=NULL, na.rm=FALSE, diff=1L, trim=0, constant=1.4826, ...) colMadDiffs(x, rows=NULL, cols=NULL, na.rm=FALSE, diff=1L, trim=0, ...) rowMadDiffs(x, rows=NULL, cols=NULL, na.rm=FALSE, diff=1L, trim=0, ...) iqrDiff(x, idxs=NULL, na.rm=FALSE, diff=1L, trim=0, ...) colIQRDiffs(x, rows=NULL, cols=NULL, na.rm=FALSE, diff=1L, trim=0, ...) rowIQRDiffs(x, rows=NULL, cols=NULL, na.rm=FALSE, diff=1L, trim=0, ...) } \arguments{ \item{x}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N or a \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}.} \item{idxs, rows, cols}{A \code{\link[base]{vector}} indicating subset of elements (or rows and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, \code{\link[base]{NA}}s are excluded, otherwise not.} \item{diff}{The positional distance of elements for which the difference should be calculated.} \item{trim}{A \code{\link[base]{double}} in [0,1/2] specifying the fraction of observations to be trimmed from each end of (sorted) \code{x} before estimation.} \item{constant}{A scale factor adjusting for asymptotically normal consistency.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length 1, length N, or length K. } \author{Henrik Bengtsson} \seealso{ For the corresponding non-differentiated estimates, see \code{\link[stats]{var}}, \code{\link[stats]{sd}}, \code{\link[stats]{mad}} and \code{\link[stats]{IQR}}. Internally, \code{\link{diff2}}() is used which is a faster version of \code{\link[base]{diff}}(). } \details{ Note that n-order difference MAD estimates, just like the ordinary MAD estimate by \code{\link[stats]{mad}}, apply a correction factor such that the estimates are consistent with the standard deviation under Gaussian distributions. The interquartile range (IQR) estimates does \emph{not} apply such a correction factor. If asymptotically normal consistency is wanted, the correction factor for IQR estimate is \code{1 / (2 * qnorm(3/4))}, which is half of that used for MAD estimates, which is \code{1 / qnorm(3/4)}. This correction factor needs to be applied manually, i.e. there is no \code{constant} argument for the IQR functions. } \references{ [1] J. von Neumann et al., \emph{The mean square successive difference}. Annals of Mathematical Statistics, 1941, 12, 153-162.\cr } \keyword{iteration} \keyword{robust} \keyword{univar} matrixStats/man/weightedVar.Rd0000644000175100001440000000553012776330572016145 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % weightedVar.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{weightedVar} \alias{weightedVar} \alias{weightedSd} \alias{colWeightedVars} \alias{rowWeightedVars} \alias{colWeightedSds} \alias{rowWeightedSds} \title{Weighted variance and weighted standard deviation} \usage{ weightedVar(x, w=NULL, idxs=NULL, na.rm=FALSE, center=NULL, ...) colWeightedVars(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) rowWeightedVars(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) weightedSd(...) colWeightedSds(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) rowWeightedSds(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) } \description{ Computes a weighted variance / standard deviation of a numeric vector or across rows or columns of a matrix. } \arguments{ \item{x}{a \code{\link[base]{numeric}} \code{\link[base]{vector}} containing the values whose weighted variance is to be computed.} \item{w}{a vector of weights the same length as \code{x} giving the weights to use for each element of \code{x}. Negative weights are treated as zero weights. Default value is equal weight to all values.} \item{idxs, rows, cols}{A \code{\link[base]{vector}} indicating subset of elements (or rows and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{na.rm}{a logical value indicating whether \code{\link[base]{NA}} values in \code{x} should be stripped before the computation proceeds, or not. If \code{\link[base]{NA}}, no check at all for \code{\link[base]{NA}}s is done. Default value is \code{\link[base]{NA}} (for efficiency).} \item{center}{Optional \code{\link[base]{numeric}} scalar specifying the center location of the data. If \code{\link[base]{NULL}}, it is estimated from data.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} scalar. } \section{Missing values}{ Missing values are dropped at the very beginning, if argument \code{na.rm} is \code{\link[base:logical]{TRUE}}, otherwise not. } \section{Weighted variance}{ The weights used by the weighted variance (and standard deviation) estimator should be consider so called \emph{frequency weights} such that \code{weightedVar(c(2,4,5), w=c(2,1,3)) == var(c(2, 2, 4, 5, 5, 5))}. Note that this means that the estimate is \emph{not} invariant to a scale factor on the weights, e.g. passing normalized weights will not give the same estimate as non-normalized weights. } \seealso{ For the non-weighted variance, see \code{\link[stats]{var}}. } \author{Henrik Bengtsson} \keyword{univar} \keyword{robust} matrixStats/man/rowDiffs.Rd0000644000175100001440000000261212776330571015454 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowDiffs.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowDiffs} \alias{rowDiffs} \alias{colDiffs} \title{Calculates difference for each row (column) in a matrix} \description{ Calculates difference for each row (column) in a matrix. } \usage{ rowDiffs(x, rows=NULL, cols=NULL, lag=1L, differences=1L, ...) colDiffs(x, rows=NULL, cols=NULL, lag=1L, differences=1L, ...) } \arguments{ \item{x}{A \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}.} \item{rows, cols}{A \code{\link[base]{vector}} indicating subset of rows (and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{lag}{An \code{\link[base]{integer}} specifying the lag.} \item{differences}{An \code{\link[base]{integer}} specifying the order of difference.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} Nx(K-1) or (N-1)xK \code{\link[base]{matrix}}. } \examples{ x <- matrix(1:27, ncol=3) d1 <- rowDiffs(x) print(d1) d2 <- t(colDiffs(t(x))) stopifnot(all.equal(d2, d1)) } \author{Henrik Bengtsson} \seealso{ See also \code{\link{diff2}}(). } \keyword{array} \keyword{iteration} \keyword{robust} \keyword{univar} matrixStats/man/diff2.Rd0000644000175100001440000000216712776330571014670 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % diff2.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{diff2} \alias{diff2} \title{Fast lagged differences} \usage{ diff2(x, idxs=NULL, lag=1L, differences=1L, ...) } \description{ Computes the lagged and iterated differences. } \arguments{ \item{x}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N.} \item{idxs}{A \code{\link[base]{vector}} indicating subset of elements to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{lag}{An \code{\link[base]{integer}} specifying the lag.} \item{differences}{An \code{\link[base]{integer}} specifying the order of difference.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N - \code{differences}. } \examples{ diff2(1:10) } \seealso{ \code{\link[base]{diff}}(). } \author{Henrik Bengtsson} \keyword{univar} \keyword{internal} matrixStats/man/validateIndices.Rd0000644000175100001440000000200012776330572016751 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % validateIndices.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{validateIndices} \alias{validateIndices} \title{Validate indices} \description{ Computes validated positive indices from given indices. } \arguments{ \item{idxs}{A \code{\link[base]{integer}} \code{\link[base]{vector}}. If \code{\link[base]{NULL}}, all indices are considered.} \item{maxIdx}{The possible max index.} \item{allowOutOfBound}{Allow positive out of bound to indicate \code{\link[base]{NA}}.} } \usage{ validateIndices(idxs=NULL, maxIdx, allowOutOfBound=TRUE) } \value{ Returns a validated integers list indicating the indices. } \examples{ idxs <- validateIndices(c(-4, 0, -3, -1), 5) # [2,5] idxs <- validateIndices(c(4, 4, 8, 2, 3), 8) # [4, 4, 8, 2, 3] } \keyword{internal} matrixStats/man/rowMedians.Rd0000644000175100001440000000413512776330571016003 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowMedians.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowMedians} \alias{rowMedians} \alias{colMedians} \title{Calculates the median for each row (column) in a matrix} \description{ Calculates the median for each row (column) in a matrix. } \usage{ rowMedians(x, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(x), ...) colMedians(x, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(x), ...) } \arguments{ \item{x}{A \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}.} \item{rows, cols}{A \code{\link[base]{vector}} indicating subset of rows (and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, \code{\link[base]{NA}}s are excluded first, otherwise not.} \item{dim.}{An \code{\link[base]{integer}} \code{\link[base]{vector}} of length two specifying the dimension of \code{x}, also when not a \code{\link[base]{matrix}}.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N (K). } \details{ The implementation of \code{rowMedians()} and \code{colMedians()} is optimized for both speed and memory. To avoid coercing to \code{\link[base]{double}}s (and hence memory allocation), there is a special implementation for \code{\link[base]{integer}} matrices. That is, if \code{x} is an \code{\link[base]{integer}} \code{\link[base]{matrix}}, then \code{rowMedians(as.double(x))} (\code{rowMedians(as.double(x))}) would require three times the memory of \code{rowMedians(x)} (\code{colMedians(x)}), but all this is avoided. } \author{Henrik Bengtsson, Harris Jaffee} \seealso{ See \code{\link{rowMedians}}() and \code{colMedians()} for weighted medians. For mean estimates, see \code{rowMeans()} in \code{\link[base]{colSums}}(). } \keyword{array} \keyword{iteration} \keyword{robust} \keyword{univar} matrixStats/man/meanOver.Rd0000644000175100001440000000473012776330571015450 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % meanOver.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{meanOver} \alias{meanOver} \title{Fast averaging over subset of vector elements} \usage{ meanOver(x, idxs=NULL, na.rm=FALSE, refine=TRUE, ...) } \description{ Computes the sample mean of all or a subset of values. } \arguments{ \item{x}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N.} \item{idxs}{A \code{\link[base]{vector}} indicating subset of elements to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, missing values are skipped, otherwise not.} \item{refine}{If \code{\link[base:logical]{TRUE}} and \code{x} is \code{\link[base]{numeric}}, then extra effort is used to calculate the average with greater numerical precision, otherwise not.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} scalar. } \details{ \code{meanOver(x, idxs)} gives equivalent results as \code{mean(x[idxs])}, but is faster and more memory efficient since it avoids the actual subsetting which requires copying of elements and garbage collection thereof. If \code{x} is \code{\link[base]{numeric}} and \code{refine=TRUE}, then a two-pass scan is used to calculate the average. The first scan calculates the total sum and divides by the number of (non-missing) values. In the second scan, this average is refined by adding the residuals towards the first average. The \code{\link[base]{mean}}() uses this approach. \code{meanOver(..., refine=FALSE)} is almost twice as fast as \code{meanOver(..., refine=TRUE)}. } \examples{ x <- 1:10 n <- length(x) idxs <- seq(from=1, to=n, by=2) s1 <- mean(x[idxs]) # 25 s2 <- meanOver(x, idxs=idxs) # 25 stopifnot(identical(s1, s2)) idxs <- seq(from=n, to=1, by=-2) s1 <- mean(x[idxs]) # 25 s2 <- meanOver(x, idxs=idxs) # 25 stopifnot(identical(s1, s2)) s1 <- mean(x) # 55 s2 <- meanOver(x) # 55 stopifnot(identical(s1, s2)) } \seealso{ \code{\link[base]{mean}}(). To efficiently sum over a subset, see \code{\link{sumOver}}(). } \author{Henrik Bengtsson} \keyword{univar} \keyword{internal} matrixStats/man/weightedMean.Rd0000644000175100001440000000636412776330572016303 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % weightedMean.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{weightedMean} \alias{weightedMean} \encoding{latin1} \title{Weighted Arithmetic Mean} \usage{ weightedMean(x, w=NULL, idxs=NULL, na.rm=FALSE, refine=FALSE, ...) } \description{ Computes the weighted sample mean of a numeric vector. } \arguments{ \item{x}{a \code{\link[base]{numeric}} \code{\link[base]{vector}} containing the values whose weighted mean is to be computed.} \item{w}{a vector of weights the same length as \code{x} giving the weights to use for each element of \code{x}. Negative weights are treated as zero weights. Default value is equal weight to all values.} \item{idxs}{A \code{\link[base]{vector}} indicating subset of elements to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{na.rm}{a logical value indicating whether \code{\link[base]{NA}} values in \code{x} should be stripped before the computation proceeds, or not. If \code{\link[base]{NA}}, no check at all for \code{\link[base]{NA}}s is done. Default value is \code{\link[base]{NA}} (for efficiency).} \item{refine}{If \code{\link[base:logical]{TRUE}} and \code{x} is \code{\link[base]{numeric}}, then extra effort is used to calculate the average with greater numerical precision, otherwise not.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} scalar. If \code{x} is of zero length, then \code{NaN} is returned, which is consistent with \code{\link[base]{mean}}(). } \examples{ x <- 1:10 n <- length(x) w <- rep(1, times=n) m0 <- weighted.mean(x, w) m1 <- weightedMean(x, w) stopifnot(identical(m1,m0)) # Pull the mean towards zero w[1] <- 5 m0 <- weighted.mean(x, w) m1 <- weightedMean(x, w) stopifnot(identical(m1,m0)) # Put even more weight on the zero w[1] <- 8.5 m0 <- weighted.mean(x, w) m1 <- weightedMean(x, w) stopifnot(identical(m1,m0)) # All weight on the first value w[1] <- Inf m0 <- weighted.mean(x, w) m1 <- weightedMean(x, w) stopifnot(identical(m1,m0)) # All weight on the last value w[1] <- 1 w[n] <- Inf m0 <- weighted.mean(x, w) m1 <- weightedMean(x, w) stopifnot(identical(m1,m0)) # All weights set to zero w <- rep(0, n) m0 <- weighted.mean(x, w) m1 <- weightedMean(x, w) stopifnot(identical(m1,m0)) } \section{Missing values}{ This function handles missing values consistently \code{\link[stats]{weighted.mean}}. More precisely, if \code{na.rm=FALSE}, then any missing values in either \code{x} or \code{w} will give result \code{NA_real_}. If \code{na.rm=TRUE}, then all \code{(x,w)} data points for which \code{x} is missing are skipped. Note that if both \code{x} and \code{w} are missing for a data points, then it is also skipped (by the same rule). However, if only \code{w} is missing, then the final results will always be \code{NA_real_} regardless of \code{na.rm}. } \seealso{ \code{\link[base]{mean}}() and \code{\link[stats]{weighted.mean}}. } \author{Henrik Bengtsson} \keyword{univar} \keyword{robust} matrixStats/man/rowQuantiles.Rd0000644000175100001440000000426512776330571016374 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowQuantiles.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowQuantiles} \alias{rowQuantiles} \alias{colQuantiles} \title{Estimates quantiles for each row (column) in a matrix} \description{ Estimates quantiles for each row (column) in a matrix. } \usage{ rowQuantiles(x, rows=NULL, cols=NULL, probs=seq(from = 0, to = 1, by = 0.25), na.rm=FALSE, type=7L, ..., drop=TRUE) colQuantiles(x, rows=NULL, cols=NULL, probs=seq(from = 0, to = 1, by = 0.25), na.rm=FALSE, type=7L, ..., drop=TRUE) } \arguments{ \item{x}{A \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}} with N >= 0.} \item{rows, cols}{A \code{\link[base]{vector}} indicating subset of rows (and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{probs}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of J probabilities in [0,1].} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, \code{\link[base]{NA}}s are excluded first, otherwise not.} \item{type}{An \code{\link[base]{integer}} specify the type of estimator. See \code{\link[stats]{quantile}} for more details.} \item{...}{Additional arguments passed to \code{\link[stats]{quantile}}.} \item{drop}{If TRUE, singleton dimensions in the result are dropped, otherwise not.} } \value{ Returns a \code{\link[base]{numeric}} NxJ (KxJ) \code{\link[base]{matrix}}, where N (K) is the number of rows (columns) for which the J quantiles are calculated. } \examples{ set.seed(1) x <- matrix(rnorm(50*40), nrow=50, ncol=40) str(x) probs <- c(0.25,0.5,0.75) # Row quantiles q <- rowQuantiles(x, probs=probs) print(q) q0 <- apply(x, MARGIN=1, FUN=quantile, probs=probs) stopifnot(all.equal(q0, t(q))) # Column IQRs q <- colQuantiles(x, probs=probs) print(q) q0 <- apply(x, MARGIN=2, FUN=quantile, probs=probs) stopifnot(all.equal(q0, t(q))) } \author{Henrik Bengtsson} \seealso{ \code{\link[stats]{quantile}}. } \keyword{array} \keyword{iteration} \keyword{robust} \keyword{univar} matrixStats/man/allocMatrix.Rd0000644000175100001440000000244012776330571016147 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % allocMatrix.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{allocMatrix} \alias{allocMatrix} \alias{allocVector} \alias{allocArray} \title{Allocates an empty vector, matrix or array} \usage{ allocVector(length, value=0, ...) allocMatrix(nrow, ncol, value=0, ...) allocArray(dim, value=0, ...) } \description{ Allocates an empty vector, matrix or array faster than the corresponding function in R. } \arguments{ \item{length, nrow, ncol, dim}{\code{\link[base]{numeric}}s specifying the dimension of the created \code{\link[base]{vector}}, \code{\link[base]{matrix}} or \code{\link[base]{array}}.} \item{value}{A \code{\link[base]{numeric}} scalar that all elements will have as value.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{vector}}, \code{\link[base]{matrix}} and \code{\link[base]{array}} respectively of the same data type as \code{value}. } \author{Henrik Bengtsson} \seealso{ See also \code{\link[base]{vector}}, \code{\link[base]{matrix}} and \code{\link[base]{array}}. } \keyword{programming} \keyword{internal} matrixStats/man/rowTabulates.Rd0000644000175100001440000000300012776330571016335 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowTabulates.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowTabulates} \alias{rowTabulates} \alias{colTabulates} \title{Tabulates the values in a matrix by row (column)} \description{ Tabulates the values in a matrix by row (column). } \usage{ rowTabulates(x, rows=NULL, cols=NULL, values=NULL, ...) colTabulates(x, rows=NULL, cols=NULL, values=NULL, ...) } \arguments{ \item{x}{An \code{\link[base]{integer}} or \code{\link[base]{raw}} NxK \code{\link[base]{matrix}}.} \item{rows, cols}{A \code{\link[base]{vector}} indicating subset of rows (and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{values}{An \code{\link[base]{vector}} of J values of count. If \code{\link[base]{NULL}}, all (unique) values are counted.} \item{...}{Not used.} } \value{ Returns a NxJ (KxJ) \code{\link[base]{matrix}} where N (K) is the number of row (column) \code{\link[base]{vector}}s tabulated and J is the number of values counted. } \examples{ x <- matrix(1:5, nrow=10, ncol=5) print(x) print(rowTabulates(x)) print(colTabulates(x)) # Count only certain values print(rowTabulates(x, values=1:3)) y <- as.raw(x) dim(y) <- dim(x) print(y) print(rowTabulates(y)) print(colTabulates(y)) } \author{Henrik Bengtsson} \keyword{utilities} matrixStats/man/rowVars.Rd0000644000175100001440000000511012776330571015330 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowVars.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowVars} \alias{rowVars} \alias{rowVars} \alias{colVars} \alias{rowVars,matrix-method} \alias{colVars,matrix-method} \title{Variance estimates for each row (column) in a matrix} \description{ Variance estimates for each row (column) in a matrix. } \usage{ rowVars(x, rows=NULL, cols=NULL, na.rm=FALSE, center=NULL, dim.=dim(x), ...) colVars(x, rows=NULL, cols=NULL, na.rm=FALSE, center=NULL, dim.=dim(x), ...) } \arguments{ \item{x}{A \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}.} \item{rows, cols}{A \code{\link[base]{vector}} indicating subset of rows (and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{center}{(optional) The center, defaults to the row means.} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, \code{\link[base]{NA}}s are excluded first, otherwise not.} \item{dim.}{An \code{\link[base]{integer}} \code{\link[base]{vector}} of length two specifying the dimension of \code{x}, also when not a \code{\link[base]{matrix}}.} \item{...}{Additional arguments passed to \code{rowMeans()} and \code{rowSums()}.} } \value{ Returns a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N (K). } \examples{ set.seed(1) x <- matrix(rnorm(20), nrow=5, ncol=4) print(x) # Row averages print(rowMeans(x)) print(rowMedians(x)) # Column averages print(colMeans(x)) print(colMedians(x)) # Row variabilities print(rowVars(x)) print(rowSds(x)) print(rowMads(x)) print(rowIQRs(x)) # Column variabilities print(rowVars(x)) print(colSds(x)) print(colMads(x)) print(colIQRs(x)) # Row ranges print(rowRanges(x)) print(cbind(rowMins(x), rowMaxs(x))) print(cbind(rowOrderStats(x, which=1), rowOrderStats(x, which=ncol(x)))) # Column ranges print(colRanges(x)) print(cbind(colMins(x), colMaxs(x))) print(cbind(colOrderStats(x, which=1), colOrderStats(x, which=nrow(x)))) x <- matrix(rnorm(2400), nrow=50, ncol=40) # Row standard deviations d <- rowDiffs(x) s1 <- rowSds(d)/sqrt(2) s2 <- rowSds(x) print(summary(s1-s2)) # Column standard deviations d <- colDiffs(x) s1 <- colSds(d)/sqrt(2) s2 <- colSds(x) print(summary(s1-s2)) } \author{Henrik Bengtsson} \seealso{ See \code{rowMeans()} and \code{rowSums()} in \code{\link[base]{colSums}}(). } \keyword{array} \keyword{iteration} \keyword{robust} \keyword{univar} matrixStats/man/rowWeightedMeans.Rd0000644000175100001440000000466612776330571017160 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowWeightedMeans.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowWeightedMeans} \alias{rowWeightedMeans} \alias{colWeightedMeans} \title{Calculates the weighted means for each row (column) in a matrix} \description{ Calculates the weighted means for each row (column) in a matrix. } \usage{ rowWeightedMeans(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) colWeightedMeans(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) } \arguments{ \item{x}{A \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}.} \item{w}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of length K (N).} \item{rows, cols}{A \code{\link[base]{vector}} indicating subset of rows (and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, missing values are excluded from the calculation, otherwise not.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N (K). } \details{ The implementations of these methods are optimized for both speed and memory. If no weights are given, the corresponding \code{rowMeans()}/\code{colMeans()} is used. } \examples{ x <- matrix(rnorm(20), nrow=5, ncol=4) print(x) # Non-weighted row averages xM0 <- rowMeans(x) xM <- rowWeightedMeans(x) stopifnot(all.equal(xM, xM0)) # Weighted row averages (uniform weights) w <- rep(2.5, ncol(x)) xM <- rowWeightedMeans(x, w=w) stopifnot(all.equal(xM, xM0)) # Weighted row averages (excluding some columns) w <- c(1,1,0,1) xM0 <- rowMeans(x[,(w == 1),drop=FALSE]); xM <- rowWeightedMeans(x, w=w) stopifnot(all.equal(xM, xM0)) # Weighted row averages (excluding some columns) w <- c(0,1,0,0) xM0 <- rowMeans(x[,(w == 1),drop=FALSE]); xM <- rowWeightedMeans(x, w=w) stopifnot(all.equal(xM, xM0)) # Weighted averages by rows and columns w <- 1:4 xM1 <- rowWeightedMeans(x, w=w) xM2 <- colWeightedMeans(t(x), w=w) stopifnot(all.equal(xM2, xM1)) } \author{Henrik Bengtsson} \seealso{ See \code{rowMeans()} and \code{colMeans()} in \code{\link[base]{colSums}}() for non-weighted means. See also \code{\link[stats]{weighted.mean}}. } \keyword{array} \keyword{iteration} \keyword{robust} \keyword{univar} matrixStats/man/signTabulate.Rd0000644000175100001440000000222412776330572016313 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % signTabulate.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{signTabulate} \alias{signTabulate} \alias{signTabulate} \title{Calculates the number of negative, zero, positive and missing values} \usage{ signTabulate(x, idxs=NULL, ...) } \description{ Calculates the number of negative, zero, positive and missing values in a \code{\link[base]{numeric}} vector. For \code{\link[base]{double}} vectors, the number of negative and positive infinite values are also counted. } \arguments{ \item{x}{a \code{\link[base]{numeric}} \code{\link[base]{vector}}.} \item{idxs}{A \code{\link[base]{vector}} indicating subset of elements to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{name}}d \code{\link[base]{numeric}} \code{\link[base]{vector}}. } \seealso{ \code{\link[base]{sign}}(). } \author{Henrik Bengtsson} \keyword{internal} matrixStats/man/rowSds.Rd0000644000175100001440000000422212776330571015151 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowSds.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowSds} \alias{rowSds} \alias{rowSds} \alias{colSds} \alias{rowMads} \alias{colMads} \alias{rowSds,matrix-method} \alias{colSds,matrix-method} \title{Standard deviation estimates for each row (column) in a matrix} \description{ Standard deviation estimates for each row (column) in a matrix. } \usage{ rowSds(x, rows=NULL, cols=NULL, ...) colSds(x, rows=NULL, cols=NULL, ...) rowMads(x, rows=NULL, cols=NULL, center=NULL, constant=1.4826, na.rm=FALSE, dim.=dim(x), centers=NULL, ...) colMads(x, rows=NULL, cols=NULL, center=NULL, constant=1.4826, na.rm=FALSE, dim.=dim(x), centers=NULL, ...) } \arguments{ \item{x}{A \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}.} \item{rows, cols}{A \code{\link[base]{vector}} indicating subset of rows (and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{center}{A optional \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N (K) with centers. By default, they are calculated using \code{\link{rowMedians}}().} \item{constant}{A scale factor. See \code{\link[stats]{mad}} for details.} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, missing values are removed first, otherwise not.} \item{dim.}{An \code{\link[base]{integer}} \code{\link[base]{vector}} of length two specifying the dimension of \code{x}, also when not a \code{\link[base]{matrix}}.} \item{...}{Additional arguments passed to \code{\link{rowVars}}() and \code{\link{rowMedians}}(), respectively.} \item{centers}{(deprectated) use \code{center} instead.} } \value{ Returns a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N (K). } \author{Henrik Bengtsson} \seealso{ \code{\link[stats]{sd}}, \code{\link[stats]{mad}} and \code{\link[stats:cor]{var}}. \code{\link{rowIQRs}}(). } \keyword{array} \keyword{iteration} \keyword{robust} \keyword{univar} matrixStats/man/rowAvgsPerColSet.Rd0000644000175100001440000001007312776330571017102 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowAvgsPerColSet.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowAvgsPerColSet} \alias{rowAvgsPerColSet} \alias{colAvgsPerRowSet} \title{Applies a row-by-row (column-by-column) averaging function to equally-sized subsets of matrix columns (rows)} \description{ Applies a row-by-row (column-by-column) averaging function to equally-sized subsets of matrix columns (rows). Each subset is averaged independently of the others. } \usage{ rowAvgsPerColSet(X, W=NULL, rows=NULL, S, FUN=rowMeans, ..., tFUN=FALSE) colAvgsPerRowSet(X, W=NULL, cols=NULL, S, FUN=colMeans, tFUN=FALSE, ...) } \arguments{ \item{X}{A \code{\link[base]{numeric}} NxM \code{\link[base]{matrix}}.} \item{W}{An optional \code{\link[base]{numeric}} NxM \code{\link[base]{matrix}} of weights.} \item{rows, cols}{A \code{\link[base]{vector}} indicating subset of rows (and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{S}{An \code{\link[base]{integer}} KxJ \code{\link[base]{matrix}} specifying the J subsets. Each column holds K column (row) indices for the corresponding subset.} \item{FUN}{The row-by-row (column-by-column) \code{\link[base]{function}} used to average over each subset of \code{X}. This function must accept a \code{\link[base]{numeric}} NxK (KxM) \code{\link[base]{matrix}} and the \code{\link[base]{logical}} argument \code{na.rm} (which is automatically set), and return a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N (M).} \item{...}{Additional arguments passed to then \code{FUN} \code{\link[base]{function}}.} \item{tFUN}{If \code{\link[base:logical]{TRUE}}, the NxK (KxM) \code{\link[base]{matrix}} passed to \code{FUN()} is transposed first.} } \value{ Returns a \code{\link[base]{numeric}} JxN (MxJ) \code{\link[base]{matrix}}, where row names equal \code{rownames(X)} (\code{colnames(S)}) and column names \code{colnames(S)} (\code{colnames(X)}). } \details{ If argument \code{S} is a single column vector with indices \code{1:N}, then \code{rowAvgsPerColSet(X, S=S, FUN=rowMeans)} gives the same result as \code{rowMeans(X)}. Analogously, for \code{rowAvgsPerColSet()}. } \examples{ X <- matrix(rnorm(20*6), nrow=20, ncol=6) rownames(X) <- LETTERS[1:nrow(X)] colnames(X) <- letters[1:ncol(X)] print(X) # - - - - - - - - - - - - - - - - - - - - - - - - - - # Apply rowMeans() for 3 sets of 2 columns # - - - - - - - - - - - - - - - - - - - - - - - - - - nbrOfSets <- 3 S <- matrix(1:ncol(X), ncol=nbrOfSets) colnames(S) <- sprintf("s\%d", 1:nbrOfSets) print(S) Z <- rowAvgsPerColSet(X, S=S) print(Z) # Validation Z0 <- cbind(s1=rowMeans(X[,1:2]), s2=rowMeans(X[,3:4]), s3=rowMeans(X[,5:6])) stopifnot(identical(drop(Z), Z0)) # - - - - - - - - - - - - - - - - - - - - - - - - - - # Apply colMeans() for 5 sets of 4 rows # - - - - - - - - - - - - - - - - - - - - - - - - - - nbrOfSets <- 5 S <- matrix(1:nrow(X), ncol=nbrOfSets) colnames(S) <- sprintf("s\%d", 1:nbrOfSets) print(S) Z <- colAvgsPerRowSet(X, S=S) print(Z) # Validation Z0 <- rbind(s1=colMeans(X[1:4,]), s2=colMeans(X[5:8,]), s3=colMeans(X[9:12,]), s4=colMeans(X[13:16,]), s5=colMeans(X[17:20,])) stopifnot(identical(drop(Z), Z0)) # - - - - - - - - - - - - - - - - - - - - - - - - - - # When there is only one "complete" set # - - - - - - - - - - - - - - - - - - - - - - - - - - nbrOfSets <- 1 S <- matrix(1:ncol(X), ncol=nbrOfSets) colnames(S) <- sprintf("s\%d", 1:nbrOfSets) print(S) Z <- rowAvgsPerColSet(X, S=S, FUN=rowMeans) print(Z) Z0 <- rowMeans(X) stopifnot(identical(drop(Z), Z0)) nbrOfSets <- 1 S <- matrix(1:nrow(X), ncol=nbrOfSets) colnames(S) <- sprintf("s\%d", 1:nbrOfSets) print(S) Z <- colAvgsPerRowSet(X, S=S, FUN=colMeans) print(Z) Z0 <- colMeans(X) stopifnot(identical(drop(Z), Z0)) } \author{Henrik Bengtsson} \keyword{internal} \keyword{utilities} matrixStats/man/rowCumsums.Rd0000644000175100001440000000430512776330571016056 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowCumsums.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowCumsums} \alias{rowCumsums} \alias{rowCumsums} \alias{colCumsums} \alias{rowCumprods} \alias{colCumprods} \alias{rowCummins} \alias{colCummins} \alias{rowCummaxs} \alias{colCummaxs} \title{Cumulative sums, products, minima and maxima for each row (column) in a matrix} \description{ Cumulative sums, products, minima and maxima for each row (column) in a matrix. } \usage{ rowCumsums(x, rows=NULL, cols=NULL, dim.=dim(x), ...) colCumsums(x, rows=NULL, cols=NULL, dim.=dim(x), ...) rowCumprods(x, rows=NULL, cols=NULL, dim.=dim(x), ...) colCumprods(x, rows=NULL, cols=NULL, dim.=dim(x), ...) rowCummins(x, rows=NULL, cols=NULL, dim.=dim(x), ...) colCummins(x, rows=NULL, cols=NULL, dim.=dim(x), ...) rowCummaxs(x, rows=NULL, cols=NULL, dim.=dim(x), ...) colCummaxs(x, rows=NULL, cols=NULL, dim.=dim(x), ...) } \arguments{ \item{x}{A \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}.} \item{rows, cols}{A \code{\link[base]{vector}} indicating subset of elements (or rows and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{dim.}{An \code{\link[base]{integer}} \code{\link[base]{vector}} of length two specifying the dimension of \code{x}, also when not a \code{\link[base]{matrix}}.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}} of the same mode as \code{x}. } \examples{ x <- matrix(1:12, nrow=4, ncol=3) print(x) yr <- rowCumsums(x) print(yr) yc <- colCumsums(x) print(yc) yr <- rowCumprods(x) print(yr) yc <- colCumprods(x) print(yc) yr <- rowCummaxs(x) print(yr) yc <- colCummaxs(x) print(yc) yr <- rowCummins(x) print(yr) yc <- colCummins(x) print(yc) } \author{Henrik Bengtsson} \seealso{ See \code{\link[base]{cumsum}}(), \code{\link[base]{cumprod}}(), \code{\link[base]{cummin}}(), and \code{\link[base]{cummax}}(). } \keyword{array} \keyword{iteration} \keyword{univar} matrixStats/man/rowRanges.Rd0000644000175100001440000000403212776330571015636 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowRanges.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowRanges} \alias{rowRanges} \alias{colRanges} \alias{rowMins} \alias{rowMaxs} \alias{colMins} \alias{colMaxs} \title{Gets the range of values in each row (column) of a matrix} \description{ Gets the range of values in each row (column) of a matrix. } \usage{ rowRanges(x, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(x), ...) colRanges(x, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(x), ...) rowMins(x, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(x), ...) colMins(x, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(x), ...) rowMaxs(x, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(x), ...) colMaxs(x, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(x), ...) } \arguments{ \item{x}{A \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}.} \item{rows, cols}{A \code{\link[base]{vector}} indicating subset of rows (and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, \code{\link[base]{NA}}s are excluded first, otherwise not.} \item{dim.}{An \code{\link[base]{integer}} \code{\link[base]{vector}} of length two specifying the dimension of \code{x}, also when not a \code{\link[base]{matrix}}.} \item{...}{Not used.} } \value{ \code{rowRanges()} (\code{colRanges()}) returns a \code{\link[base]{numeric}} Nx2 (Kx2) \code{\link[base]{matrix}}, where N (K) is the number of rows (columns) for which the ranges are calculated. \code{rowMins()/rowMaxs()} (\code{colMins()/colMaxs()}) returns a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N (K). } \author{Henrik Bengtsson} \seealso{ \code{\link{rowOrderStats}}() and \code{\link[base]{pmin.int}}(). } \keyword{array} \keyword{iteration} \keyword{robust} \keyword{univar} matrixStats/man/indexByRow.Rd0000644000175100001440000000225712776330571015770 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % indexByRow.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{indexByRow} \alias{indexByRow} \title{Translates matrix indices by rows into indices by columns} \description{ Translates matrix indices by rows into indices by columns. } \usage{ indexByRow(dim, idxs=NULL, ...) } \arguments{ \item{dim}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of length two specifying the length of the "template" matrix.} \item{idxs}{A \code{\link[base]{vector}} of indices. If \code{\link[base]{NULL}}, all indices are returned.} \item{...}{Not use.} } \value{ Returns an \code{\link[base]{integer}} \code{\link[base]{vector}} of indices. } \examples{ dim <- c(5, 4) X <- matrix(NA_integer_, nrow=dim[1], ncol=dim[2]) Y <- t(X) idxs <- seq_along(X) # Assign by columns X[idxs] <- idxs print(X) # Assign by rows Y[indexByRow(dim(Y), idxs)] <- idxs print(Y) stopifnot(X == t(Y)) } \author{Henrik Bengtsson} \keyword{iteration} \keyword{logic} matrixStats/man/rowCounts.Rd0000644000175100001440000000566412776330571015706 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowCounts.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowCounts} \alias{rowCounts} \alias{colCounts} \alias{count} \alias{allValue} \alias{anyValue} \alias{rowAnys} \alias{colAnys} \alias{rowAlls} \alias{colAlls} \title{Counts the number of TRUE values in each row (column) of a matrix} \description{ Counts the number of TRUE values in each row (column) of a matrix. } \usage{ count(x, idxs=NULL, value=TRUE, na.rm=FALSE, ...) rowCounts(x, rows=NULL, cols=NULL, value=TRUE, na.rm=FALSE, dim.=dim(x), ...) colCounts(x, rows=NULL, cols=NULL, value=TRUE, na.rm=FALSE, dim.=dim(x), ...) rowAlls(x, rows=NULL, cols=NULL, value=TRUE, na.rm=FALSE, dim.=dim(x), ...) colAlls(x, rows=NULL, cols=NULL, value=TRUE, na.rm=FALSE, dim.=dim(x), ...) allValue(x, idxs=NULL, value=TRUE, na.rm=FALSE, ...) rowAnys(x, rows=NULL, cols=NULL, value=TRUE, na.rm=FALSE, dim.=dim(x), ...) colAnys(x, rows=NULL, cols=NULL, value=TRUE, na.rm=FALSE, dim.=dim(x), ...) anyValue(x, idxs=NULL, value=TRUE, na.rm=FALSE, ...) } \arguments{ \item{x}{An NxK \code{\link[base]{matrix}} or an N*K \code{\link[base]{vector}}.} \item{idxs, rows, cols}{A \code{\link[base]{vector}} indicating subset of elements (or rows and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{value}{A value to search for.} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, \code{\link[base]{NA}}s are excluded first, otherwise not.} \item{dim.}{An \code{\link[base]{integer}} \code{\link[base]{vector}} of length two specifying the dimension of \code{x}, also when not a \code{\link[base]{matrix}}.} \item{...}{Not used.} } \value{ \code{rowCounts()} (\code{colCounts()}) returns an \code{\link[base]{integer}} \code{\link[base]{vector}} of length N (K). The other methods returns a \code{\link[base]{logical}} \code{\link[base]{vector}} of length N (K). } \details{ These functions takes either a @matrix or a @vector as input. If a @vector, then argument \code{dim} must be specified and fulfill \code{prod(dim) == length(x)}. The result will be identical to the results obtained when passing \code{matrix(x, nrow=dim[1L], ncol=dim[2L])}, but avoids having to temporarily create/allocate a @matrix, if only such is needed only for these calculations. } \examples{ x <- matrix(FALSE, nrow=10, ncol=5) x[3:7,c(2,4)] <- TRUE x[2:4,] <- TRUE x[,1] <- TRUE x[5,] <- FALSE x[,5] <- FALSE print(x) print(rowCounts(x)) # 1 4 4 4 0 3 3 1 1 1 print(colCounts(x)) # 9 5 3 5 0 print(rowAnys(x)) print(which(rowAnys(x))) # 1 2 3 4 6 7 8 9 10 print(colAnys(x)) print(which(colAnys(x))) # 1 2 3 4 } \author{Henrik Bengtsson} \keyword{array} \keyword{logic} \keyword{iteration} \keyword{univar} matrixStats/man/matrixStats-package.Rd0000644000175100001440000000263512776330571017612 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % 999.package.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{matrixStats-package} \alias{matrixStats-package} \alias{matrixStats} \docType{package} \title{Package matrixStats} \description{ High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian(). } \section{Installation}{ To install this package, please do: \preformatted{ install.packages("matrixStats") } } \section{Vignettes}{ For an overview of the package, see the '\href{../doc/index.html}{vignettes}'; \enumerate{ \item Summary of functions. } } \section{How to cite this package}{ Henrik Bengtsson (2016). matrixStats: Functions that Apply to Rows and Columns of Matrices (and to Vectors). R package version 0.51.0. https://github.com/HenrikBengtsson/matrixStats } \author{Henrik Bengtsson, Hector Corrada Bravo, Robert Gentleman, Ola Hossjer, Harris Jaffee, Dongcan Jiang, Peter Langfelder} \keyword{package} matrixStats/man/sumOver.Rd0000644000175100001440000000570512776330572015340 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % sumOver.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{sumOver} \alias{sumOver} \title{Fast sum over subset of vector elements} \usage{ sumOver(x, idxs=NULL, na.rm=FALSE, mode=typeof(x), ...) } \description{ Computes the sum of all or a subset of values. } \arguments{ \item{x}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N.} \item{idxs}{A \code{\link[base]{vector}} indicating subset of elements to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, missing values are skipped, otherwise not.} \item{mode}{A \code{\link[base]{character}} string specifying the data type of the return value. Default is to use the same mode as argument \code{x}.} \item{...}{Not used.} } \value{ Returns a scalar of the data type specified by argument \code{mode}. If \code{mode="integer"}, then integer overflow occurs if the \emph{sum} is outside the range of defined integer values. } \details{ \code{sumOver(x, idxs)} gives equivalent results as \code{sum(x[idxs])}, but is faster and more memory efficient since it avoids the actual subsetting which requires copying of elements and garbage collection thereof. Furthermore, \code{sumOver(x, mode="double")} is equivalent to \code{sum(as.numeric(x))}, but is much more memory efficient when \code{x} is an \code{\link[base]{integer}} vector. } \examples{ x <- 1:10 n <- length(x) idxs <- seq(from=1, to=n, by=2) s1 <- sum(x[idxs]) # 25 s2 <- sumOver(x, idxs=idxs) # 25 stopifnot(identical(s1, s2)) idxs <- seq(from=n, to=1, by=-2) s1 <- sum(x[idxs]) # 25 s2 <- sumOver(x, idxs=idxs) # 25 stopifnot(identical(s1, s2)) s1 <- sum(x) # 55 s2 <- sumOver(x) # 55 stopifnot(identical(s1, s2)) # Total gives integer overflow x <- c(.Machine$integer.max, 1L, -.Machine$integer.max) s1 <- sum(x[1:2]) # NA_integer_ s2 <- sumOver(x[1:2]) # NA_integer_ stopifnot(identical(s1, s2)) # Total gives integer overflow (coerce to numeric) s1 <- sum(as.numeric(x[1:2])) # 2147483648 s2 <- sumOver(as.numeric(x[1:2])) # 2147483648 s3 <- sumOver(x[1:2], mode="double") # 2147483648 stopifnot(identical(s1, s2)) stopifnot(identical(s1, s3)) # Cumulative sum would give integer overflow but not the total s1 <- sum(x) # 1L s2 <- sumOver(x) # 1L stopifnot(identical(s1, s2)) } \seealso{ \code{\link[base]{sum}}(). To efficiently average over a subset, see \code{\link{meanOver}}(). } \author{Henrik Bengtsson} \keyword{univar} \keyword{internal} matrixStats/man/weightedMad.Rd0000644000175100001440000000542112776330572016115 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % weightedMad.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{weightedMad} \alias{weightedMad} \alias{rowWeightedMads} \alias{colWeightedMads} \title{Weighted Median Absolute Deviation (MAD)} \usage{ weightedMad(x, w=NULL, idxs=NULL, na.rm=FALSE, constant=1.4826, center=NULL, ...) colWeightedMads(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) rowWeightedMads(x, w=NULL, rows=NULL, cols=NULL, na.rm=FALSE, ...) } \description{ Computes a weighted MAD of a numeric vector. } \arguments{ \item{x}{a \code{\link[base]{numeric}} \code{\link[base]{vector}} containing the values whose weighted MAD is to be computed.} \item{w}{a vector of weights the same length as \code{x} giving the weights to use for each element of \code{x}. Negative weights are treated as zero weights. Default value is equal weight to all values.} \item{idxs, rows, cols}{A \code{\link[base]{vector}} indicating subset of elements (or rows and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{na.rm}{a logical value indicating whether \code{\link[base]{NA}} values in \code{x} should be stripped before the computation proceeds, or not. If \code{\link[base]{NA}}, no check at all for \code{\link[base]{NA}}s is done. Default value is \code{\link[base]{NA}} (for efficiency).} \item{constant}{A \code{\link[base]{numeric}} scale factor, cf. \code{\link[stats]{mad}}.} \item{center}{Optional \code{\link[base]{numeric}} scalar specifying the center location of the data. If \code{\link[base]{NULL}}, it is estimated from data.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} scalar. } \section{Missing values}{ Missing values are dropped at the very beginning, if argument \code{na.rm} is \code{\link[base:logical]{TRUE}}, otherwise not. } \examples{ x <- 1:10 n <- length(x) m1 <- mad(x) m2 <- weightedMad(x) stopifnot(identical(m1, m2)) w <- rep(1, times=n) m1 <- weightedMad(x, w) stopifnot(identical(m1,m2)) # All weight on the first value w[1] <- Inf m <- weightedMad(x, w) stopifnot(m == 0) # All weight on the first two values w[1:2] <- Inf m1 <- mad(x[1:2]) m2 <- weightedMad(x, w) stopifnot(identical(m1,m2)) # All weights set to zero w <- rep(0, times=n) m <- weightedMad(x, w) stopifnot(is.na(m)) } \seealso{ For the non-weighted MAD, see \code{\link[stats]{mad}}. Internally \code{\link{weightedMedian}}() is used to calculate the weighted median. } \author{Henrik Bengtsson} \keyword{univar} \keyword{robust} matrixStats/man/rowRanks.Rd0000644000175100001440000000746312776330571015510 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowRanks.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowRanks} \alias{rowRanks} \alias{colRanks} \title{Gets the rank of each row (column) of a matrix} \description{ Gets the rank of each row (column) of a matrix. } \usage{ rowRanks(x, rows=NULL, cols=NULL, ties.method=c("max", "average", "min"), dim.=dim(x), ...) colRanks(x, rows=NULL, cols=NULL, ties.method=c("max", "average", "min"), dim.=dim(x), preserveShape=FALSE, ...) } \arguments{ \item{x}{A \code{\link[base]{numeric}} or \code{\link[base]{integer}} NxK \code{\link[base]{matrix}}.} \item{rows, cols}{A \code{\link[base]{vector}} indicating subset of rows (and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{ties.method}{A \code{\link[base]{character}} string specifying how ties are treated. For details, see below.} \item{dim.}{An \code{\link[base]{integer}} \code{\link[base]{vector}} of length two specifying the dimension of \code{x}, also when not a \code{\link[base]{matrix}}.} \item{preserveShape}{A \code{\link[base]{logical}} specifying whether the \code{\link[base]{matrix}} returned should preserve the input shape of \code{x}, or not.} \item{...}{Not used.} } \value{ An \code{\link[base]{integer}} \code{\link[base]{matrix}} is returned. The \code{rowRanks()} function always returns an NxK \code{\link[base]{matrix}}, where N (K) is the number of rows (columns) whose ranks are calculated. The \code{colRanks()} function returns an NxK \code{\link[base]{matrix}}, if \code{preserveShape = TRUE}, otherwise a KxN \code{\link[base]{matrix}}. %% The mode of the returned matrix is \code{\link[base]{integer}}, except for %% \code{ties.method == "average"} when it is \code{\link[base]{double}}. } \details{ The row ranks of \code{x} are collected as \emph{rows} of the result matrix. The column ranks of \code{x} are collected as \emph{rows} if \code{preserveShape = FALSE}, otherwise as \emph{columns}. The implementation is optimized for both speed and memory. To avoid coercing to \code{\link[base]{double}}s (and hence memory allocation), there is a unique implementation for \code{\link[base]{integer}} matrices. It is more memory efficient to do \code{colRanks(x, preserveShape=TRUE)} than \code{t(colRanks(x, preserveShape=FALSE))}. Any \code{\link[base]{names}} of \code{x} are ignored and absent in the result. } \section{Missing and non- values}{ These are ranked as \code{NA}, as with \code{na.last="keep"} in the \code{\link[base]{rank}}() function. } \section{Ties}{ When some values are equal ("ties"), argument \code{ties.method} specifies what their ranks should be. If \code{ties.method} is \code{"max"}, ties are ranked as the maximum value. If \code{ties.method} is \code{"average"}, ties are ranked by their average. If \code{ties.method} is \code{"max"} (\code{"min"}), ties are ranked as the maximum (minimum) value. If \code{ties.method} is \code{"average"}, ties are ranked by their average. For further details, see \code{\link[base]{rank}}(). } \author{ Hector Corrada Bravo and Harris Jaffee. Peter Langfelder for adding 'ties.method' support. Henrik Bengtsson adapted the original native implementation of \code{rowRanks()} from Robert Gentleman's \code{rowQ()} in the \pkg{Biobase} package. } \seealso{ \code{\link[base]{rank}}(). For developers, see also Section 'Utility functions' in 'Writing R Extensions manual', particularly the native functions \code{R_qsort_I()} and \code{R_qsort_int_I()}. } \keyword{array} \keyword{iteration} \keyword{robust} \keyword{univar} matrixStats/man/rowIQRs.Rd0000644000175100001440000000363112776330571015241 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowIQRs.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowIQRs} \alias{rowIQRs} \alias{colIQRs} \alias{iqr} \title{Estimates of the interquartile range for each row (column) in a matrix} \description{ Estimates of the interquartile range for each row (column) in a matrix. } \usage{ rowIQRs(x, rows=NULL, cols=NULL, na.rm=FALSE, ...) colIQRs(x, rows=NULL, cols=NULL, na.rm=FALSE, ...) iqr(x, idxs=NULL, na.rm=FALSE, ...) } \arguments{ \item{x}{A \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}.} \item{idxs, rows, cols}{A \code{\link[base]{vector}} indicating subset of elements (or rows and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, missing values are dropped first, otherwise not.} \item{...}{Additional arguments passed to \code{\link{rowQuantiles}}() (\code{colQuantiles()}).} } \value{ Returns a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N (K). } \section{Missing values}{ Contrary to \code{\link[stats]{IQR}}, which gives an error if there are missing values and \code{na.rm=FALSE}, \code{iqr()} and its corresponding row and column-specific functions return \code{\link[base]{NA}}_real_. } \examples{ set.seed(1) x <- matrix(rnorm(50*40), nrow=50, ncol=40) str(x) # Row IQRs q <- rowIQRs(x) print(q) q0 <- apply(x, MARGIN=1, FUN=IQR) stopifnot(all.equal(q0, q)) # Column IQRs q <- colIQRs(x) print(q) q0 <- apply(x, MARGIN=2, FUN=IQR) stopifnot(all.equal(q0, q)) } \author{Henrik Bengtsson} \seealso{ See \code{\link[stats]{IQR}}. See \code{\link{rowSds}}(). } \keyword{array} \keyword{iteration} \keyword{robust} \keyword{univar} matrixStats/man/rowLogSumExps.Rd0000644000175100001440000000337712776330571016500 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % rowLogSumExps.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{rowLogSumExps} \alias{rowLogSumExps} \alias{colLogSumExps} \alias{rowLogSumExps,matrix-method} \alias{colLogSumExps,matrix-method} \title{Accurately computes the logarithm of the sum of exponentials across rows or columns} \description{ Accurately computes the logarithm of the sum of exponentials across rows or columns. } \usage{ rowLogSumExps(lx, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(lx), ...) colLogSumExps(lx, rows=NULL, cols=NULL, na.rm=FALSE, dim.=dim(lx), ...) } \arguments{ \item{lx}{A \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}. Typically \code{lx} are \eqn{log(x)} values.} \item{rows, cols}{A \code{\link[base]{vector}} indicating subset of rows (and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, any missing values are ignored, otherwise not.} \item{dim.}{An \code{\link[base]{integer}} \code{\link[base]{vector}} of length two specifying the dimension of \code{x}, also when not a \code{\link[base]{matrix}}.} \item{...}{Not used.} } \value{ A \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N (K). } \section{Benchmarking}{ These methods are implemented in native code and have been optimized for speed and memory. } \author{ Native implementation by Henrik Bengtsson. Original R code by Nakayama ??? (Japan). } \seealso{ To calculate the same on vectors, \code{\link{logSumExp}}(). } \keyword{array}