boot/0000755000176000001440000000000012663024217011250 5ustar ripleyusersboot/po/0000755000176000001440000000000012465333547011677 5ustar ripleyusersboot/po/R-fr.po0000644000176000001440000002167512035553135013046 0ustar ripleyusers# Translation of R-boot.pot to French # Copyright (C) 2005 The R Foundation # This file is distributed under the same license as the boot R package. # Philippe Grosjean , 2005. # msgid "" msgstr "" "Project-Id-Version: boot 1.2-23\n" "Report-Msgid-Bugs-To: bugs@r-project.org\n" "POT-Creation-Date: 2012-10-11 15:21\n" "PO-Revision-Date: 2012-10-03 15:35+0100\n" "Last-Translator: Philippe Grosjean \n" "Language-Team: French \n" "Language: fr\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=ISO-8859-1\n" "Content-Transfer-Encoding: 8bit\n" "Plural-Forms: nplurals=2; plural=(n > 1);\n" "X-Generator: Poedit 1.5.3\n" msgid "" "'simple=TRUE' is only valid for 'sim=\"ordinary\", stype=\"i\", n=0', so " "ignored" msgstr "" "'simple=TRUE' n'est seulement valable que pour 'sim=\"ordinary\", stype=\"i" "\", n=0' ; il est donc ignor" msgid "no data in call to 'boot'" msgstr "pas de donnes lors de l'appel 'boot'" msgid "negative value of 'm' supplied" msgstr "valeur ngative donne pour 'm'" msgid "length of 'm' incompatible with 'strata'" msgstr "longueur de 'm' incompatible avec 'strata'" msgid "dimensions of 'R' and 'weights' do not match" msgstr "les dimensions de 'R' et 'weights' ne sont pas conformes" msgid "arguments are not all the same type of \"boot\" object" msgstr "les arguments ne sont pas tous du mme type pour l'objet \"boot\"" msgid "index array not defined for model-based resampling" msgstr "" "indiage de tableau non dfini pour un rchantillonnage bas sur un modle" msgid "boot.array not implemented for this object" msgstr "boot.array non implment pour cet objet" msgid "array cannot be found for parametric bootstrap" msgstr "tableau non trouv pour un bootstrap pamtrique" msgid "%s distribution not supported: using normal instead" msgstr "" "%s distribution non supporte, utilisation d'une distribution normale la " "place" msgid "only first element of 'index' used" msgstr "seul le premier lment d''index' est utilis" msgid "'K' outside allowable range" msgstr "'K' en dehors de la plage admise" msgid "'K' has been set to %f" msgstr "'K' est fix %f" msgid "'t' and 't0' must be supplied together" msgstr "'t' et 't0' doivent tre fixs simultanment" msgid "index out of bounds; minimum index only used." msgstr "indice hors plage ; l'indice le plus petit est utilis" msgid "'t' must of length %d" msgstr "'t' doit tre de longueur %d" msgid "bootstrap variances needed for studentized intervals" msgstr "" "les variances de bootstrap sont ncessaires pour les intervalles studentiss" msgid "BCa intervals not defined for time series bootstraps" msgstr "" "les intervalles BCa ne sont pas dfinis pour les bootstraps sur les sries " "temporelles" msgid "bootstrap output object or 't0' required" msgstr "objet rsultat d'un bootstrap ou 't0' requis" msgid "unable to calculate 'var.t0'" msgstr "impossible de calculer 'var.t0'" msgid "extreme order statistics used as endpoints" msgstr "statistiques d'ordre extrme utilises comme points finaux" msgid "variance required for studentized intervals" msgstr "variance requise pour les intervalles de confiance studentiss" msgid "estimated adjustment 'w' is infinite" msgstr "l'ajustement de 'w' est infini" msgid "estimated adjustment 'a' is NA" msgstr "l'ajustement de 'a' estim est NA" msgid "only first element of 'index' used in 'abc.ci'" msgstr "seul le premier lment de 'index' est utilis dans 'abc.ci'" msgid "missing values not allowed in 'data'" msgstr "valeurs manquantes non autorises dans 'data'" msgid "unknown value of 'sim'" msgstr "valeur inconnue de 'sim'" msgid "'data' must be a matrix with at least 2 columns" msgstr "'data' doit tre une matrice contenant au moins 2 colonnes" msgid "'index' must contain 2 elements" msgstr "'index' doit contenir 2 lments" msgid "only first 2 elements of 'index' used" msgstr "seuls les deux premiers lments d''index' sont utiliss" msgid "indices are incompatible with 'ncol(data)'" msgstr "les indices sont incompatibles avec 'ncol(data)'" msgid "sim = \"weird\" cannot be used with a \"coxph\" object" msgstr "sim=\"weird\" ne peut tre utilis avec un object \"coxph\"" msgid "only columns %s and %s of 'data' used" msgstr "seule les colonnes %s et %s de 'data' sont utilises" msgid "no coefficients in Cox model -- model ignored" msgstr "pas de coefficients dans le modle Cox -- modle ignor" msgid "'F.surv' is required but missing" msgstr "'F.surv' est requis mais manquant" msgid "'G.surv' is required but missing" msgstr "'G.surv' est requis mais manquant" msgid "'strata' of wrong length" msgstr "'strata' de mauvaise longueur" msgid "influence values cannot be found from a parametric bootstrap" msgstr "" "les valeurs d'influence ne peuvent tre trouves partir d'un bootstrap " "paramtrique" msgid "neither 'data' nor bootstrap object specified" msgstr "pas de 'data' ou d'objet bootstrap spcifi" msgid "neither 'statistic' nor bootstrap object specified" msgstr "pas de 'statistic' ou d'objet bootstrap spcifi" msgid "'stype' must be \"w\" for type=\"inf\"" msgstr "'stype' doit tre \"w\" pour type=\"inf\"" msgid "input 't' ignored; type=\"inf\"" msgstr "entre 't' ignore ; type=\"inf\"" msgid "bootstrap object needed for type=\"reg\"" msgstr "objet 'bootstrap' requis pour type=\"reg\"" msgid "input 't' ignored; type=\"jack\"" msgstr "entre 't' ignore ; type=\"jack\"" msgid "input 't' ignored; type=\"pos\"" msgstr "entre 't' ignore ; type=\"pos\"" msgid "input 't0' ignored: neither 't' nor 'L' supplied" msgstr "entre 't0' ignore : ni 't', ni 'L' n'est fourni" msgid "bootstrap output matrix missing" msgstr "matrice manquante dans la sortie bootstrap" msgid "use 'boot.ci' for scalar parameters" msgstr "utilisez 'boot.ci' pour des paramtres scalaires" msgid "unable to achieve requested overall error rate" msgstr "impossible d'atteindre le taux global d'erreur spcifi" msgid "unable to find multiplier for %f" msgstr "impossible de trouver un multiplicateur pour %f" msgid "'theta' or 'lambda' required" msgstr "'theta' ou 'lambda' requis" msgid "0 elements not allowed in 'q'" msgstr "0 lments non permis pour 'q'" msgid "bootstrap replicates must be supplied" msgstr "les rplications de bootstrap doivent tre fournies" msgid "either 'boot.out' or 'w' must be specified." msgstr "soit 'boot.out', soit 'w' doit tre spcifi" msgid "only first column of 't' used" msgstr "seule la premire colonne de 't' est utilise" msgid "invalid value of 'sim' supplied" msgstr "valeur incorrecte spcifie pour 'sim'" msgid "'R' and 'theta' have incompatible lengths" msgstr "'R' et 'theta' ont des longueurs non conformes" msgid "R[1L] must be positive for frequency smoothing" msgstr "R[1L] doit tre positif pour un lissage des frquences" msgid "'R' and 'alpha' have incompatible lengths" msgstr "'R' et 'alpha' ont des longueurs non conformes" msgid "extreme values used for quantiles" msgstr "valeurs extrmes utilises pour les quantiles" msgid "'theta' must be supplied if R[1L] = 0" msgstr "'theta' doit tre fourni si R[1L] = 0" msgid "'alpha' ignored; R[1L] = 0" msgstr "'alpha' ignor ; R[1L] = 0" msgid "control methods undefined when 'boot.out' has weights" msgstr "mthodes de contrle non dfinies lorsque 'boot.out' est pondr" msgid "number of columns of 'A' (%d) not equal to length of 'u' (%d)" msgstr "" "le nombre de colonnes de 'A' (%d) n'est pas gal la longueur de 'u' (%d)" msgid "either 'A' and 'u' or 'K.adj' and 'K2' must be supplied" msgstr "soit 'A' et 'u', soit 'K.adj' et 'K2' doivent tre fournis" msgid "this type not implemented for Poisson" msgstr "ce type n'est pas implment pour 'Poisson'" msgid "this type not implemented for Binary" msgstr "ce type n'est pas implment pour 'Binary'" msgid "one of 't' or 't0' required" msgstr "soit 't', soit 't0' est requis" msgid "function 'u' missing" msgstr "fonction 'u' manquante" msgid "'u' must be a function" msgstr "'u' doit tre une fonction" msgid "unable to find range" msgstr "impossible de trouver l'tendue des valeurs" msgid "'R' must be positive" msgstr "'R' doit tre positif" msgid "invalid value of 'l'" msgstr "valeur de 'l' incorrecte" msgid "unrecognized value of 'sim'" msgstr "valeur de 'sim' non reconnue" msgid "multivariate time series not allowed" msgstr "sries temporelles multivaries non admises" msgid "likelihood never exceeds %f" msgstr "la vraissemblance n'a jamais excd %f" msgid "likelihood exceeds %f at only one point" msgstr "la vraissemblance excde %f a seulement un point" #~ msgid "only columns" #~ msgstr "seulement des colonnes" #~ msgid "and" #~ msgstr "et" #~ msgid "of data used" #~ msgstr "des donnes utilises" #~ msgid "number of columns of A (" #~ msgstr "le nombre de colonnes de A (" #~ msgid "at only one point" #~ msgstr " seulement un point" #~ msgid "invalid proportions input" #~ msgstr "proportions d'entre incorrectes" #~ msgid "irregular time series not allowed" #~ msgstr "sries temporelles irrgulires non admises" boot/po/R-boot.pot0000644000176000001440000001124012465333606013557 0ustar ripleyusersmsgid "" msgstr "" "Project-Id-Version: boot 1.3-14\n" "POT-Creation-Date: 2015-02-07 06:58\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language-Team: LANGUAGE \n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=CHARSET\n" "Content-Transfer-Encoding: 8bit\n" msgid "'simple=TRUE' is only valid for 'sim=\"ordinary\", stype=\"i\", n=0', so ignored" msgstr "" msgid "no data in call to 'boot'" msgstr "" msgid "negative value of 'm' supplied" msgstr "" msgid "length of 'm' incompatible with 'strata'" msgstr "" msgid "dimensions of 'R' and 'weights' do not match" msgstr "" msgid "arguments are not all the same type of \"boot\" object" msgstr "" msgid "index array not defined for model-based resampling" msgstr "" msgid "boot.array not implemented for this object" msgstr "" msgid "array cannot be found for parametric bootstrap" msgstr "" msgid "%s distribution not supported: using normal instead" msgstr "" msgid "only first element of 'index' used" msgstr "" msgid "'K' outside allowable range" msgstr "" msgid "'K' has been set to %f" msgstr "" msgid "'t' and 't0' must be supplied together" msgstr "" msgid "index out of bounds; minimum index only used." msgstr "" msgid "'t' must of length %d" msgstr "" msgid "bootstrap variances needed for studentized intervals" msgstr "" msgid "BCa intervals not defined for time series bootstraps" msgstr "" msgid "bootstrap output object or 't0' required" msgstr "" msgid "unable to calculate 'var.t0'" msgstr "" msgid "extreme order statistics used as endpoints" msgstr "" msgid "variance required for studentized intervals" msgstr "" msgid "estimated adjustment 'w' is infinite" msgstr "" msgid "estimated adjustment 'a' is NA" msgstr "" msgid "only first element of 'index' used in 'abc.ci'" msgstr "" msgid "missing values not allowed in 'data'" msgstr "" msgid "unknown value of 'sim'" msgstr "" msgid "'data' must be a matrix with at least 2 columns" msgstr "" msgid "'index' must contain 2 elements" msgstr "" msgid "only first 2 elements of 'index' used" msgstr "" msgid "indices are incompatible with 'ncol(data)'" msgstr "" msgid "sim = \"weird\" cannot be used with a \"coxph\" object" msgstr "" msgid "only columns %s and %s of 'data' used" msgstr "" msgid "no coefficients in Cox model -- model ignored" msgstr "" msgid "'F.surv' is required but missing" msgstr "" msgid "'G.surv' is required but missing" msgstr "" msgid "'strata' of wrong length" msgstr "" msgid "influence values cannot be found from a parametric bootstrap" msgstr "" msgid "neither 'data' nor bootstrap object specified" msgstr "" msgid "neither 'statistic' nor bootstrap object specified" msgstr "" msgid "'stype' must be \"w\" for type=\"inf\"" msgstr "" msgid "input 't' ignored; type=\"inf\"" msgstr "" msgid "bootstrap object needed for type=\"reg\"" msgstr "" msgid "input 't' ignored; type=\"jack\"" msgstr "" msgid "input 't' ignored; type=\"pos\"" msgstr "" msgid "input 't0' ignored: neither 't' nor 'L' supplied" msgstr "" msgid "bootstrap output matrix missing" msgstr "" msgid "use 'boot.ci' for scalar parameters" msgstr "" msgid "unable to achieve requested overall error rate" msgstr "" msgid "unable to find multiplier for %f" msgstr "" msgid "'theta' or 'lambda' required" msgstr "" msgid "0 elements not allowed in 'q'" msgstr "" msgid "bootstrap replicates must be supplied" msgstr "" msgid "either 'boot.out' or 'w' must be specified." msgstr "" msgid "only first column of 't' used" msgstr "" msgid "invalid value of 'sim' supplied" msgstr "" msgid "'R' and 'theta' have incompatible lengths" msgstr "" msgid "R[1L] must be positive for frequency smoothing" msgstr "" msgid "'R' and 'alpha' have incompatible lengths" msgstr "" msgid "extreme values used for quantiles" msgstr "" msgid "'theta' must be supplied if R[1L] = 0" msgstr "" msgid "'alpha' ignored; R[1L] = 0" msgstr "" msgid "control methods undefined when 'boot.out' has weights" msgstr "" msgid "number of columns of 'A' (%d) not equal to length of 'u' (%d)" msgstr "" msgid "either 'A' and 'u' or 'K.adj' and 'K2' must be supplied" msgstr "" msgid "this type not implemented for Poisson" msgstr "" msgid "this type not implemented for Binary" msgstr "" msgid "one of 't' or 't0' required" msgstr "" msgid "function 'u' missing" msgstr "" msgid "'u' must be a function" msgstr "" msgid "unable to find range" msgstr "" msgid "'R' must be positive" msgstr "" msgid "invalid value of 'l'" msgstr "" msgid "unrecognized value of 'sim'" msgstr "" msgid "multivariate time series not allowed" msgstr "" msgid "likelihood never exceeds %f" msgstr "" msgid "likelihood exceeds %f at only one point" msgstr "" boot/po/R-de.po0000644000176000001440000002126212035553062013016 0ustar ripleyusers# Translation of boot to German # Copyright (C) 2005 The R Foundation # This file is distributed under the same license as the boot package. # Copyright (C) of this file 2009-2012 Chris Leick . # 2012 Detlef Steuer msgid "" msgstr "" "Project-Id-Version: R 2.15.2 / boot 1.3-6-\n" "Report-Msgid-Bugs-To: bugs@r-project.org\n" "POT-Creation-Date: 2012-10-11 15:21\n" "PO-Revision-Date: 2012-10-11 16:01+0200\n" "Last-Translator: Chris Leick \n" "Language-Team: German \n" "Language: de\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=UTF-8\n" "Content-Transfer-Encoding: 8bit\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" msgid "" "'simple=TRUE' is only valid for 'sim=\"ordinary\", stype=\"i\", n=0', so " "ignored" msgstr "" "'simple=TRUE' gilt nur für 'sim=\"ordinary\", stype=\"i\", n=0' und wird " "daher hier ignoriert" msgid "no data in call to 'boot'" msgstr "keine Daten im Aufruf von 'boot'" msgid "negative value of 'm' supplied" msgstr "negativer Wert von 'm' angegeben" msgid "length of 'm' incompatible with 'strata'" msgstr "Länge von 'm' inkompatibel mit 'strata'" msgid "dimensions of 'R' and 'weights' do not match" msgstr "Dimensionen von 'R' und 'weights' stimmen nicht überein" msgid "arguments are not all the same type of \"boot\" object" msgstr "Argumente waren nicht all vom selben Typ des 'boot'-Objekts" # http://de.wikipedia.org/wiki/Resampling msgid "index array not defined for model-based resampling" msgstr "Index-Array nicht für Modell-basiertes Resampling definiert" msgid "boot.array not implemented for this object" msgstr "'boot.array' nicht für dieses Objekt implementiert" # http://de.wikipedia.org/wiki/Bootstrapping_(Statistik) msgid "array cannot be found for parametric bootstrap" msgstr "Array kann nicht für parametrisches Bootstrapping gefunden werden" # R/bootfuns.q msgid "%s distribution not supported: using normal instead" msgstr "" "%s Verteilung nicht unterstützt, stattdessen wird Normalverteilung benutzt" msgid "only first element of 'index' used" msgstr "nur erstes Element von 'index' benutzt" msgid "'K' outside allowable range" msgstr "'K' außerhalb des erlaubbaren Bereichs" msgid "'K' has been set to %f" msgstr "'K' wurde auf %f gesetzt" msgid "'t' and 't0' must be supplied together" msgstr "'t' und 't0' müssen zusammen angegeben werden" msgid "index out of bounds; minimum index only used." msgstr "Index außerhalb des Rands. Minimalindex wird benutzt." msgid "'t' must of length %d" msgstr "'t' muss die Länge %d haben" # http://de.wikipedia.org/wiki/Studentisierung msgid "bootstrap variances needed for studentized intervals" msgstr "Bootstrap-Varianzen für studentisierte Intervalle benötigt" msgid "BCa intervals not defined for time series bootstraps" msgstr "BCa Intervalle nicht für Zeitreihenbootstrap definiert." msgid "bootstrap output object or 't0' required" msgstr "Bootstrap-Ausgabeobjekt oder 't0' benötigt" msgid "unable to calculate 'var.t0'" msgstr "'var.t0' kann nicht berechnet werden" # http://xtremes.stat.math.uni-siegen.de/xtremes_old/history.pdf msgid "extreme order statistics used as endpoints" msgstr "Extremwertstatistiken werden als Endpunkte benutzt" msgid "variance required for studentized intervals" msgstr "Varianz für studentisierte Intervalle benötigt" msgid "estimated adjustment 'w' is infinite" msgstr "geschätzte Anpassung 'w' ist unendlich" msgid "estimated adjustment 'a' is NA" msgstr "geschätzte Einstellung 'a' ist NA" msgid "only first element of 'index' used in 'abc.ci'" msgstr "nur erstes Element von 'index' wird in 'abc.ci' benutzt" msgid "missing values not allowed in 'data'" msgstr "fehlende Werte in 'data' nicht erlaubt" msgid "unknown value of 'sim'" msgstr "unbekannter Wert von 'sim'" msgid "'data' must be a matrix with at least 2 columns" msgstr "'data' muss eine Matrix mit mindestens 2 Spalten sein" msgid "'index' must contain 2 elements" msgstr "'index' muss 2 Elemente enthalten" msgid "only first 2 elements of 'index' used" msgstr "nur die beiden ersten Elemente von 'index' werden benutzt" msgid "indices are incompatible with 'ncol(data)'" msgstr "Indizes sind inkompatibel mit 'ncol(data)'" msgid "sim = \"weird\" cannot be used with a \"coxph\" object" msgstr "sim = \"weird\" kann nicht mit einem \"coxph\" Objekt benutzt werden" msgid "only columns %s and %s of 'data' used" msgstr "nur die Spalten %s und %s von 'data' werden benutzt" msgid "no coefficients in Cox model -- model ignored" msgstr "keine Koeffizienten im Cox-Modell -- Modell ignoriert" msgid "'F.surv' is required but missing" msgstr "'F.surv' wird benötigt, fehlt jedoch" msgid "'G.surv' is required but missing" msgstr "'G.surv' wird benötigt, fehlt jedoch" msgid "'strata' of wrong length" msgstr "'strata' hat falsche Länge" msgid "influence values cannot be found from a parametric bootstrap" msgstr "" "es können keine beeinflussenden Werte von einem parametrischen Bootstrap " "gefunden werden" msgid "neither 'data' nor bootstrap object specified" msgstr "weder 'data' noch Bootstrap-Objekt angegeben" msgid "neither 'statistic' nor bootstrap object specified" msgstr "weder 'statistic' noch Bootstrap-Objekt angegeben" msgid "'stype' must be \"w\" for type=\"inf\"" msgstr "'stype' muss für type=\"inf\" 'w' sein" msgid "input 't' ignored; type=\"inf\"" msgstr "Eingabe 't' ignoriert; type=\"inf\"" msgid "bootstrap object needed for type=\"reg\"" msgstr "Bootstrap-Objekt für type=\"reg\" benötigt" msgid "input 't' ignored; type=\"jack\"" msgstr "Eingabe 't' ignoriert; type=\"jack\"" msgid "input 't' ignored; type=\"pos\"" msgstr "Eingabe 't' ignoriert; type=\"pos\"" msgid "input 't0' ignored: neither 't' nor 'L' supplied" msgstr "Eingabe 't0' ignoriert: weder 't' noch 'L' angegeben" msgid "bootstrap output matrix missing" msgstr "Bootstrap-Ausgabematrix fehlt" msgid "use 'boot.ci' for scalar parameters" msgstr "benutzen Sie 'boot.ci' für skalare Parameter" msgid "unable to achieve requested overall error rate" msgstr "geforderte overall Fehlerquote kann nicht erreicht werden" msgid "unable to find multiplier for %f" msgstr "Es kann kein Multiplikator für %f gefunden werden" msgid "'theta' or 'lambda' required" msgstr "'theta' oder 'lambda' benötigt" msgid "0 elements not allowed in 'q'" msgstr "0 Elemente nicht in 'q' erlaubt" msgid "bootstrap replicates must be supplied" msgstr "Bootstrap-Kopien müssen angegeben werden" msgid "either 'boot.out' or 'w' must be specified." msgstr "Entweder 'boot.out' oder 'w' muss angegeben werden." msgid "only first column of 't' used" msgstr "Nur erste Spalte von 't' wird benutzt." msgid "invalid value of 'sim' supplied" msgstr "ungültiger Wert von 'sim' angegeben" msgid "'R' and 'theta' have incompatible lengths" msgstr "'R' und 'theta' haben inkompatible Längen" msgid "R[1L] must be positive for frequency smoothing" msgstr "R[1L] muss für Frequenz-Glättung positiv sein" msgid "'R' and 'alpha' have incompatible lengths" msgstr "'R' und 'alpha' haben inkompatible Längen" msgid "extreme values used for quantiles" msgstr "Extremwerte werden für Quantile benutzt" msgid "'theta' must be supplied if R[1L] = 0" msgstr "'theta' muss angegeben werden, falls R[1L] = 0 ist" msgid "'alpha' ignored; R[1L] = 0" msgstr "'alpha' ignoriert; R[1L]=0" msgid "control methods undefined when 'boot.out' has weights" msgstr "Kontrollmethoden undefiniert, wenn 'boot.out' Gewichte hat" msgid "number of columns of 'A' (%d) not equal to length of 'u' (%d)" msgstr "" "Anzahl der Spalten von 'A' (%d) ist nicht gleich der Länge von 'u' (%d)" msgid "either 'A' and 'u' or 'K.adj' and 'K2' must be supplied" msgstr "entweder 'A' und 'u' oder 'K.adj' und 'K2' müssen angegeben werden" msgid "this type not implemented for Poisson" msgstr "dieser Typ ist nicht für Poisson implementiert" msgid "this type not implemented for Binary" msgstr "dieser Typ ist nicht für Binary implementiert" msgid "one of 't' or 't0' required" msgstr "eins von 't' oder 't0' benötigt" msgid "function 'u' missing" msgstr "Funktion 'u' fehlt" msgid "'u' must be a function" msgstr "'u' muss eine Funktion sein" msgid "unable to find range" msgstr "Bereich kann nicht gefunden werden" msgid "'R' must be positive" msgstr "'R' muss psitiv sein" msgid "invalid value of 'l'" msgstr "ungültiger Wert von 'l'" msgid "unrecognized value of 'sim'" msgstr "unbekannter Wert von 'sim'" # http://de.wikipedia.org/wiki/Multivariat msgid "multivariate time series not allowed" msgstr "multivariate Zeitserien nicht erlaubt" msgid "likelihood never exceeds %f" msgstr "Wahrscheinlichkeit überschreitet niemals %f" msgid "likelihood exceeds %f at only one point" msgstr "Wahrscheinlichkeit überschreitet %f an einem Punkt" boot/po/R-pl.po0000644000176000001440000003753512314055425013053 0ustar ripleyusersmsgid "" msgstr "" "Project-Id-Version: boot 1.3-10\n" "Report-Msgid-Bugs-To: bugs@r-project.org\n" "POT-Creation-Date: 2013-03-20 07:24\n" "PO-Revision-Date: \n" "Last-Translator: Łukasz Daniel \n" "Language-Team: Łukasz Daniel \n" "Language: pl_PL\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=UTF-8\n" "Content-Transfer-Encoding: 8bit\n" "na-Revision-Date: 2012-05-29 07:55+0100\n" "Plural-Forms: nplurals=3; plural=(n==1 ? 0 : n%10>=2 && n%10<=4 && (n%100<10 " "|| n%100>=20) ? 1 : 2);\n" "X-Poedit-SourceCharset: iso-8859-1\n" "X-Generator: Poedit 1.5.4\n" # boot/R/bootfuns.R: 111 # warning("'simple=TRUE' is only valid for 'sim=\"ordinary\", stype=\"i\", n=0', so ignored") msgid "" "'simple=TRUE' is only valid for 'sim=\"ordinary\", stype=\"i\", n=0', so " "ignored" msgstr "" "'simple=TRUE' jest poprawne jedynie dla 'sim=\"ordinary\", stype=\"i\", " "n=0', tak więc zignorowano" # boot/R/bootfuns.R: 118 # stop("no data in call to 'boot'") msgid "no data in call to 'boot'" msgstr "brak danych w wywołaniu 'boot'" # boot/R/bootfuns.R: 126 # stop("negative value of 'm' supplied") msgid "negative value of 'm' supplied" msgstr "dostarczono ujemną wartość 'm'" # boot/R/bootfuns.R: 128 # stop("length of 'm' incompatible with 'strata'") msgid "length of 'm' incompatible with 'strata'" msgstr "długość 'm' jest niekompatybilna z 'strata'" # boot/R/bootfuns.R: 131 # stop("dimensions of 'R' and 'weights' do not match") msgid "dimensions of 'R' and 'weights' do not match" msgstr "wymiary 'R' oraz 'weights' nie zgadzają się" # boot/R/bootfuns.R: 249 # stop("arguments are not all the same type of \"boot\" object") msgid "arguments are not all the same type of \"boot\" object" msgstr "argumenty nie są wszystkie tego samego typu obiektu 'boot'" # boot/R/bootfuns.R: 275 # stop("index array not defined for model-based resampling") msgid "index array not defined for model-based resampling" msgstr "" "tablica indeksów nie jest zdefiniowana dla próbkowania opartego na modelu" # boot/R/bootfuns.R: 299 # stop("boot.array not implemented for this object") msgid "boot.array not implemented for this object" msgstr "'boot.array' nie został zaimplementowany dla tego obiektu" # boot/R/bootfuns.R: 304 # stop("array cannot be found for parametric bootstrap") msgid "array cannot be found for parametric bootstrap" msgstr "nie można znaleźć tablicy dla parametrycznego bootstrapu" # boot/R/bootfuns.R: 359 # warning(gettextf("%s distribution not supported: using normal instead", sQuote(qdist)), domain = "R-boot") msgid "%s distribution not supported: using normal instead" msgstr "rozkład %s nie jest wspierany: w zamian używanie rozkładu normalnego" # boot/R/bootfuns.R: 697 # warning("only first element of 'index' used") # boot/R/bootfuns.R: 1654 # warning("only first element of 'index' used") # boot/R/bootfuns.R: 1666 # warning("only first element of 'index' used") # boot/R/bootfuns.R: 1676 # warning("only first element of 'index' used") # boot/R/bootfuns.R: 1684 # warning("only first element of 'index' used") # boot/R/bootfuns.R: 1839 # warning("only first element of 'index' used") # boot/R/bootfuns.R: 2163 # warning("only first element of 'index' used") # boot/R/bootfuns.R: 2219 # warning("only first element of 'index' used") # boot/R/bootfuns.R: 2260 # warning("only first element of 'index' used") # boot/R/bootfuns.R: 2288 # warning("only first element of 'index' used") # boot/R/bootfuns.R: 2417 # warning("only first element of 'index' used") # boot/R/bootfuns.R: 2438 # warning("only first element of 'index' used") msgid "only first element of 'index' used" msgstr "tylko pierwszy element 'index' został użyty" # boot/R/bootfuns.R: 844 # stop("'K' outside allowable range") msgid "'K' outside allowable range" msgstr "'K' poza dozwolonym zakresem" # boot/R/bootfuns.R: 851 # warning(gettextf("'K' has been set to %f", K), domain = "R-boot") msgid "'K' has been set to %f" msgstr "'K' został ustawiony na %f" # boot/R/bootfuns.R: 898 # stop("'t' and 't0' must be supplied together") msgid "'t' and 't0' must be supplied together" msgstr "'t' oraz 't0' muszą zostać dostarczone razem" # boot/R/bootfuns.R: 908 # warning("index out of bounds; minimum index only used.") msgid "index out of bounds; minimum index only used." msgstr "indeks poza zakresem; użyto minimalnego indeksu." # boot/R/bootfuns.R: 926 # stop(gettextf("'t' must of length %d", boot.out$R), domain = "R-boot") msgid "'t' must of length %d" msgstr "'t' musi być długości %d" # boot/R/bootfuns.R: 950 # warning("bootstrap variances needed for studentized intervals") msgid "bootstrap variances needed for studentized intervals" msgstr "potrzebne są bootstrapowe wariancje dla studentyzowanych przedziałów" # boot/R/bootfuns.R: 959 # warning("BCa intervals not defined for time series bootstraps") msgid "BCa intervals not defined for time series bootstraps" msgstr "" "przedziały BC nie są zdefiniowane dla bootstrapowych szeregów czasowych" # boot/R/bootfuns.R: 1098 # stop("bootstrap output object or 't0' required") msgid "bootstrap output object or 't0' required" msgstr "wymagany jest obiekt wyjściowy bootstrapu albo 't0'" # boot/R/bootfuns.R: 1109 # stop("unable to calculate 'var.t0'") msgid "unable to calculate 'var.t0'" msgstr "nie można wyliczyć 'var.t0'" # boot/R/bootfuns.R: 1136 # warning("extreme order statistics used as endpoints") msgid "extreme order statistics used as endpoints" msgstr "ekstremalnie uporządkowana statystyka użyta jako punkty końcowe" # boot/R/bootfuns.R: 1172 # warning("variance required for studentized intervals") msgid "variance required for studentized intervals" msgstr "wariancja jest wymagana dla studentyzowanych przedziałów ufności" # boot/R/bootfuns.R: 1211 # stop("estimated adjustment 'w' is infinite") msgid "estimated adjustment 'w' is infinite" msgstr "oszacowana korekta 'w' wynosi nieskończoność" # boot/R/bootfuns.R: 1217 # stop("estimated adjustment 'a' is NA") msgid "estimated adjustment 'a' is NA" msgstr "oszacowana korekta 'a' wynosi 'NA'" # boot/R/bootfuns.R: 1235 # warning("only first element of 'index' used in 'abc.ci'") msgid "only first element of 'index' used in 'abc.ci'" msgstr "tylko pierwszy element 'index' został użyty w 'abc.ci'" # boot/R/bootfuns.R: 1300 # stop("missing values not allowed in 'data'") msgid "missing values not allowed in 'data'" msgstr "brakujące wartości nie są dozwolone w 'data'" # boot/R/bootfuns.R: 1302 # stop("unknown value of 'sim'") msgid "unknown value of 'sim'" msgstr "nieznana wartość 'sim'" # boot/R/bootfuns.R: 1316 # stop("'data' must be a matrix with at least 2 columns") # boot/R/bootfuns.R: 1318 # stop("'data' must be a matrix with at least 2 columns") msgid "'data' must be a matrix with at least 2 columns" msgstr "'data' musi być macierzą o co najmniej 2 kolumnach" # boot/R/bootfuns.R: 1320 # stop("'index' must contain 2 elements") msgid "'index' must contain 2 elements" msgstr "'index' musi zawierać 2 elementy" # boot/R/bootfuns.R: 1322 # warning("only first 2 elements of 'index' used") msgid "only first 2 elements of 'index' used" msgstr "tylko pierwsze 2 elementy 'index' zostały użyte" # boot/R/bootfuns.R: 1326 # stop("indices are incompatible with 'ncol(data)'") msgid "indices are incompatible with 'ncol(data)'" msgstr "indeksy są niezgodne z 'ncol(data)'" # boot/R/bootfuns.R: 1329 # stop("sim = \"weird\" cannot be used with a \"coxph\" object") msgid "sim = \"weird\" cannot be used with a \"coxph\" object" msgstr "sim=\"weird\" nie może być użyte z obiektem \"coxph\"" # boot/R/bootfuns.R: 1331 # warning(gettextf("only columns %s and %s of 'data' used", index[1L], index[2L]), domain = "R-boot") msgid "only columns %s and %s of 'data' used" msgstr "tylko kolumny %s oraz %s 'data' zostały użyte" # boot/R/bootfuns.R: 1336 # warning("no coefficients in Cox model -- model ignored") msgid "no coefficients in Cox model -- model ignored" msgstr "brak współczynników w modelu Coxa -- model został zignorowany" # boot/R/bootfuns.R: 1340 # stop("'F.surv' is required but missing") msgid "'F.surv' is required but missing" msgstr "'F.surv' jest wymagany, ale jest nieobecny" # boot/R/bootfuns.R: 1342 # stop("'G.surv' is required but missing") msgid "'G.surv' is required but missing" msgstr "'G.surv' jest wymagany, ale jest nieobecny" # boot/R/bootfuns.R: 1343 # stop("'strata' of wrong length") msgid "'strata' of wrong length" msgstr "'strata' o niepoprawnej długości" # boot/R/bootfuns.R: 1623 # stop("influence values cannot be found from a parametric bootstrap") msgid "influence values cannot be found from a parametric bootstrap" msgstr "" "wartości wpływu nie mogą zostać znalezione z parametrycznego bootstrapu" # boot/R/bootfuns.R: 1635 # stop("neither 'data' nor bootstrap object specified") msgid "neither 'data' nor bootstrap object specified" msgstr "brak 'data' lub określonego obiektu bootstrapu" # boot/R/bootfuns.R: 1637 # stop("neither 'statistic' nor bootstrap object specified") msgid "neither 'statistic' nor bootstrap object specified" msgstr "brak 'statistic' lub określonego obiektu bootstrapu" # boot/R/bootfuns.R: 1652 # stop("'stype' must be \"w\" for type=\"inf\"") msgid "'stype' must be \"w\" for type=\"inf\"" msgstr "'stype' musi być \"w\" dla type=\"inf\"" # boot/R/bootfuns.R: 1658 # warning("input 't' ignored; type=\"inf\"") msgid "input 't' ignored; type=\"inf\"" msgstr "wejście 't' zostało zignornowane; type=\"inf\"" # boot/R/bootfuns.R: 1663 # stop("bootstrap object needed for type=\"reg\"") msgid "bootstrap object needed for type=\"reg\"" msgstr "obiekt bootstrapu jest potrzebny dla type=\"reg\"" # boot/R/bootfuns.R: 1674 # warning("input 't' ignored; type=\"jack\"") msgid "input 't' ignored; type=\"jack\"" msgstr "wejście 't' zostało zignornowane; type=\"jack\"" # boot/R/bootfuns.R: 1682 # warning("input 't' ignored; type=\"pos\"") msgid "input 't' ignored; type=\"pos\"" msgstr "wejście 't' zostało zignorowane; type=\"pos\"" # boot/R/bootfuns.R: 1847 # warning("input 't0' ignored: neither 't' nor 'L' supplied") msgid "input 't0' ignored: neither 't' nor 'L' supplied" msgstr "wejście 't0' zostało zignornowane: nie dostarczono ani 't' ani 'L'" # boot/R/bootfuns.R: 1889 # stop("bootstrap output matrix missing") msgid "bootstrap output matrix missing" msgstr "brakuje wyjściowej macierzy bootstrapu" # boot/R/bootfuns.R: 1891 # stop("use 'boot.ci' for scalar parameters") msgid "use 'boot.ci' for scalar parameters" msgstr "użyj 'boot.ci' dla skalarnych parametrów" # boot/R/bootfuns.R: 1903 # warning("unable to achieve requested overall error rate") msgid "unable to achieve requested overall error rate" msgstr "nie można uzyskać zażądanego ogólnego wskaźnika błędu" # boot/R/bootfuns.R: 2090 # stop(gettextf("unable to find multiplier for %f", theta[i]), domain = "R-boot") msgid "unable to find multiplier for %f" msgstr "nie można znaleźć mnożnika dla %f" # boot/R/bootfuns.R: 2094 # stop("'theta' or 'lambda' required") msgid "'theta' or 'lambda' required" msgstr "'theta' lub 'lambda' są wymagane" # boot/R/bootfuns.R: 2122 # stop("0 elements not allowed in 'q'") msgid "0 elements not allowed in 'q'" msgstr "0 elementów nie jest dozwolone w 'q'" # boot/R/bootfuns.R: 2157 # stop("bootstrap replicates must be supplied") # boot/R/bootfuns.R: 2212 # stop("bootstrap replicates must be supplied") # boot/R/bootfuns.R: 2254 # stop("bootstrap replicates must be supplied") msgid "bootstrap replicates must be supplied" msgstr "bootstrapowane repliki muszą zostać dostarczone" # boot/R/bootfuns.R: 2161 # stop("either 'boot.out' or 'w' must be specified.") # boot/R/bootfuns.R: 2217 # stop("either 'boot.out' or 'w' must be specified.") # boot/R/bootfuns.R: 2258 # stop("either 'boot.out' or 'w' must be specified.") msgid "either 'boot.out' or 'w' must be specified." msgstr "jedno z 'boot.out' lub 'w' musi zostać dostarczone." # boot/R/bootfuns.R: 2292 # warning("only first column of 't' used") msgid "only first column of 't' used" msgstr "tylko pierwsza kolumna 't' została użyta" # boot/R/bootfuns.R: 2340 # stop("invalid value of 'sim' supplied") msgid "invalid value of 'sim' supplied" msgstr "dostarczono niepoprawną wartość 'sim'" # boot/R/bootfuns.R: 2342 # stop("'R' and 'theta' have incompatible lengths") msgid "'R' and 'theta' have incompatible lengths" msgstr "'R' oraz 'theta' mają niekompatybilne długości" # boot/R/bootfuns.R: 2344 # stop("R[1L] must be positive for frequency smoothing") msgid "R[1L] must be positive for frequency smoothing" msgstr "R[1L] musi być dodatnia dla wygładzania częstotliwości" # boot/R/bootfuns.R: 2350 # stop("'R' and 'alpha' have incompatible lengths") msgid "'R' and 'alpha' have incompatible lengths" msgstr "'R' oraz 'alpha' mają niekompatybilne długości" # boot/R/bootfuns.R: 2355 # warning("extreme values used for quantiles") msgid "extreme values used for quantiles" msgstr "ekstremalne wartości użyte dla kwantyli" # boot/R/bootfuns.R: 2364 # stop("'theta' must be supplied if R[1L] = 0") msgid "'theta' must be supplied if R[1L] = 0" msgstr "'theta' musi zostać dostarczona jeśli R[1L] = 0" # boot/R/bootfuns.R: 2366 # warning("'alpha' ignored; R[1L] = 0") msgid "'alpha' ignored; R[1L] = 0" msgstr "'alpha' zostało zignornowane; R[1L]=0" # boot/R/bootfuns.R: 2405 # stop("control methods undefined when 'boot.out' has weights") msgid "control methods undefined when 'boot.out' has weights" msgstr "metody kontroli nie są zdefiniowane gdy 'boot.out' posiada wagi" # boot/R/bootfuns.R: 2817 # stop(gettextf("number of columns of 'A' (%d) not equal to length of 'u' (%d)", d, length(u)), domain = "R-boot") msgid "number of columns of 'A' (%d) not equal to length of 'u' (%d)" msgstr "liczba kolumn 'A' (%d) nie równa się długości 'u' (%d)" # boot/R/bootfuns.R: 2820 # stop("either 'A' and 'u' or 'K.adj' and 'K2' must be supplied") msgid "either 'A' and 'u' or 'K.adj' and 'K2' must be supplied" msgstr "albo 'A' oraz 'u', albo 'K.adj' oraz 'K2' muszą zostać dostarczone" # boot/R/bootfuns.R: 2932 # stop("this type not implemented for Poisson") msgid "this type not implemented for Poisson" msgstr "ten typ nie jest zaimplementowany dla rozkładu Poisson'a" # boot/R/bootfuns.R: 2966 # stop("this type not implemented for Binary") msgid "this type not implemented for Binary" msgstr "ten typ nie jest zaimplementowany dla rozkładu Bernoulliego" # boot/R/bootfuns.R: 2994 # stop("one of 't' or 't0' required") msgid "one of 't' or 't0' required" msgstr "jeden z 't' lub 't0' jest wymagany" # boot/R/bootfuns.R: 3008 # stop("function 'u' missing") msgid "function 'u' missing" msgstr "brakuje funkcji 'u'" # boot/R/bootfuns.R: 3009 # stop("'u' must be a function") msgid "'u' must be a function" msgstr "'u' musi być funkcją" # boot/R/bootfuns.R: 3029 # stop("unable to find range") # boot/R/bootfuns.R: 3075 # stop("unable to find range") # boot/R/bootfuns.R: 3148 # stop("unable to find range") # boot/R/bootfuns.R: 3190 # stop("unable to find range") msgid "unable to find range" msgstr "nie można znaleźć zakresu" # boot/R/bootfuns.R: 3399 # stop("'R' must be positive") msgid "'R' must be positive" msgstr "'R' musi być dodatnie" # boot/R/bootfuns.R: 3411 # stop("invalid value of 'l'") msgid "invalid value of 'l'" msgstr "niepoprawna wartość 'l'" # boot/R/bootfuns.R: 3441 # stop("unrecognized value of 'sim'") msgid "unrecognized value of 'sim'" msgstr "nierozpoznana wartość 'sim'" # boot/R/bootfuns.R: 3475 # stop("multivariate time series not allowed") msgid "multivariate time series not allowed" msgstr "wielowymiarowe szeregi czasowe nie są dozwolone" # boot/R/bootpracs.R: 70 # stop(gettextf("likelihood never exceeds %f", lim), domain = "R-boot") msgid "likelihood never exceeds %f" msgstr "funkcja wiarygodności nigdy nie przekracza %f" # boot/R/bootpracs.R: 73 # stop(gettextf("likelihood exceeds %f at only one point", lim), domain = "R-boot") msgid "likelihood exceeds %f at only one point" msgstr "funkcja wiarygodności przekracza %f tylko w jednym punkcie" boot/po/R-ko.po0000644000176000001440000002473012465333515013050 0ustar ripleyusers# Korean translation for R boot package # Recommended/boot/po/R-ko.po # Maintainer: Brian Ripley # # This file is distributed under the same license as the R boot package. # Chel Hee Lee , 2013-2015. # # Reviewing process is completed (26-JAN-2015) # The original source code is reviewed (26-JAN-2015) # QC: PASS # Freezing on 06-FEB-2015 for R-3.1.3 # msgid "" msgstr "" "Project-Id-Version: boot 1.3-6\n" "POT-Creation-Date: 2012-10-11 15:21\n" "PO-Revision-Date: 2015-02-06 21:56-0600\n" "Last-Translator:Chel Hee Lee \n" "Language-Team: Chel Hee Lee \n" "Language: ko\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=UTF-8\n" "Content-Transfer-Encoding: 8bit\n" "Plural-Forms: nplurals=1; plural=0;\n" msgid "" "'simple=TRUE' is only valid for 'sim=\"ordinary\", stype=\"i\", n=0', so " "ignored" msgstr "" "'simple=TRUE'은 오로지 'sim=\"ordinary\", stype=\"i\", n=0'의 경우에만 유효하게 사용할 수 있으므로 무시됩니다." msgid "no data in call to 'boot'" msgstr "'boot'에 호출중인 데이터가 없습니다." msgid "negative value of 'm' supplied" msgstr "'m'에 음수가 입력되었습니다." msgid "length of 'm' incompatible with 'strata'" msgstr "'m'의 길이가 'strata'의 개수와 일치하지 않습니다." msgid "dimensions of 'R' and 'weights' do not match" msgstr "'R'과 'weights'의 차원이 서로 일치하지 않습니다." msgid "arguments are not all the same type of \"boot\" object" msgstr "입력된 인자들 중 적어도 하나 이상이 \"boot\" 클래스의 객체가 아닙니다." msgid "index array not defined for model-based resampling" msgstr "모델기반의 리샘플링(model-based resampling)을 위하여 정의된 인덱스 배열(index array)가 아닙니다." msgid "boot.array not implemented for this object" msgstr "boot.array 함수는 이 객체를 위하여 구현된 것이 아닙니다." msgid "array cannot be found for parametric bootstrap" msgstr "모수적 붓스트랩(parameteric bootstrap)을 위한 배열을 찾을 수 없습니다." msgid "%s distribution not supported: using normal instead" msgstr "%s 분포가 지원되지 않기 때문에 정규분포를 대신 사용합니다." msgid "only first element of 'index' used" msgstr "'index'의 첫번째 요소만이 사용되었습니다." msgid "'K' outside allowable range" msgstr "'K'는 허용범위 밖에 존재합니다." msgid "'K' has been set to %f" msgstr "'K'는 %f로 설정되었습니다." msgid "'t' and 't0' must be supplied together" msgstr "'t'와 't0'는 반드시 함께 입력되어야 합니다." msgid "index out of bounds; minimum index only used." msgstr "인덱스의 범위를 벗어났기 때문에 인덱스가 가지는 가장 작은 값(minimum index)을 사용합니다." msgid "'t' must of length %d" msgstr "'t'의 길이는 반드시 %d이어야 합니다." msgid "bootstrap variances needed for studentized intervals" msgstr "스튜던트화된 구간(studentized intervals)에 필요한 붓스트랩 분산(boostrap variances)입니다." msgid "BCa intervals not defined for time series bootstraps" msgstr "시계열 붓스트랩(time series bootstraps)을 위하여 정의된 BCa 구간(intervals)이 아닙니다." msgid "bootstrap output object or 't0' required" msgstr "붓스트랩으로부터 생성된 객체 또는 't0'가 필요합니다." msgid "unable to calculate 'var.t0'" msgstr "'var.t0'를 계산할 수 없습니다." msgid "extreme order statistics used as endpoints" msgstr "종점(endpoints)으로 사용된 극단적 순서통계량(extreme order statistics)입니다." msgid "variance required for studentized intervals" msgstr "스튜던트화된 구간(studentized intervals)에 요구되는 분산입니다." msgid "estimated adjustment 'w' is infinite" msgstr "추정된 보정값(adjustment) 'w'가 유한하지 않습니다." msgid "estimated adjustment 'a' is NA" msgstr "추정된 보정값(adjustment) 'a'이 유한하지 않습니다." msgid "only first element of 'index' used in 'abc.ci'" msgstr "'index'의 첫번째 요소만이 'abc.ci'에 사용되었습니다." msgid "missing values not allowed in 'data'" msgstr "'data'에는 결측치가 있어서는 안됩니다." msgid "unknown value of 'sim'" msgstr "'sim'의 값을 알 수 없습니다." msgid "'data' must be a matrix with at least 2 columns" msgstr "'data'는 반드시 적어도 2개의 열을 가지는 행렬이어야 합니다." msgid "'index' must contain 2 elements" msgstr "'index'는 반드시 2개의 요소를 가지고 있어야 합니다." msgid "only first 2 elements of 'index' used" msgstr "오로지 'index'의 첫번째 2개 요소들만을 사용합니다." msgid "indices are incompatible with 'ncol(data)'" msgstr "인덱스의 길이가 'ncol(data)'와 일치하지 않습니다." msgid "sim = \"weird\" cannot be used with a \"coxph\" object" msgstr "sim 인자는 \"coxph\"라는 객체를 입력받는 경우에 \"weird\"이라는 값을 가질 수 없습니다." msgid "only columns %s and %s of 'data' used" msgstr "'data'의 %s와 %s 번째 열만이 사용되었습니다." msgid "no coefficients in Cox model -- model ignored" msgstr "Cox 모델에 계수(coefficients)들이 없으므로 모델이 무시되었습니다" msgid "'F.surv' is required but missing" msgstr "'F.surv'이 필요한데 이를 찾을 수 없습니다." msgid "'G.surv' is required but missing" msgstr "'G.surv'가 필요한데 이를 찾을 수 없습니다." msgid "'strata' of wrong length" msgstr "'strata'의 길이가 올바르지 않습니다." msgid "influence values cannot be found from a parametric bootstrap" msgstr "모수적 붓스트랩(parametric bootstrap)으로부터 영향치(influence values)들을 찾을 수 없습니다." msgid "neither 'data' nor bootstrap object specified" msgstr "'data'와 붓스트랩 객체 모두 입력되지 않았습니다." msgid "neither 'statistic' nor bootstrap object specified" msgstr "'statistic'과 붓스트랩 객체 모두 입력되지 않았습니다." msgid "'stype' must be \"w\" for type=\"inf\"" msgstr "type 인자가 \"inf\"을 가지는 경우 'stype'는 반드시 \"w\"이어야 합니다." msgid "input 't' ignored; type=\"inf\"" msgstr "입력 't'가 무시되었으며 type=\"inf\"가 적용됩니다." msgid "bootstrap object needed for type=\"reg\"" msgstr "type 인자가 \"reg\"를 가지는 경우 붓스트랩 객체가 필요합니다." msgid "input 't' ignored; type=\"jack\"" msgstr "입력 't'가 무시되었으며 type=\"jack\"가 적용됩니다." msgid "input 't' ignored; type=\"pos\"" msgstr "입력 't'가 무시되었으며 type=\"pos\"가 적용됩니다." msgid "input 't0' ignored: neither 't' nor 'L' supplied" msgstr "'t'와 'L' 두 가지 모두가 입력되지 않아 입력 't0'는 무시되었습니다." msgid "bootstrap output matrix missing" msgstr "붓스트랩의 결과를 담고 있는 행렬을 찾을 수 없습니다." msgid "use 'boot.ci' for scalar parameters" msgstr "스칼라 파라미터(scalar parameters)인 경우 'boot.ci'를 사용하세요" msgid "unable to achieve requested overall error rate" msgstr "요청한 전체적인 오류률(overall error rate)을 계산할 수 없습니다." msgid "unable to find multiplier for %f" msgstr "%f에 대한 계수(multiplier)를 찾을 수 없습니다." msgid "'theta' or 'lambda' required" msgstr "'theta' 또는 'lambda'가 필요합니다." msgid "0 elements not allowed in 'q'" msgstr "'q'의 구성요소는 0 값일 수 없습니다." msgid "bootstrap replicates must be supplied" msgstr "붓스트랩 반복수(bootstrap replicates)는 반드시 주어져야 합니다." msgid "either 'boot.out' or 'w' must be specified." msgstr "'boot.out' 또는 'w' 중 하나는 반드시 주어져야 합니다." msgid "only first column of 't' used" msgstr "오로지 't'의 첫번째 열만이 사용됩니다." msgid "invalid value of 'sim' supplied" msgstr "'sim'에 주어진 값이 올바르지 않습니다." msgid "'R' and 'theta' have incompatible lengths" msgstr "'R'과 'theta'의 길이가 서로 맞지 않습니다." msgid "R[1L] must be positive for frequency smoothing" msgstr "빈도 스무딩(frequency smoothing)을 사용하기 위해서는 반드시 R[1L]의 값은 양수이어야 합니다." msgid "'R' and 'alpha' have incompatible lengths" msgstr "'R'과 'alpha'의 길이가 서로 맞지 않습니다." msgid "extreme values used for quantiles" msgstr "분위수(quantiles)에 사용된 극단값(extreme values)들입니다." msgid "'theta' must be supplied if R[1L] = 0" msgstr "만약 R[1L] = 0이라면 'theta'의 값이 반드시 주어져야 합니다." msgid "'alpha' ignored; R[1L] = 0" msgstr "입력된 'alpha'의 값이 무시되었으며 R[1L] = 0이 적용됩니다." msgid "control methods undefined when 'boot.out' has weights" msgstr "'boot.out'가 가중치(weights)를 가지는 경우에 대한 제어방법(control methods)가 정의되지 않았습니다." msgid "number of columns of 'A' (%d) not equal to length of 'u' (%d)" msgstr "'A'의 열의 개수 (%d)가 'u'의 길이 (%d)와 일치하지 않습니다." msgid "either 'A' and 'u' or 'K.adj' and 'K2' must be supplied" msgstr "'A'와 'u' 또는 'K.adj'와 'K2' 둘 중의 한 쌍은 반드시 제공되어야 합니다." msgid "this type not implemented for Poisson" msgstr "본 유형(type)은 포아송(Poisson)의 경우에는 아직 구현되지 않았습니다." msgid "this type not implemented for Binary" msgstr "본 유형(type)은 이진데이터(Binary)의 경우에는 아직 구현되지 않았습니다." msgid "one of 't' or 't0' required" msgstr "'t' 또는 't0' 중 하나가 필요합니다." msgid "function 'u' missing" msgstr "함수 'u'가 입력되지 않았습니다." msgid "'u' must be a function" msgstr "'u'는 반드시 함수이어야 합니다." msgid "unable to find range" msgstr "범위(range)를 구할 수 없습니다." msgid "'R' must be positive" msgstr "'R'은 반드시 양수이어야 합니다." msgid "invalid value of 'l'" msgstr "'l'의 값이 올바르지 않습니다." msgid "unrecognized value of 'sim'" msgstr "'sim'의 값을 알 수 없습니다." msgid "multivariate time series not allowed" msgstr "다변량 시계열(multivariate time series)는 허용되지 않습니다." msgid "likelihood never exceeds %f" msgstr "우도(likelhiood)가 %f를 절대로 넘지 않습니다." msgid "likelihood exceeds %f at only one point" msgstr "우도(likelihood)가 오로지 한 점에서만 %f를 넘어섭니다." boot/po/R-ru.po0000644000176000001440000002113412122137701013045 0ustar ripleyusers# Russian translations for R # R # # Copyright (C) 2007 The R Foundation # This file is distributed under the same license as the R package. # Alexey Shipunov 2009 # msgid "" msgstr "" "Project-Id-Version: R 2.10.0\n" "Report-Msgid-Bugs-To: bugs@r-project.org\n" "POT-Creation-Date: 2012-10-11 15:21\n" "PO-Revision-Date: 2013-03-19 14:42-0600\n" "Last-Translator: Alexey Shipunov \n" "Language-Team: Russian\n" "Language: \n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=KOI8-R\n" "Content-Transfer-Encoding: 8bit\n" "X-Poedit-Language: Russian\n" "Plural-Forms: nplurals=3; plural=(n%10==1 && n%100!=11 ? 0 : n%10>=2 && n%10<=4 && (n%100<10 || n%100>=20) ? 1 : 2);\n" msgid "'simple=TRUE' is only valid for 'sim=\"ordinary\", stype=\"i\", n=0', so ignored" msgstr "'simple=TRUE' 'sim=\"ordinary\", stype=\"i\", n=0, " msgid "no data in call to 'boot'" msgstr " 'boot'" msgid "negative value of 'm' supplied" msgstr " 'm'" msgid "length of 'm' incompatible with 'strata'" msgstr " 'm' 'strata'" msgid "dimensions of 'R' and 'weights' do not match" msgstr " 'R' 'weights' " msgid "arguments are not all the same type of \"boot\" object" msgstr " \"boot\" " msgid "index array not defined for model-based resampling" msgstr " " msgid "boot.array not implemented for this object" msgstr "'boot.array' " msgid "array cannot be found for parametric bootstrap" msgstr " " msgid "%s distribution not supported: using normal instead" msgstr " %s , " msgid "only first element of 'index' used" msgstr " " msgid "'K' outside allowable range" msgstr "'K' " msgid "'K' has been set to %f" msgstr "'K' %f" msgid "'t' and 't0' must be supplied together" msgstr "'t' 't0' " msgid "index out of bounds; minimum index only used." msgstr " ; ." msgid "'t' must of length %d" msgstr "'t' %d" msgid "bootstrap variances needed for studentized intervals" msgstr "- -" msgid "BCa intervals not defined for time series bootstraps" msgstr "BCa " msgid "bootstrap output object or 't0' required" msgstr " 't0'" msgid "unable to calculate 'var.t0'" msgstr " 'var.t0'" msgid "extreme order statistics used as endpoints" msgstr "'extreme order statistics' " msgid "variance required for studentized intervals" msgstr " - " msgid "estimated adjustment 'w' is infinite" msgstr " 'w' -- infinite" msgid "estimated adjustment 'a' is NA" msgstr " 'a' -- NA" msgid "only first element of 'index' used in 'abc.ci'" msgstr " 'abc.ci'" msgid "missing values not allowed in 'data'" msgstr " " msgid "unknown value of 'sim'" msgstr " 'sim'" msgid "'data' must be a matrix with at least 2 columns" msgstr " 2 " msgid "'index' must contain 2 elements" msgstr " 2 " msgid "only first 2 elements of 'index' used" msgstr " 2 " msgid "indices are incompatible with 'ncol(data)'" msgstr " 'ncol(data)'" msgid "sim = \"weird\" cannot be used with a \"coxph\" object" msgstr "sim=\"weird\" \"coxph\"" msgid "only columns %s and %s of 'data' used" msgstr " %s %s " msgid "no coefficients in Cox model -- model ignored" msgstr " 'Cox' -- " msgid "'F.surv' is required but missing" msgstr "'F.surv' , " msgid "'G.surv' is required but missing" msgstr "'G.surv' , " msgid "'strata' of wrong length" msgstr "'strata' " msgid "influence values cannot be found from a parametric bootstrap" msgstr " " msgid "neither 'data' nor bootstrap object specified" msgstr " -" msgid "neither 'statistic' nor bootstrap object specified" msgstr " 'statistic' -" msgid "'stype' must be \"w\" for type=\"inf\"" msgstr "'stype' \"w\" type=\"inf\"" msgid "input 't' ignored; type=\"inf\"" msgstr " 't' ; type=\"inf\"" msgid "bootstrap object needed for type=\"reg\"" msgstr " type=\"reg\" -" msgid "input 't' ignored; type=\"jack\"" msgstr " 't' ; type=\"jack\"" msgid "input 't' ignored; type=\"pos\"" msgstr " 't' ; type=\"pos\"" msgid "input 't0' ignored: neither 't' nor 'L' supplied" msgstr " 't0' : 't', 'L'" msgid "bootstrap output matrix missing" msgstr " -" msgid "use 'boot.ci' for scalar parameters" msgstr " 'boot.ci' " msgid "unable to achieve requested overall error rate" msgstr " " msgid "unable to find multiplier for %f" msgstr " %f" msgid "'theta' or 'lambda' required" msgstr " 'theta' 'lambda'" msgid "0 elements not allowed in 'q'" msgstr "0 'q' " msgid "bootstrap replicates must be supplied" msgstr " -" msgid "either 'boot.out' or 'w' must be specified." msgstr " 'boot.out', 'w'." msgid "only first column of 't' used" msgstr " 't'" msgid "invalid value of 'sim' supplied" msgstr " 'sim'" msgid "'R' and 'theta' have incompatible lengths" msgstr " 'R' 'theta' -- " msgid "R[1L] must be positive for frequency smoothing" msgstr "R[1L] " msgid "'R' and 'alpha' have incompatible lengths" msgstr "'R' 'alpha' " msgid "extreme values used for quantiles" msgstr " " msgid "'theta' must be supplied if R[1L] = 0" msgstr " 'theta', R[1L] = 0" msgid "'alpha' ignored; R[1L] = 0" msgstr "'alpha' ; R[1L]=0" msgid "control methods undefined when 'boot.out' has weights" msgstr " 'boot.out' " msgid "number of columns of 'A' (%d) not equal to length of 'u' (%d)" msgstr " 'A' (%d) 'u' (%d)" msgid "either 'A' and 'u' or 'K.adj' and 'K2' must be supplied" msgstr " 'A' 'u', 'K.adj' 'K2'" msgid "this type not implemented for Poisson" msgstr " " msgid "this type not implemented for Binary" msgstr " " msgid "one of 't' or 't0' required" msgstr " 't' 't0'" msgid "function 'u' missing" msgstr " 'u' " msgid "'u' must be a function" msgstr "'u' " msgid "unable to find range" msgstr " " msgid "'R' must be positive" msgstr "'R' " msgid "invalid value of 'l'" msgstr " 'l'" msgid "unrecognized value of 'sim'" msgstr " 'sim'" msgid "multivariate time series not allowed" msgstr " " msgid "likelihood never exceeds %f" msgstr " %f" msgid "likelihood exceeds %f at only one point" msgstr " %f " #~ msgid "only columns" #~ msgstr " " #~ msgid "and" #~ msgstr "" #~ msgid "of data used" #~ msgstr " " #~ msgid "number of columns of A (" #~ msgstr " A (" #~ msgid ")" #~ msgstr ")" #~ msgid "at only one point" #~ msgstr " " #~ msgid "invalid proportions input" #~ msgstr " " #~ msgid "irregular time series not allowed" #~ msgstr " " boot/inst/0000755000176000001440000000000012306331347012224 5ustar ripleyusersboot/inst/po/0000755000176000001440000000000012121561347012642 5ustar ripleyusersboot/inst/po/pl/0000755000176000001440000000000011772542457013271 5ustar ripleyusersboot/inst/po/pl/LC_MESSAGES/0000755000176000001440000000000011772542457015056 5ustar ripleyusersboot/inst/po/pl/LC_MESSAGES/R-boot.mo0000644000176000001440000002132412465333606016551 0ustar ripleyusersNk3  6)R)|/L&s"&% / F 4d . 4 . *, &W ~ ( % 4 5" ,X 7 +  $ *- !X z 2 - * < X v  0   ('Bj$$-2-P~=%%>"\.2$%.,[ x#+&1GX**1/1a&4!_("$.1!Mo%J:;2;n:/'4=1rG@--D[4"/B()kK1$'KL./-D%j(.;./G0w!/4A/q:"/1 *Q -| 8 4 <!9U!=!!%!"."G"*e"C"L9JB*5= N+1'47 - MAC?8D;@,2>H.$#/6I 0E G:F&)%( K3!"<%s distribution not supported: using normal instead'F.surv' is required but missing'G.surv' is required but missing'K' has been set to %f'K' outside allowable range'R' and 'alpha' have incompatible lengths'R' and 'theta' have incompatible lengths'R' must be positive'alpha' ignored; R[1L] = 0'data' must be a matrix with at least 2 columns'index' must contain 2 elements'simple=TRUE' is only valid for 'sim="ordinary", stype="i", n=0', so ignored'strata' of wrong length'stype' must be "w" for type="inf"'t' and 't0' must be supplied together't' must of length %d'theta' must be supplied if R[1L] = 0'theta' or 'lambda' required'u' must be a function0 elements not allowed in 'q'BCa intervals not defined for time series bootstrapsR[1L] must be positive for frequency smoothingarguments are not all the same type of "boot" objectarray cannot be found for parametric bootstrapboot.array not implemented for this objectbootstrap object needed for type="reg"bootstrap output matrix missingbootstrap output object or 't0' requiredbootstrap replicates must be suppliedbootstrap variances needed for studentized intervalscontrol methods undefined when 'boot.out' has weightsdimensions of 'R' and 'weights' do not matcheither 'A' and 'u' or 'K.adj' and 'K2' must be suppliedeither 'boot.out' or 'w' must be specified.estimated adjustment 'a' is NAestimated adjustment 'w' is infiniteextreme order statistics used as endpointsextreme values used for quantilesfunction 'u' missingindex array not defined for model-based resamplingindex out of bounds; minimum index only used.indices are incompatible with 'ncol(data)'influence values cannot be found from a parametric bootstrapinput 't' ignored; type="inf"input 't' ignored; type="jack"input 't' ignored; type="pos"input 't0' ignored: neither 't' nor 'L' suppliedinvalid value of 'l'invalid value of 'sim' suppliedlength of 'm' incompatible with 'strata'likelihood exceeds %f at only one pointlikelihood never exceeds %fmissing values not allowed in 'data'multivariate time series not allowednegative value of 'm' suppliedneither 'data' nor bootstrap object specifiedneither 'statistic' nor bootstrap object specifiedno coefficients in Cox model -- model ignoredno data in call to 'boot'number of columns of 'A' (%d) not equal to length of 'u' (%d)one of 't' or 't0' requiredonly columns %s and %s of 'data' usedonly first 2 elements of 'index' usedonly first column of 't' usedonly first element of 'index' usedonly first element of 'index' used in 'abc.ci'sim = "weird" cannot be used with a "coxph" objectthis type not implemented for Binarythis type not implemented for Poissonunable to achieve requested overall error rateunable to calculate 'var.t0'unable to find multiplier for %funable to find rangeunknown value of 'sim'unrecognized value of 'sim'use 'boot.ci' for scalar parametersvariance required for studentized intervalsProject-Id-Version: boot 1.3-10 Report-Msgid-Bugs-To: bugs@r-project.org POT-Creation-Date: 2013-03-20 07:24 PO-Revision-Date: Last-Translator: Łukasz Daniel Language-Team: Łukasz Daniel Language: pl_PL MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit na-Revision-Date: 2012-05-29 07:55+0100 Plural-Forms: nplurals=3; plural=(n==1 ? 0 : n%10>=2 && n%10<=4 && (n%100<10 || n%100>=20) ? 1 : 2); X-Poedit-SourceCharset: iso-8859-1 X-Generator: Poedit 1.5.4 rozkład %s nie jest wspierany: w zamian używanie rozkładu normalnego'F.surv' jest wymagany, ale jest nieobecny'G.surv' jest wymagany, ale jest nieobecny'K' został ustawiony na %f'K' poza dozwolonym zakresem'R' oraz 'alpha' mają niekompatybilne długości'R' oraz 'theta' mają niekompatybilne długości'R' musi być dodatnie'alpha' zostało zignornowane; R[1L]=0'data' musi być macierzą o co najmniej 2 kolumnach'index' musi zawierać 2 elementy'simple=TRUE' jest poprawne jedynie dla 'sim="ordinary", stype="i", n=0', tak więc zignorowano'strata' o niepoprawnej długości'stype' musi być "w" dla type="inf"'t' oraz 't0' muszą zostać dostarczone razem't' musi być długości %d'theta' musi zostać dostarczona jeśli R[1L] = 0'theta' lub 'lambda' są wymagane'u' musi być funkcją0 elementów nie jest dozwolone w 'q'przedziały BC nie są zdefiniowane dla bootstrapowych szeregów czasowychR[1L] musi być dodatnia dla wygładzania częstotliwościargumenty nie są wszystkie tego samego typu obiektu 'boot'nie można znaleźć tablicy dla parametrycznego bootstrapu'boot.array' nie został zaimplementowany dla tego obiektuobiekt bootstrapu jest potrzebny dla type="reg"brakuje wyjściowej macierzy bootstrapuwymagany jest obiekt wyjściowy bootstrapu albo 't0'bootstrapowane repliki muszą zostać dostarczonepotrzebne są bootstrapowe wariancje dla studentyzowanych przedziałówmetody kontroli nie są zdefiniowane gdy 'boot.out' posiada wagiwymiary 'R' oraz 'weights' nie zgadzają sięalbo 'A' oraz 'u', albo 'K.adj' oraz 'K2' muszą zostać dostarczonejedno z 'boot.out' lub 'w' musi zostać dostarczone.oszacowana korekta 'a' wynosi 'NA'oszacowana korekta 'w' wynosi nieskończonośćekstremalnie uporządkowana statystyka użyta jako punkty końcoweekstremalne wartości użyte dla kwantylibrakuje funkcji 'u'tablica indeksów nie jest zdefiniowana dla próbkowania opartego na modeluindeks poza zakresem; użyto minimalnego indeksu.indeksy są niezgodne z 'ncol(data)'wartości wpływu nie mogą zostać znalezione z parametrycznego bootstrapuwejście 't' zostało zignornowane; type="inf"wejście 't' zostało zignornowane; type="jack"wejście 't' zostało zignorowane; type="pos"wejście 't0' zostało zignornowane: nie dostarczono ani 't' ani 'L'niepoprawna wartość 'l'dostarczono niepoprawną wartość 'sim'długość 'm' jest niekompatybilna z 'strata'funkcja wiarygodności przekracza %f tylko w jednym punkciefunkcja wiarygodności nigdy nie przekracza %fbrakujące wartości nie są dozwolone w 'data'wielowymiarowe szeregi czasowe nie są dozwolonedostarczono ujemną wartość 'm'brak 'data' lub określonego obiektu bootstrapubrak 'statistic' lub określonego obiektu bootstrapubrak współczynników w modelu Coxa -- model został zignorowanybrak danych w wywołaniu 'boot'liczba kolumn 'A' (%d) nie równa się długości 'u' (%d)jeden z 't' lub 't0' jest wymaganytylko kolumny %s oraz %s 'data' zostały użytetylko pierwsze 2 elementy 'index' zostały użytetylko pierwsza kolumna 't' została użytatylko pierwszy element 'index' został użytytylko pierwszy element 'index' został użyty w 'abc.ci'sim="weird" nie może być użyte z obiektem "coxph"ten typ nie jest zaimplementowany dla rozkładu Bernoulliegoten typ nie jest zaimplementowany dla rozkładu Poisson'anie można uzyskać zażądanego ogólnego wskaźnika błędunie można wyliczyć 'var.t0'nie można znaleźć mnożnika dla %fnie można znaleźć zakresunieznana wartość 'sim'nierozpoznana wartość 'sim'użyj 'boot.ci' dla skalarnych parametrówwariancja jest wymagana dla studentyzowanych przedziałów ufnościboot/inst/po/en@quot/0000755000176000001440000000000011663151666014266 5ustar ripleyusersboot/inst/po/en@quot/LC_MESSAGES/0000755000176000001440000000000011772542457016056 5ustar ripleyusersboot/inst/po/en@quot/LC_MESSAGES/R-boot.mo0000644000176000001440000001777412465333606017567 0ustar ripleyusersNk3  6)R)|/L&s"&% / F 4d . 4 . *, &W ~ ( % 4 5" ,X 7 +  $ *- !X z 2 - * < X v  0   ('Bj$$-2-P~=%%>"\.2$%.,[ x#+613h$$1!1S3#Tj&.)$!F!a4.4.*K&v,%49E4G3"0(S*|!2-.C<r!"!<S#l0'($."S1v6- E+#q))!& 622i$%.  7Xm'+L9JB*5= N+1'47 - MAC?8D;@,2>H.$#/6I 0E G:F&)%( K3!"<%s distribution not supported: using normal instead'F.surv' is required but missing'G.surv' is required but missing'K' has been set to %f'K' outside allowable range'R' and 'alpha' have incompatible lengths'R' and 'theta' have incompatible lengths'R' must be positive'alpha' ignored; R[1L] = 0'data' must be a matrix with at least 2 columns'index' must contain 2 elements'simple=TRUE' is only valid for 'sim="ordinary", stype="i", n=0', so ignored'strata' of wrong length'stype' must be "w" for type="inf"'t' and 't0' must be supplied together't' must of length %d'theta' must be supplied if R[1L] = 0'theta' or 'lambda' required'u' must be a function0 elements not allowed in 'q'BCa intervals not defined for time series bootstrapsR[1L] must be positive for frequency smoothingarguments are not all the same type of "boot" objectarray cannot be found for parametric bootstrapboot.array not implemented for this objectbootstrap object needed for type="reg"bootstrap output matrix missingbootstrap output object or 't0' requiredbootstrap replicates must be suppliedbootstrap variances needed for studentized intervalscontrol methods undefined when 'boot.out' has weightsdimensions of 'R' and 'weights' do not matcheither 'A' and 'u' or 'K.adj' and 'K2' must be suppliedeither 'boot.out' or 'w' must be specified.estimated adjustment 'a' is NAestimated adjustment 'w' is infiniteextreme order statistics used as endpointsextreme values used for quantilesfunction 'u' missingindex array not defined for model-based resamplingindex out of bounds; minimum index only used.indices are incompatible with 'ncol(data)'influence values cannot be found from a parametric bootstrapinput 't' ignored; type="inf"input 't' ignored; type="jack"input 't' ignored; type="pos"input 't0' ignored: neither 't' nor 'L' suppliedinvalid value of 'l'invalid value of 'sim' suppliedlength of 'm' incompatible with 'strata'likelihood exceeds %f at only one pointlikelihood never exceeds %fmissing values not allowed in 'data'multivariate time series not allowednegative value of 'm' suppliedneither 'data' nor bootstrap object specifiedneither 'statistic' nor bootstrap object specifiedno coefficients in Cox model -- model ignoredno data in call to 'boot'number of columns of 'A' (%d) not equal to length of 'u' (%d)one of 't' or 't0' requiredonly columns %s and %s of 'data' usedonly first 2 elements of 'index' usedonly first column of 't' usedonly first element of 'index' usedonly first element of 'index' used in 'abc.ci'sim = "weird" cannot be used with a "coxph" objectthis type not implemented for Binarythis type not implemented for Poissonunable to achieve requested overall error rateunable to calculate 'var.t0'unable to find multiplier for %funable to find rangeunknown value of 'sim'unrecognized value of 'sim'use 'boot.ci' for scalar parametersvariance required for studentized intervalsProject-Id-Version: boot 1.3-14 POT-Creation-Date: 2015-02-07 06:58 PO-Revision-Date: 2015-02-07 06:58 Last-Translator: Automatically generated Language-Team: none MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Language: en Plural-Forms: nplurals=2; plural=(n != 1); %s distribution not supported: using normal instead‘F.surv’ is required but missing‘G.surv’ is required but missing‘K’ has been set to %f‘K’ outside allowable range‘R’ and ‘alpha’ have incompatible lengths‘R’ and ‘theta’ have incompatible lengths‘R’ must be positive‘alpha’ ignored; R[1L] = 0‘data’ must be a matrix with at least 2 columns‘index’ must contain 2 elements‘simple=TRUE’ is only valid for ‘sim="ordinary", stype="i", n=0’, so ignored‘strata’ of wrong length‘stype’ must be "w" for type="inf"‘t’ and ‘t0’ must be supplied together‘t’ must of length %d‘theta’ must be supplied if R[1L] = 0‘theta’ or ‘lambda’ required‘u’ must be a function0 elements not allowed in ‘q’BCa intervals not defined for time series bootstrapsR[1L] must be positive for frequency smoothingarguments are not all the same type of "boot" objectarray cannot be found for parametric bootstrapboot.array not implemented for this objectbootstrap object needed for type="reg"bootstrap output matrix missingbootstrap output object or ‘t0’ requiredbootstrap replicates must be suppliedbootstrap variances needed for studentized intervalscontrol methods undefined when ‘boot.out’ has weightsdimensions of ‘R’ and ‘weights’ do not matcheither ‘A’ and ‘u’ or ‘K.adj’ and ‘K2’ must be suppliedeither ‘boot.out’ or ‘w’ must be specified.estimated adjustment ‘a’ is NAestimated adjustment ‘w’ is infiniteextreme order statistics used as endpointsextreme values used for quantilesfunction ‘u’ missingindex array not defined for model-based resamplingindex out of bounds; minimum index only used.indices are incompatible with ‘ncol(data)’influence values cannot be found from a parametric bootstrapinput ‘t’ ignored; type="inf"input ‘t’ ignored; type="jack"input ‘t’ ignored; type="pos"input ‘t0’ ignored: neither ‘t’ nor ‘L’ suppliedinvalid value of ‘l’invalid value of ‘sim’ suppliedlength of ‘m’ incompatible with ‘strata’likelihood exceeds %f at only one pointlikelihood never exceeds %fmissing values not allowed in ‘data’multivariate time series not allowednegative value of ‘m’ suppliedneither ‘data’ nor bootstrap object specifiedneither ‘statistic’ nor bootstrap object specifiedno coefficients in Cox model -- model ignoredno data in call to ‘boot’number of columns of ‘A’ (%d) not equal to length of ‘u’ (%d)one of ‘t’ or ‘t0’ requiredonly columns %s and %s of ‘data’ usedonly first 2 elements of ‘index’ usedonly first column of ‘t’ usedonly first element of ‘index’ usedonly first element of ‘index’ used in ‘abc.ci’sim = "weird" cannot be used with a "coxph" objectthis type not implemented for Binarythis type not implemented for Poissonunable to achieve requested overall error rateunable to calculate ‘var.t0’unable to find multiplier for %funable to find rangeunknown value of ‘sim’unrecognized value of ‘sim’use ‘boot.ci’ for scalar parametersvariance required for studentized intervalsboot/inst/po/ko/0000755000176000001440000000000012121561347013253 5ustar ripleyusersboot/inst/po/ko/LC_MESSAGES/0000755000176000001440000000000012121561347015040 5ustar ripleyusersboot/inst/po/ko/LC_MESSAGES/R-boot.mo0000644000176000001440000002442712465333606016556 0ustar ripleyusersNk3  6)R)|/L&s"&% / F 4d . 4 . *, &W ~ ( % 4 5" ,X 7 +  $ *- !X z 2 - * < X v  0   ('Bj$$-2-P~=%%>"\.2$%.,[ x#+_1R88#V+z77+LBQE'0U:31nM++1Flxw^]ZKPcJIQIo} ?\E&AlA_HP+zy@As?p @ ? Y1!(!4!A!G+":s"4"K"'/#BW#G#U#18$Mj$.$:$D"%5g%8%D%m&`&[&SF''';')'%((%N(Pt(Q(L9JB*5= N+1'47 - MAC?8D;@,2>H.$#/6I 0E G:F&)%( K3!"<%s distribution not supported: using normal instead'F.surv' is required but missing'G.surv' is required but missing'K' has been set to %f'K' outside allowable range'R' and 'alpha' have incompatible lengths'R' and 'theta' have incompatible lengths'R' must be positive'alpha' ignored; R[1L] = 0'data' must be a matrix with at least 2 columns'index' must contain 2 elements'simple=TRUE' is only valid for 'sim="ordinary", stype="i", n=0', so ignored'strata' of wrong length'stype' must be "w" for type="inf"'t' and 't0' must be supplied together't' must of length %d'theta' must be supplied if R[1L] = 0'theta' or 'lambda' required'u' must be a function0 elements not allowed in 'q'BCa intervals not defined for time series bootstrapsR[1L] must be positive for frequency smoothingarguments are not all the same type of "boot" objectarray cannot be found for parametric bootstrapboot.array not implemented for this objectbootstrap object needed for type="reg"bootstrap output matrix missingbootstrap output object or 't0' requiredbootstrap replicates must be suppliedbootstrap variances needed for studentized intervalscontrol methods undefined when 'boot.out' has weightsdimensions of 'R' and 'weights' do not matcheither 'A' and 'u' or 'K.adj' and 'K2' must be suppliedeither 'boot.out' or 'w' must be specified.estimated adjustment 'a' is NAestimated adjustment 'w' is infiniteextreme order statistics used as endpointsextreme values used for quantilesfunction 'u' missingindex array not defined for model-based resamplingindex out of bounds; minimum index only used.indices are incompatible with 'ncol(data)'influence values cannot be found from a parametric bootstrapinput 't' ignored; type="inf"input 't' ignored; type="jack"input 't' ignored; type="pos"input 't0' ignored: neither 't' nor 'L' suppliedinvalid value of 'l'invalid value of 'sim' suppliedlength of 'm' incompatible with 'strata'likelihood exceeds %f at only one pointlikelihood never exceeds %fmissing values not allowed in 'data'multivariate time series not allowednegative value of 'm' suppliedneither 'data' nor bootstrap object specifiedneither 'statistic' nor bootstrap object specifiedno coefficients in Cox model -- model ignoredno data in call to 'boot'number of columns of 'A' (%d) not equal to length of 'u' (%d)one of 't' or 't0' requiredonly columns %s and %s of 'data' usedonly first 2 elements of 'index' usedonly first column of 't' usedonly first element of 'index' usedonly first element of 'index' used in 'abc.ci'sim = "weird" cannot be used with a "coxph" objectthis type not implemented for Binarythis type not implemented for Poissonunable to achieve requested overall error rateunable to calculate 'var.t0'unable to find multiplier for %funable to find rangeunknown value of 'sim'unrecognized value of 'sim'use 'boot.ci' for scalar parametersvariance required for studentized intervalsProject-Id-Version: boot 1.3-6 POT-Creation-Date: 2012-10-11 15:21 PO-Revision-Date: 2015-02-06 21:56-0600 Last-Translator:Chel Hee Lee Language-Team: Chel Hee Lee Language: ko MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Plural-Forms: nplurals=1; plural=0; %s 분포가 지원되지 않기 때문에 정규분포를 대신 사용합니다.'F.surv'이 필요한데 이를 찾을 수 없습니다.'G.surv'가 필요한데 이를 찾을 수 없습니다.'K'는 %f로 설정되었습니다.'K'는 허용범위 밖에 존재합니다.'R'과 'alpha'의 길이가 서로 맞지 않습니다.'R'과 'theta'의 길이가 서로 맞지 않습니다.'R'은 반드시 양수이어야 합니다.입력된 'alpha'의 값이 무시되었으며 R[1L] = 0이 적용됩니다.'data'는 반드시 적어도 2개의 열을 가지는 행렬이어야 합니다.'index'는 반드시 2개의 요소를 가지고 있어야 합니다.'simple=TRUE'은 오로지 'sim="ordinary", stype="i", n=0'의 경우에만 유효하게 사용할 수 있으므로 무시됩니다.'strata'의 길이가 올바르지 않습니다.type 인자가 "inf"을 가지는 경우 'stype'는 반드시 "w"이어야 합니다.'t'와 't0'는 반드시 함께 입력되어야 합니다.'t'의 길이는 반드시 %d이어야 합니다.만약 R[1L] = 0이라면 'theta'의 값이 반드시 주어져야 합니다.'theta' 또는 'lambda'가 필요합니다.'u'는 반드시 함수이어야 합니다.'q'의 구성요소는 0 값일 수 없습니다.시계열 붓스트랩(time series bootstraps)을 위하여 정의된 BCa 구간(intervals)이 아닙니다.빈도 스무딩(frequency smoothing)을 사용하기 위해서는 반드시 R[1L]의 값은 양수이어야 합니다.입력된 인자들 중 적어도 하나 이상이 "boot" 클래스의 객체가 아닙니다.모수적 붓스트랩(parameteric bootstrap)을 위한 배열을 찾을 수 없습니다.boot.array 함수는 이 객체를 위하여 구현된 것이 아닙니다.type 인자가 "reg"를 가지는 경우 붓스트랩 객체가 필요합니다.붓스트랩의 결과를 담고 있는 행렬을 찾을 수 없습니다.붓스트랩으로부터 생성된 객체 또는 't0'가 필요합니다.붓스트랩 반복수(bootstrap replicates)는 반드시 주어져야 합니다.스튜던트화된 구간(studentized intervals)에 필요한 붓스트랩 분산(boostrap variances)입니다.'boot.out'가 가중치(weights)를 가지는 경우에 대한 제어방법(control methods)가 정의되지 않았습니다.'R'과 'weights'의 차원이 서로 일치하지 않습니다.'A'와 'u' 또는 'K.adj'와 'K2' 둘 중의 한 쌍은 반드시 제공되어야 합니다.'boot.out' 또는 'w' 중 하나는 반드시 주어져야 합니다.추정된 보정값(adjustment) 'a'이 유한하지 않습니다.추정된 보정값(adjustment) 'w'가 유한하지 않습니다.종점(endpoints)으로 사용된 극단적 순서통계량(extreme order statistics)입니다.분위수(quantiles)에 사용된 극단값(extreme values)들입니다.함수 'u'가 입력되지 않았습니다.모델기반의 리샘플링(model-based resampling)을 위하여 정의된 인덱스 배열(index array)가 아닙니다.인덱스의 범위를 벗어났기 때문에 인덱스가 가지는 가장 작은 값(minimum index)을 사용합니다.인덱스의 길이가 'ncol(data)'와 일치하지 않습니다.모수적 붓스트랩(parametric bootstrap)으로부터 영향치(influence values)들을 찾을 수 없습니다.입력 't'가 무시되었으며 type="inf"가 적용됩니다.입력 't'가 무시되었으며 type="jack"가 적용됩니다.입력 't'가 무시되었으며 type="pos"가 적용됩니다.'t'와 'L' 두 가지 모두가 입력되지 않아 입력 't0'는 무시되었습니다.'l'의 값이 올바르지 않습니다.'sim'에 주어진 값이 올바르지 않습니다.'m'의 길이가 'strata'의 개수와 일치하지 않습니다.우도(likelihood)가 오로지 한 점에서만 %f를 넘어섭니다.우도(likelhiood)가 %f를 절대로 넘지 않습니다.'data'에는 결측치가 있어서는 안됩니다.다변량 시계열(multivariate time series)는 허용되지 않습니다.'m'에 음수가 입력되었습니다.'data'와 붓스트랩 객체 모두 입력되지 않았습니다.'statistic'과 붓스트랩 객체 모두 입력되지 않았습니다.Cox 모델에 계수(coefficients)들이 없으므로 모델이 무시되었습니다'boot'에 호출중인 데이터가 없습니다.'A'의 열의 개수 (%d)가 'u'의 길이 (%d)와 일치하지 않습니다.'t' 또는 't0' 중 하나가 필요합니다.'data'의 %s와 %s 번째 열만이 사용되었습니다.오로지 'index'의 첫번째 2개 요소들만을 사용합니다.오로지 't'의 첫번째 열만이 사용됩니다.'index'의 첫번째 요소만이 사용되었습니다.'index'의 첫번째 요소만이 'abc.ci'에 사용되었습니다.sim 인자는 "coxph"라는 객체를 입력받는 경우에 "weird"이라는 값을 가질 수 없습니다.본 유형(type)은 이진데이터(Binary)의 경우에는 아직 구현되지 않았습니다.본 유형(type)은 포아송(Poisson)의 경우에는 아직 구현되지 않았습니다.요청한 전체적인 오류률(overall error rate)을 계산할 수 없습니다.'var.t0'를 계산할 수 없습니다.%f에 대한 계수(multiplier)를 찾을 수 없습니다.범위(range)를 구할 수 없습니다.'sim'의 값을 알 수 없습니다.'sim'의 값을 알 수 없습니다.스칼라 파라미터(scalar parameters)인 경우 'boot.ci'를 사용하세요스튜던트화된 구간(studentized intervals)에 요구되는 분산입니다.boot/inst/po/fr/0000755000176000001440000000000011663151666013262 5ustar ripleyusersboot/inst/po/fr/LC_MESSAGES/0000755000176000001440000000000011772542456015051 5ustar ripleyusersboot/inst/po/fr/LC_MESSAGES/R-boot.mo0000644000176000001440000002067712465333606016557 0ustar ripleyusersNk3  6)R)|/L&s"&% / F 4d . 4 . *, &W ~ ( % 4 5" ,X 7 +  $ *- !X z 2 - * < X v  0   ('Bj$$-2-P~=%%>"\.2$%.,[ x#+1P!2!Tv ..:8 sd%,=j%V6Y?/(()*R,}3L@+8l:,! /:N-K60QU 19k&*0&-.+\+07'=Je48-=-k<7* +9 7e  / + !2!0O!>!L9JB*5= N+1'47 - MAC?8D;@,2>H.$#/6I 0E G:F&)%( K3!"<%s distribution not supported: using normal instead'F.surv' is required but missing'G.surv' is required but missing'K' has been set to %f'K' outside allowable range'R' and 'alpha' have incompatible lengths'R' and 'theta' have incompatible lengths'R' must be positive'alpha' ignored; R[1L] = 0'data' must be a matrix with at least 2 columns'index' must contain 2 elements'simple=TRUE' is only valid for 'sim="ordinary", stype="i", n=0', so ignored'strata' of wrong length'stype' must be "w" for type="inf"'t' and 't0' must be supplied together't' must of length %d'theta' must be supplied if R[1L] = 0'theta' or 'lambda' required'u' must be a function0 elements not allowed in 'q'BCa intervals not defined for time series bootstrapsR[1L] must be positive for frequency smoothingarguments are not all the same type of "boot" objectarray cannot be found for parametric bootstrapboot.array not implemented for this objectbootstrap object needed for type="reg"bootstrap output matrix missingbootstrap output object or 't0' requiredbootstrap replicates must be suppliedbootstrap variances needed for studentized intervalscontrol methods undefined when 'boot.out' has weightsdimensions of 'R' and 'weights' do not matcheither 'A' and 'u' or 'K.adj' and 'K2' must be suppliedeither 'boot.out' or 'w' must be specified.estimated adjustment 'a' is NAestimated adjustment 'w' is infiniteextreme order statistics used as endpointsextreme values used for quantilesfunction 'u' missingindex array not defined for model-based resamplingindex out of bounds; minimum index only used.indices are incompatible with 'ncol(data)'influence values cannot be found from a parametric bootstrapinput 't' ignored; type="inf"input 't' ignored; type="jack"input 't' ignored; type="pos"input 't0' ignored: neither 't' nor 'L' suppliedinvalid value of 'l'invalid value of 'sim' suppliedlength of 'm' incompatible with 'strata'likelihood exceeds %f at only one pointlikelihood never exceeds %fmissing values not allowed in 'data'multivariate time series not allowednegative value of 'm' suppliedneither 'data' nor bootstrap object specifiedneither 'statistic' nor bootstrap object specifiedno coefficients in Cox model -- model ignoredno data in call to 'boot'number of columns of 'A' (%d) not equal to length of 'u' (%d)one of 't' or 't0' requiredonly columns %s and %s of 'data' usedonly first 2 elements of 'index' usedonly first column of 't' usedonly first element of 'index' usedonly first element of 'index' used in 'abc.ci'sim = "weird" cannot be used with a "coxph" objectthis type not implemented for Binarythis type not implemented for Poissonunable to achieve requested overall error rateunable to calculate 'var.t0'unable to find multiplier for %funable to find rangeunknown value of 'sim'unrecognized value of 'sim'use 'boot.ci' for scalar parametersvariance required for studentized intervalsProject-Id-Version: boot 1.2-23 Report-Msgid-Bugs-To: bugs@r-project.org POT-Creation-Date: 2012-10-11 15:21 PO-Revision-Date: 2012-10-03 15:35+0100 Last-Translator: Philippe Grosjean Language-Team: French Language: fr MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: 8bit Plural-Forms: nplurals=2; plural=(n > 1); X-Generator: Poedit 1.5.3 %s distribution non supporte, utilisation d'une distribution normale la place'F.surv' est requis mais manquant'G.surv' est requis mais manquant'K' est fix %f'K' en dehors de la plage admise'R' et 'alpha' ont des longueurs non conformes'R' et 'theta' ont des longueurs non conformes'R' doit tre positif'alpha' ignor ; R[1L] = 0'data' doit tre une matrice contenant au moins 2 colonnes'index' doit contenir 2 lments'simple=TRUE' n'est seulement valable que pour 'sim="ordinary", stype="i", n=0' ; il est donc ignor'strata' de mauvaise longueur'stype' doit tre "w" pour type="inf"'t' et 't0' doivent tre fixs simultanment't' doit tre de longueur %d'theta' doit tre fourni si R[1L] = 0'theta' ou 'lambda' requis'u' doit tre une fonction0 lments non permis pour 'q'les intervalles BCa ne sont pas dfinis pour les bootstraps sur les sries temporellesR[1L] doit tre positif pour un lissage des frquencesles arguments ne sont pas tous du mme type pour l'objet "boot"tableau non trouv pour un bootstrap pamtriqueboot.array non implment pour cet objetobjet 'bootstrap' requis pour type="reg"matrice manquante dans la sortie bootstrapobjet rsultat d'un bootstrap ou 't0' requisles rplications de bootstrap doivent tre fourniesles variances de bootstrap sont ncessaires pour les intervalles studentissmthodes de contrle non dfinies lorsque 'boot.out' est pondrles dimensions de 'R' et 'weights' ne sont pas conformessoit 'A' et 'u', soit 'K.adj' et 'K2' doivent tre fournissoit 'boot.out', soit 'w' doit tre spcifil'ajustement de 'a' estim est NAl'ajustement de 'w' est infinistatistiques d'ordre extrme utilises comme points finauxvaleurs extrmes utilises pour les quantilesfonction 'u' manquanteindiage de tableau non dfini pour un rchantillonnage bas sur un modleindice hors plage ; l'indice le plus petit est utilisles indices sont incompatibles avec 'ncol(data)'les valeurs d'influence ne peuvent tre trouves partir d'un bootstrap paramtriqueentre 't' ignore ; type="inf"entre 't' ignore ; type="jack"entre 't' ignore ; type="pos"entre 't0' ignore : ni 't', ni 'L' n'est fournivaleur de 'l' incorrectevaleur incorrecte spcifie pour 'sim'longueur de 'm' incompatible avec 'strata'la vraissemblance excde %f a seulement un pointla vraissemblance n'a jamais excd %fvaleurs manquantes non autorises dans 'data'sries temporelles multivaries non admisesvaleur ngative donne pour 'm'pas de 'data' ou d'objet bootstrap spcifipas de 'statistic' ou d'objet bootstrap spcifipas de coefficients dans le modle Cox -- modle ignorpas de donnes lors de l'appel 'boot'le nombre de colonnes de 'A' (%d) n'est pas gal la longueur de 'u' (%d)soit 't', soit 't0' est requisseule les colonnes %s et %s de 'data' sont utilisesseuls les deux premiers lments d''index' sont utilissseule la premire colonne de 't' est utiliseseul le premier lment d''index' est utilisseul le premier lment de 'index' est utilis dans 'abc.ci'sim="weird" ne peut tre utilis avec un object "coxph"ce type n'est pas implment pour 'Binary'ce type n'est pas implment pour 'Poisson'impossible d'atteindre le taux global d'erreur spcifiimpossible de calculer 'var.t0'impossible de trouver un multiplicateur pour %fimpossible de trouver l'tendue des valeursvaleur inconnue de 'sim'valeur de 'sim' non reconnueutilisez 'boot.ci' pour des paramtres scalairesvariance requise pour les intervalles de confiance studentissboot/inst/po/de/0000755000176000001440000000000011663151666013243 5ustar ripleyusersboot/inst/po/de/LC_MESSAGES/0000755000176000001440000000000012023430346015012 5ustar ripleyusersboot/inst/po/de/LC_MESSAGES/R-boot.mo0000644000176000001440000002055012465333606016526 0ustar ripleyusersNk3  6)R)|/L&s"&% / F 4d . 4 . *, &W ~ ( % 4 5" ,X 7 +  $ *- !X z 2 - * < X v  0   ('Bj$$-2-P~=%%>"\.2$%.,[ x#+1K%%Dj'**51!gZ%.&U2r8/:;jB3*H+f)<:84Cm3"'20(c<6*Y>!"!44$M(r3,&%# I,j15 H  i39&&7F@~./9 $X 2} "   - !07!L9JB*5= N+1'47 - MAC?8D;@,2>H.$#/6I 0E G:F&)%( K3!"<%s distribution not supported: using normal instead'F.surv' is required but missing'G.surv' is required but missing'K' has been set to %f'K' outside allowable range'R' and 'alpha' have incompatible lengths'R' and 'theta' have incompatible lengths'R' must be positive'alpha' ignored; R[1L] = 0'data' must be a matrix with at least 2 columns'index' must contain 2 elements'simple=TRUE' is only valid for 'sim="ordinary", stype="i", n=0', so ignored'strata' of wrong length'stype' must be "w" for type="inf"'t' and 't0' must be supplied together't' must of length %d'theta' must be supplied if R[1L] = 0'theta' or 'lambda' required'u' must be a function0 elements not allowed in 'q'BCa intervals not defined for time series bootstrapsR[1L] must be positive for frequency smoothingarguments are not all the same type of "boot" objectarray cannot be found for parametric bootstrapboot.array not implemented for this objectbootstrap object needed for type="reg"bootstrap output matrix missingbootstrap output object or 't0' requiredbootstrap replicates must be suppliedbootstrap variances needed for studentized intervalscontrol methods undefined when 'boot.out' has weightsdimensions of 'R' and 'weights' do not matcheither 'A' and 'u' or 'K.adj' and 'K2' must be suppliedeither 'boot.out' or 'w' must be specified.estimated adjustment 'a' is NAestimated adjustment 'w' is infiniteextreme order statistics used as endpointsextreme values used for quantilesfunction 'u' missingindex array not defined for model-based resamplingindex out of bounds; minimum index only used.indices are incompatible with 'ncol(data)'influence values cannot be found from a parametric bootstrapinput 't' ignored; type="inf"input 't' ignored; type="jack"input 't' ignored; type="pos"input 't0' ignored: neither 't' nor 'L' suppliedinvalid value of 'l'invalid value of 'sim' suppliedlength of 'm' incompatible with 'strata'likelihood exceeds %f at only one pointlikelihood never exceeds %fmissing values not allowed in 'data'multivariate time series not allowednegative value of 'm' suppliedneither 'data' nor bootstrap object specifiedneither 'statistic' nor bootstrap object specifiedno coefficients in Cox model -- model ignoredno data in call to 'boot'number of columns of 'A' (%d) not equal to length of 'u' (%d)one of 't' or 't0' requiredonly columns %s and %s of 'data' usedonly first 2 elements of 'index' usedonly first column of 't' usedonly first element of 'index' usedonly first element of 'index' used in 'abc.ci'sim = "weird" cannot be used with a "coxph" objectthis type not implemented for Binarythis type not implemented for Poissonunable to achieve requested overall error rateunable to calculate 'var.t0'unable to find multiplier for %funable to find rangeunknown value of 'sim'unrecognized value of 'sim'use 'boot.ci' for scalar parametersvariance required for studentized intervalsProject-Id-Version: R 2.15.2 / boot 1.3-6- Report-Msgid-Bugs-To: bugs@r-project.org POT-Creation-Date: 2012-10-11 15:21 PO-Revision-Date: 2012-10-11 16:01+0200 Last-Translator: Chris Leick Language-Team: German Language: de MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Plural-Forms: nplurals=2; plural=(n != 1); %s Verteilung nicht unterstützt, stattdessen wird Normalverteilung benutzt'F.surv' wird benötigt, fehlt jedoch'G.surv' wird benötigt, fehlt jedoch'K' wurde auf %f gesetzt'K' außerhalb des erlaubbaren Bereichs'R' und 'alpha' haben inkompatible Längen'R' und 'theta' haben inkompatible Längen'R' muss psitiv sein'alpha' ignoriert; R[1L]=0'data' muss eine Matrix mit mindestens 2 Spalten sein'index' muss 2 Elemente enthalten'simple=TRUE' gilt nur für 'sim="ordinary", stype="i", n=0' und wird daher hier ignoriert'strata' hat falsche Länge'stype' muss für type="inf" 'w' sein't' und 't0' müssen zusammen angegeben werden't' muss die Länge %d haben'theta' muss angegeben werden, falls R[1L] = 0 ist'theta' oder 'lambda' benötigt'u' muss eine Funktion sein0 Elemente nicht in 'q' erlaubtBCa Intervalle nicht für Zeitreihenbootstrap definiert.R[1L] muss für Frequenz-Glättung positiv seinArgumente waren nicht all vom selben Typ des 'boot'-ObjektsArray kann nicht für parametrisches Bootstrapping gefunden werden'boot.array' nicht für dieses Objekt implementiertBootstrap-Objekt für type="reg" benötigtBootstrap-Ausgabematrix fehltBootstrap-Ausgabeobjekt oder 't0' benötigtBootstrap-Kopien müssen angegeben werdenBootstrap-Varianzen für studentisierte Intervalle benötigtKontrollmethoden undefiniert, wenn 'boot.out' Gewichte hatDimensionen von 'R' und 'weights' stimmen nicht übereinentweder 'A' und 'u' oder 'K.adj' und 'K2' müssen angegeben werdenEntweder 'boot.out' oder 'w' muss angegeben werden.geschätzte Einstellung 'a' ist NAgeschätzte Anpassung 'w' ist unendlichExtremwertstatistiken werden als Endpunkte benutztExtremwerte werden für Quantile benutztFunktion 'u' fehltIndex-Array nicht für Modell-basiertes Resampling definiertIndex außerhalb des Rands. Minimalindex wird benutzt.Indizes sind inkompatibel mit 'ncol(data)'es können keine beeinflussenden Werte von einem parametrischen Bootstrap gefunden werdenEingabe 't' ignoriert; type="inf"Eingabe 't' ignoriert; type="jack"Eingabe 't' ignoriert; type="pos"Eingabe 't0' ignoriert: weder 't' noch 'L' angegebenungültiger Wert von 'l'ungültiger Wert von 'sim' angegebenLänge von 'm' inkompatibel mit 'strata'Wahrscheinlichkeit überschreitet %f an einem PunktWahrscheinlichkeit überschreitet niemals %ffehlende Werte in 'data' nicht erlaubtmultivariate Zeitserien nicht erlaubtnegativer Wert von 'm' angegebenweder 'data' noch Bootstrap-Objekt angegebenweder 'statistic' noch Bootstrap-Objekt angegebenkeine Koeffizienten im Cox-Modell -- Modell ignoriertkeine Daten im Aufruf von 'boot'Anzahl der Spalten von 'A' (%d) ist nicht gleich der Länge von 'u' (%d)eins von 't' oder 't0' benötigtnur die Spalten %s und %s von 'data' werden benutztnur die beiden ersten Elemente von 'index' werden benutztNur erste Spalte von 't' wird benutzt.nur erstes Element von 'index' benutztnur erstes Element von 'index' wird in 'abc.ci' benutztsim = "weird" kann nicht mit einem "coxph" Objekt benutzt werdendieser Typ ist nicht für Binary implementiertdieser Typ ist nicht für Poisson implementiertgeforderte overall Fehlerquote kann nicht erreicht werden'var.t0' kann nicht berechnet werdenEs kann kein Multiplikator für %f gefunden werdenBereich kann nicht gefunden werdenunbekannter Wert von 'sim'unbekannter Wert von 'sim'benutzen Sie 'boot.ci' für skalare ParameterVarianz für studentisierte Intervalle benötigtboot/inst/po/ru/0000755000176000001440000000000011663151666013301 5ustar ripleyusersboot/inst/po/ru/LC_MESSAGES/0000755000176000001440000000000012032132427015047 5ustar ripleyusersboot/inst/po/ru/LC_MESSAGES/R-boot.mo0000644000176000001440000002020112465333606016555 0ustar ripleyusersNk3  6)R)|/L&s"&% / F 4d . 4 . *, &W ~ ( % 4 5" ,X 7 +  $ *- !X z 2 - * < X v  0   ('Bj$$-2-P~=%%>"\.2$%.,[ x#+18 Dd,& )8C"|X&%;a${9:1+l4,$!+Am4C*01'b*,91NDd7#CIg-#"/3%c*'"(*(4S3*+&J'q292194k ,( +U L9JB*5= N+1'47 - MAC?8D;@,2>H.$#/6I 0E G:F&)%( K3!"<%s distribution not supported: using normal instead'F.surv' is required but missing'G.surv' is required but missing'K' has been set to %f'K' outside allowable range'R' and 'alpha' have incompatible lengths'R' and 'theta' have incompatible lengths'R' must be positive'alpha' ignored; R[1L] = 0'data' must be a matrix with at least 2 columns'index' must contain 2 elements'simple=TRUE' is only valid for 'sim="ordinary", stype="i", n=0', so ignored'strata' of wrong length'stype' must be "w" for type="inf"'t' and 't0' must be supplied together't' must of length %d'theta' must be supplied if R[1L] = 0'theta' or 'lambda' required'u' must be a function0 elements not allowed in 'q'BCa intervals not defined for time series bootstrapsR[1L] must be positive for frequency smoothingarguments are not all the same type of "boot" objectarray cannot be found for parametric bootstrapboot.array not implemented for this objectbootstrap object needed for type="reg"bootstrap output matrix missingbootstrap output object or 't0' requiredbootstrap replicates must be suppliedbootstrap variances needed for studentized intervalscontrol methods undefined when 'boot.out' has weightsdimensions of 'R' and 'weights' do not matcheither 'A' and 'u' or 'K.adj' and 'K2' must be suppliedeither 'boot.out' or 'w' must be specified.estimated adjustment 'a' is NAestimated adjustment 'w' is infiniteextreme order statistics used as endpointsextreme values used for quantilesfunction 'u' missingindex array not defined for model-based resamplingindex out of bounds; minimum index only used.indices are incompatible with 'ncol(data)'influence values cannot be found from a parametric bootstrapinput 't' ignored; type="inf"input 't' ignored; type="jack"input 't' ignored; type="pos"input 't0' ignored: neither 't' nor 'L' suppliedinvalid value of 'l'invalid value of 'sim' suppliedlength of 'm' incompatible with 'strata'likelihood exceeds %f at only one pointlikelihood never exceeds %fmissing values not allowed in 'data'multivariate time series not allowednegative value of 'm' suppliedneither 'data' nor bootstrap object specifiedneither 'statistic' nor bootstrap object specifiedno coefficients in Cox model -- model ignoredno data in call to 'boot'number of columns of 'A' (%d) not equal to length of 'u' (%d)one of 't' or 't0' requiredonly columns %s and %s of 'data' usedonly first 2 elements of 'index' usedonly first column of 't' usedonly first element of 'index' usedonly first element of 'index' used in 'abc.ci'sim = "weird" cannot be used with a "coxph" objectthis type not implemented for Binarythis type not implemented for Poissonunable to achieve requested overall error rateunable to calculate 'var.t0'unable to find multiplier for %funable to find rangeunknown value of 'sim'unrecognized value of 'sim'use 'boot.ci' for scalar parametersvariance required for studentized intervalsProject-Id-Version: R 2.10.0 Report-Msgid-Bugs-To: bugs@r-project.org POT-Creation-Date: 2012-10-11 15:21 PO-Revision-Date: 2013-03-19 14:42-0600 Last-Translator: Alexey Shipunov Language-Team: Russian Language: MIME-Version: 1.0 Content-Type: text/plain; charset=KOI8-R Content-Transfer-Encoding: 8bit X-Poedit-Language: Russian Plural-Forms: nplurals=3; plural=(n%10==1 && n%100!=11 ? 0 : n%10>=2 && n%10<=4 && (n%100<10 || n%100>=20) ? 1 : 2); %s , 'F.surv' , 'G.surv' , 'K' %f'K' 'R' 'alpha' 'R' 'theta' -- 'R' 'alpha' ; R[1L]=0 2 2 'simple=TRUE' 'sim="ordinary", stype="i", n=0, 'strata' 'stype' "w" type="inf"'t' 't0' 't' %d 'theta', R[1L] = 0 'theta' 'lambda''u' 0 'q' BCa R[1L] "boot" 'boot.array' type="reg" - - 't0' -- - 'boot.out' 'R' 'weights' 'A' 'u', 'K.adj' 'K2' 'boot.out', 'w'. 'a' -- NA 'w' -- infinite'extreme order statistics' 'u' ; . 'ncol(data)' 't' ; type="inf" 't' ; type="jack" 't' ; type="pos" 't0' : 't', 'L' 'l' 'sim' 'm' 'strata' %f %f 'm' - 'statistic' - 'Cox' -- 'boot' 'A' (%d) 'u' (%d) 't' 't0' %s %s 2 't' 'abc.ci'sim="weird" "coxph" 'var.t0' %f 'sim' 'sim' 'boot.ci' - boot/inst/bd.q0000644000176000001440000003531011110552531012765 0ustar ripleyusers# script to make cd4.nested library(boot) data(cd4) temp <- boot(cd4, nested.corr, R=1, stype="w", t0=corr(cd4), M=9) tempcl <- temp$call tempcl$R <- 999 tempcl$M <- 249 junk <- scan(file="",list(0,0),nmax=999*2) 1.144 0.832 1.070 0.828 -0.783 0.164 -0.867 0.176 0.627 0.700 4.499 0.904 1.415 0.844 -0.457 0.324 1.270 0.868 1.479 0.860 -0.772 0.192 -0.587 0.288 -1.805 0.040 0.080 0.548 0.693 0.732 -1.321 0.072 0.313 0.624 0.751 0.772 -0.868 0.152 -0.365 0.348 0.386 0.640 -0.158 0.476 0.561 0.660 -0.087 0.412 0.962 0.724 1.432 0.892 -0.151 0.388 0.420 0.604 1.055 0.792 -1.209 0.068 -1.072 0.128 0.104 0.544 -0.225 0.384 0.800 0.764 0.477 0.652 1.089 0.732 -0.978 0.164 1.664 0.812 2.055 0.908 -0.365 0.316 0.697 0.740 3.514 0.948 -0.921 0.120 1.076 0.760 -0.486 0.300 0.137 0.524 -0.976 0.120 -0.035 0.496 -1.607 0.064 2.672 0.960 0.001 0.412 0.249 0.596 -0.471 0.308 0.541 0.624 -0.137 0.440 1.047 0.816 -0.175 0.420 -0.689 0.240 -0.208 0.412 -1.574 0.064 0.073 0.520 -1.192 0.104 3.359 0.980 -0.041 0.428 -0.506 0.332 0.762 0.748 -0.735 0.232 1.372 0.856 0.109 0.560 -1.611 0.064 -0.798 0.200 -0.534 0.244 -0.617 0.236 0.671 0.724 -1.500 0.032 -0.302 0.368 -0.793 0.240 2.322 0.956 0.297 0.572 2.979 0.964 0.144 0.520 0.231 0.556 1.462 0.888 0.843 0.728 -0.508 0.308 -0.637 0.196 1.269 0.800 0.534 0.652 -0.342 0.324 0.164 0.456 -1.023 0.212 0.611 0.712 0.329 0.576 2.476 0.920 -0.198 0.428 0.154 0.520 -0.161 0.364 0.198 0.520 0.502 0.684 1.138 0.848 -0.266 0.452 -1.077 0.080 0.340 0.540 0.015 0.480 0.309 0.608 0.968 0.788 0.589 0.648 -0.869 0.192 0.655 0.664 -1.566 0.036 -1.521 0.060 0.273 0.608 -1.270 0.072 2.160 0.904 -0.855 0.252 -1.121 0.108 0.834 0.744 0.944 0.732 0.342 0.620 1.105 0.812 -0.862 0.152 0.562 0.748 -1.021 0.092 -0.569 0.288 -0.272 0.328 0.289 0.612 2.235 0.916 -0.595 0.280 -0.487 0.264 -0.755 0.184 0.429 0.592 -1.712 0.064 -1.945 0.112 0.538 0.660 -0.285 0.404 0.280 0.564 -0.442 0.320 0.936 0.744 0.101 0.576 2.294 0.884 -0.702 0.188 -0.281 0.356 -0.790 0.208 0.307 0.628 1.904 0.908 -0.663 0.292 0.879 0.752 -0.930 0.172 -0.014 0.412 0.546 0.636 0.199 0.536 -0.996 0.164 -0.615 0.252 -0.377 0.372 3.053 0.884 -0.233 0.452 0.127 0.468 -0.404 0.328 -1.082 0.200 1.277 0.852 -0.837 0.232 1.921 0.888 1.175 0.836 -0.238 0.388 -0.361 0.344 -0.202 0.308 3.016 0.964 -1.572 0.036 0.438 0.620 -0.057 0.452 1.577 0.844 -0.501 0.300 -0.537 0.268 1.599 0.884 -0.531 0.244 -1.506 0.028 -0.093 0.476 1.510 0.880 0.404 0.656 0.045 0.548 -0.821 0.152 1.358 0.788 -0.307 0.316 -1.460 0.044 -0.380 0.312 -0.248 0.352 -0.794 0.152 0.107 0.516 0.533 0.664 0.412 0.572 -1.150 0.120 -0.906 0.180 -0.232 0.332 0.672 0.640 1.328 0.876 -0.814 0.228 -0.962 0.128 0.484 0.672 -0.550 0.256 -1.716 0.036 -0.381 0.248 3.577 0.984 1.080 0.832 0.980 0.748 0.248 0.624 0.097 0.516 1.662 0.848 -1.761 0.052 -0.983 0.156 3.283 0.920 -0.243 0.388 -1.203 0.116 1.693 0.924 0.339 0.632 -1.719 0.028 1.984 0.916 -1.866 0.080 -0.696 0.228 2.025 0.924 1.700 0.904 0.144 0.512 -0.956 0.144 -1.384 0.056 0.345 0.628 0.900 0.728 1.201 0.820 -0.455 0.276 -0.714 0.260 0.235 0.572 -1.419 0.060 1.621 0.912 2.301 0.932 0.615 0.708 -1.681 0.032 0.459 0.624 -0.178 0.424 1.130 0.812 -0.708 0.228 -1.745 0.036 1.357 0.872 -0.143 0.444 -0.307 0.380 2.208 0.944 -0.565 0.236 -1.516 0.092 -1.410 0.056 -0.455 0.332 0.864 0.716 -1.704 0.100 2.354 0.964 -1.185 0.080 -0.787 0.164 2.212 0.920 -0.449 0.244 -1.067 0.124 -0.994 0.136 3.940 0.988 -0.737 0.212 1.044 0.812 0.282 0.564 -0.305 0.380 -1.340 0.124 0.071 0.508 0.373 0.580 0.734 0.668 1.532 0.808 -0.124 0.400 0.392 0.584 1.058 0.772 0.931 0.792 1.847 0.940 0.824 0.764 1.472 0.892 -0.251 0.384 1.035 0.756 0.143 0.540 -0.320 0.360 2.331 0.960 -0.582 0.232 0.914 0.668 -0.580 0.296 2.503 0.868 0.033 0.564 -0.085 0.400 0.383 0.600 1.374 0.864 0.346 0.568 1.878 0.888 2.702 0.964 2.039 0.864 0.390 0.636 -0.777 0.248 0.288 0.544 -1.198 0.096 -0.146 0.452 1.027 0.812 -0.760 0.184 0.108 0.500 -0.192 0.380 -0.195 0.432 1.977 0.940 1.711 0.904 -1.150 0.092 1.119 0.832 0.949 0.792 1.467 0.828 -1.438 0.100 -0.044 0.404 0.505 0.652 0.271 0.568 0.191 0.556 0.498 0.644 0.847 0.720 0.209 0.580 -0.319 0.360 -1.090 0.128 0.676 0.744 -0.912 0.144 0.665 0.680 -0.783 0.224 0.769 0.740 -1.674 0.048 -0.634 0.248 1.131 0.828 0.666 0.672 -0.451 0.252 0.271 0.576 0.740 0.684 0.090 0.484 -0.496 0.252 -0.285 0.316 1.476 0.860 1.497 0.868 0.067 0.432 0.121 0.508 3.213 0.864 -0.194 0.408 0.692 0.712 -0.519 0.252 -1.203 0.100 0.583 0.688 1.374 0.884 -0.582 0.248 -0.091 0.432 -0.143 0.420 -0.599 0.260 0.432 0.620 -1.553 0.084 0.539 0.656 -0.355 0.344 0.294 0.600 1.056 0.720 -0.980 0.112 0.717 0.716 0.395 0.632 -0.460 0.308 0.910 0.792 1.136 0.816 -0.167 0.400 -0.718 0.184 0.914 0.812 1.916 0.900 0.743 0.712 0.084 0.476 -1.222 0.116 1.963 0.900 -0.924 0.132 0.141 0.572 0.051 0.492 -0.518 0.268 -0.046 0.388 0.119 0.460 1.606 0.780 -1.401 0.040 2.684 0.960 -0.427 0.344 0.726 0.704 1.054 0.780 0.271 0.564 0.352 0.604 0.970 0.800 1.396 0.832 2.602 0.944 0.222 0.508 -1.753 0.068 0.393 0.612 -0.567 0.220 2.108 0.912 0.118 0.492 2.223 0.912 -0.611 0.312 0.148 0.508 2.778 0.896 0.735 0.708 0.525 0.672 -0.268 0.300 -0.198 0.392 -0.197 0.420 11.599 0.996 -0.329 0.372 -0.181 0.440 -1.069 0.108 -0.569 0.256 -0.270 0.348 0.257 0.608 -0.351 0.356 -0.392 0.344 -0.686 0.204 0.457 0.664 1.620 0.928 1.429 0.888 0.598 0.636 0.646 0.668 2.532 0.940 1.214 0.784 -0.407 0.392 -2.367 0.056 -2.385 0.008 -0.155 0.412 2.218 0.904 -0.785 0.212 -1.280 0.112 1.108 0.820 0.130 0.516 -0.766 0.204 0.403 0.604 0.027 0.500 -0.979 0.120 0.458 0.624 -0.942 0.212 0.945 0.736 1.439 0.888 1.938 0.836 -0.169 0.348 -0.522 0.268 -0.254 0.384 0.333 0.580 0.377 0.600 2.680 0.980 0.527 0.656 2.282 0.964 -1.032 0.116 0.709 0.684 1.423 0.800 -1.590 0.076 0.077 0.564 0.634 0.616 -0.508 0.224 -1.112 0.132 1.383 0.836 0.178 0.600 -0.332 0.384 3.285 0.904 0.532 0.628 -1.336 0.112 0.239 0.600 1.150 0.836 0.172 0.436 -0.253 0.376 -0.929 0.164 0.360 0.572 0.434 0.580 2.318 0.908 0.087 0.544 0.305 0.596 0.048 0.456 0.204 0.592 -0.658 0.220 0.860 0.708 -1.662 0.120 3.368 0.984 -0.639 0.252 0.048 0.536 0.164 0.552 1.989 0.924 -2.212 0.004 0.612 0.672 -0.917 0.124 0.189 0.496 -1.585 0.056 -0.091 0.444 -1.178 0.104 -1.206 0.160 -0.253 0.392 1.784 0.884 -0.620 0.240 -0.424 0.384 0.663 0.712 0.594 0.724 -0.572 0.188 1.857 0.916 1.158 0.768 1.150 0.820 -1.758 0.024 3.633 0.976 2.310 0.956 1.666 0.868 -1.069 0.108 -0.148 0.360 1.539 0.876 -0.433 0.336 -0.207 0.404 2.368 0.908 -0.758 0.184 0.106 0.544 0.702 0.736 -1.089 0.116 -0.097 0.476 0.929 0.796 0.870 0.720 -0.402 0.292 -1.631 0.080 0.666 0.612 0.069 0.472 -0.611 0.300 -0.239 0.360 0.557 0.688 0.314 0.524 -0.620 0.288 0.297 0.552 0.639 0.612 0.336 0.580 0.167 0.496 1.978 0.920 0.111 0.532 -1.199 0.072 0.214 0.604 0.043 0.488 1.525 0.900 -0.133 0.404 -0.915 0.140 4.312 0.980 0.019 0.476 -0.274 0.312 1.667 0.800 2.133 0.920 0.486 0.640 1.329 0.848 -1.314 0.064 0.681 0.700 6.797 0.988 -0.122 0.416 -0.097 0.396 -1.186 0.148 4.301 0.976 0.472 0.664 -0.542 0.304 -0.558 0.332 -0.767 0.152 -0.071 0.412 0.660 0.620 0.032 0.524 0.489 0.612 -0.658 0.256 -0.001 0.500 -0.344 0.332 0.356 0.544 0.093 0.532 0.988 0.768 -0.215 0.392 1.478 0.816 -0.745 0.212 0.324 0.624 1.159 0.772 1.183 0.784 0.069 0.500 -2.341 0.016 0.821 0.728 1.557 0.924 -0.417 0.320 0.423 0.568 -1.048 0.112 -0.201 0.408 -1.616 0.092 0.977 0.740 -0.061 0.428 -0.359 0.360 0.030 0.444 0.206 0.544 -1.570 0.052 -1.882 0.024 -0.310 0.348 -0.200 0.440 -0.927 0.132 -0.140 0.416 6.426 0.988 1.455 0.796 -0.467 0.344 0.214 0.536 0.121 0.504 -1.076 0.112 -0.523 0.260 -0.238 0.388 -0.188 0.432 -0.308 0.300 -0.548 0.224 2.200 0.904 -1.019 0.148 -0.086 0.452 0.715 0.708 0.513 0.676 -0.916 0.188 0.509 0.724 -0.777 0.184 -1.028 0.120 -0.163 0.348 1.825 0.912 0.936 0.788 -0.847 0.236 0.948 0.756 -0.166 0.424 -0.459 0.304 -0.244 0.404 0.668 0.664 0.201 0.544 -1.586 0.048 -0.248 0.352 0.501 0.636 -0.027 0.404 -0.994 0.120 1.553 0.900 0.986 0.804 0.395 0.664 0.326 0.612 -0.579 0.208 0.004 0.508 -0.610 0.192 0.061 0.348 0.150 0.516 1.833 0.940 -0.172 0.476 -0.411 0.344 1.312 0.824 -0.249 0.436 1.583 0.872 -0.746 0.136 0.334 0.552 2.246 0.968 -0.298 0.344 -0.213 0.432 -0.665 0.212 2.006 0.912 1.653 0.872 0.621 0.676 -1.736 0.036 -0.283 0.376 -0.038 0.472 2.060 0.928 1.043 0.776 6.828 0.996 -1.116 0.112 0.452 0.616 0.115 0.484 2.737 0.932 0.372 0.608 2.498 0.944 -0.535 0.312 -0.350 0.340 -1.899 0.036 1.397 0.792 -0.023 0.488 0.546 0.664 0.398 0.596 -0.793 0.212 -0.708 0.216 -0.011 0.476 -0.493 0.256 -2.167 0.052 -0.876 0.164 0.637 0.728 -0.946 0.148 -0.836 0.156 2.177 0.888 1.402 0.820 -0.666 0.236 0.110 0.388 3.174 0.952 -0.888 0.164 0.418 0.596 1.639 0.872 0.866 0.772 -0.705 0.176 1.089 0.760 -0.145 0.424 -0.421 0.392 1.982 0.920 0.108 0.532 1.156 0.812 1.580 0.780 -0.537 0.312 1.897 0.916 -0.763 0.212 -0.625 0.244 2.069 0.952 -0.151 0.420 0.688 0.720 -0.599 0.260 0.853 0.744 -0.767 0.192 0.541 0.620 -0.647 0.228 2.548 0.968 -1.238 0.112 -0.327 0.380 0.030 0.544 1.425 0.852 -0.703 0.212 1.248 0.836 1.115 0.816 1.333 0.792 -0.380 0.304 -0.870 0.188 1.067 0.756 -0.985 0.088 -0.339 0.296 -1.463 0.052 0.341 0.560 -0.204 0.388 0.577 0.596 -0.792 0.208 0.084 0.492 -1.002 0.172 -1.224 0.064 0.792 0.716 0.704 0.656 1.208 0.828 -0.087 0.440 -1.072 0.180 -0.998 0.144 2.540 0.960 -0.360 0.272 1.549 0.900 -1.793 0.016 -0.449 0.316 0.006 0.492 -0.005 0.480 -1.018 0.088 2.047 0.916 0.095 0.492 0.943 0.752 0.291 0.596 -1.024 0.128 0.944 0.812 0.033 0.540 1.268 0.792 0.449 0.644 3.289 0.988 2.087 0.884 0.504 0.640 1.161 0.832 1.523 0.840 -1.057 0.120 -0.078 0.532 -0.870 0.176 0.047 0.520 1.706 0.888 0.568 0.604 0.811 0.776 -1.592 0.120 0.475 0.664 -0.939 0.112 -0.053 0.416 5.270 0.988 2.233 0.924 -0.819 0.168 -1.034 0.112 -0.247 0.368 0.981 0.764 0.165 0.564 -0.813 0.208 0.190 0.568 -0.454 0.288 0.607 0.668 -0.665 0.260 -0.138 0.412 0.347 0.624 3.328 0.940 1.128 0.784 2.335 0.968 1.273 0.844 -0.154 0.396 -0.054 0.432 -0.517 0.236 0.174 0.564 0.088 0.536 -0.590 0.224 0.459 0.644 0.732 0.704 3.150 0.956 -0.489 0.292 0.012 0.488 0.575 0.644 -0.111 0.448 3.006 0.924 -0.496 0.236 0.673 0.672 0.842 0.756 0.440 0.668 -0.301 0.416 0.401 0.584 3.002 0.972 -0.458 0.312 1.929 0.904 1.385 0.836 -1.517 0.076 -1.178 0.120 -1.057 0.092 0.349 0.612 -0.500 0.264 -1.323 0.084 -0.543 0.272 1.251 0.848 -0.427 0.336 1.248 0.812 2.041 0.912 -1.427 0.040 -2.134 0.012 -0.188 0.380 -0.970 0.136 -1.005 0.136 -0.121 0.412 0.314 0.596 -1.833 0.044 0.141 0.540 2.007 0.944 1.716 0.856 2.131 0.908 -0.313 0.360 -0.232 0.420 0.008 0.472 -0.782 0.156 -0.456 0.300 -0.423 0.308 -0.371 0.304 -0.156 0.468 1.309 0.844 1.077 0.800 -0.766 0.168 -0.628 0.268 -0.713 0.204 -0.521 0.252 1.767 0.880 0.569 0.604 -0.024 0.504 4.942 0.916 -0.051 0.432 -0.326 0.336 0.816 0.704 -0.295 0.356 0.207 0.592 0.961 0.772 3.595 0.980 0.942 0.756 0.072 0.496 1.819 0.872 0.758 0.688 8.778 0.984 -0.598 0.292 1.979 0.924 -0.381 0.236 -0.367 0.284 -0.537 0.300 0.937 0.768 1.641 0.808 -0.400 0.332 0.175 0.540 0.514 0.704 0.284 0.540 2.383 0.856 1.450 0.812 0.557 0.660 0.128 0.508 -0.692 0.180 0.853 0.800 -0.936 0.128 2.212 0.924 -0.033 0.456 0.831 0.700 -0.024 0.460 0.957 0.776 -0.323 0.328 1.112 0.784 -0.060 0.412 -0.108 0.412 -1.699 0.040 1.583 0.832 0.335 0.572 -1.355 0.084 -1.408 0.116 -0.629 0.240 0.121 0.492 0.449 0.656 -1.767 0.100 -0.502 0.236 0.015 0.452 0.013 0.484 0.807 0.732 -1.236 0.088 0.545 0.688 1.738 0.916 -1.314 0.068 1.060 0.876 0.348 0.660 1.032 0.816 -1.209 0.084 -0.504 0.292 -1.395 0.052 1.774 0.864 -0.165 0.452 -0.271 0.396 1.471 0.844 -0.256 0.384 -0.758 0.228 -1.859 0.036 0.001 0.528 -0.098 0.412 -2.176 0.012 -1.416 0.084 -0.705 0.184 -0.696 0.200 0.211 0.596 1.841 0.908 -0.722 0.240 0.842 0.756 0.656 0.652 0.295 0.640 -2.105 0.020 -0.479 0.204 2.837 0.948 1.985 0.888 -0.200 0.400 1.560 0.860 1.135 0.836 -0.601 0.216 -0.639 0.220 2.231 0.920 -2.118 0.116 0.693 0.740 -1.787 0.008 1.723 0.872 2.110 0.896 0.045 0.444 0.480 0.576 0.325 0.616 -0.360 0.408 -0.487 0.328 0.985 0.712 -0.175 0.396 0.100 0.500 1.248 0.788 0.435 0.620 -0.718 0.220 -1.187 0.108 -0.192 0.396 -0.838 0.156 0.023 0.512 -0.469 0.256 -0.096 0.440 -0.805 0.200 0.294 0.584 2.222 0.956 0.542 0.696 -0.686 0.312 -0.696 0.172 0.669 0.652 0.258 0.580 -1.389 0.048 -0.927 0.140 0.535 0.660 -0.717 0.128 -0.741 0.208 -0.749 0.216 -0.586 0.236 2.391 0.928 -0.843 0.188 -0.338 0.380 -1.137 0.104 -1.279 0.068 -0.272 0.344 0.731 0.748 1.470 0.840 0.730 0.760 0.550 0.708 -1.340 0.068 -1.243 0.068 0.198 0.520 0.728 0.744 -0.098 0.360 -1.113 0.116 -0.860 0.164 -0.662 0.160 -0.221 0.428 1.247 0.792 0.565 0.720 -1.304 0.104 2.031 0.896 1.163 0.792 1.361 0.824 -0.579 0.256 junk1 <- scan(file="",list(i=0,base=0,one=0),nmax=60) 1 2.12 2.47 2 4.35 4.61 3 3.39 5.26 4 2.51 3.02 5 4.04 6.36 6 5.10 5.93 7 3.77 3.93 8 3.35 4.09 9 4.10 4.88 10 3.35 3.81 11 4.15 4.74 12 3.56 3.29 13 3.39 5.55 14 1.88 2.82 15 2.56 4.23 16 2.96 3.23 17 2.49 2.56 18 3.03 4.31 19 2.66 4.37 20 3.00 2.40 cd4.nested <- boot:::boot.return(sim="ordinary", t0=c(0,0.532), t=cbind(junk[[1]],junk[[2]]), strata=rep(1,20), R=999, data=data.frame(baseline=junk1$base,oneyear=junk1$one), stat=temp$statistic, stype="w", call=tempcl, seed=NULL, m=0, weights=rep(0.05,20)) rm(junk,junk1,temp,tempcl) save(cd4.nested, file="cd4.nested.rda", compress=TRUE) boot/inst/CITATION0000644000176000001440000000231711716773365013402 0ustar ripleyuserscitHeader("To cite the 'boot' package in publications use:") year <- sub(".*(2[[:digit:]]{3})-.*", "\\1", meta$Date, perl = TRUE) vers <- paste("R package version", meta$Version) citEntry(entry="Manual", title = "boot: Bootstrap R (S-Plus) Functions", author = personList(as.person("Angelo Canty"), as.person("B. D. Ripley")), year = year, note = vers, textVersion = paste("Angelo Canty and Brian Ripley (", year, "). boot: Bootstrap R (S-Plus) Functions. ", vers, ".", sep="")) citEntry(entry="Book", title = "Bootstrap Methods and Their Applications", author = personList(as.person("A. C. Davison"), as.person("D. V. Hinkley")), publisher = "Cambridge University Press", address = "Cambridge", year = "1997", note = "ISBN 0-521-57391-2", url = "http://statwww.epfl.ch/davison/BMA/", textVersion = paste("Davison, A. C. & Hinkley, D. V. (1997)", "Bootstrap Methods and Their Applications.", "Cambridge University Press, Cambridge. ISBN 0-521-57391-2") ) boot/tests/0000755000176000001440000000000011663151666012422 5ustar ripleyusersboot/tests/Examples/0000755000176000001440000000000012105463455014172 5ustar ripleyusersboot/tests/Examples/boot-Ex.Rout.save0000644000176000001440000022727012473141221017321 0ustar ripleyusers R Under development (unstable) (2015-02-23 r67886) -- "Unsuffered Consequences" Copyright (C) 2015 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin14.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > pkgname <- "boot" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('boot') > > base::assign(".oldSearch", base::search(), pos = 'CheckExEnv') > cleanEx() > nameEx("Imp.Estimates") > ### * Imp.Estimates > > flush(stderr()); flush(stdout()) > > ### Name: Imp.Estimates > ### Title: Importance Sampling Estimates > ### Aliases: Imp.Estimates imp.moments imp.prob imp.quantile imp.reg > ### Keywords: htest nonparametric > > ### ** Examples > > # Example 9.8 of Davison and Hinkley (1997) requires tilting the > # resampling distribution of the studentized statistic to be centred > # at the observed value of the test statistic, 1.84. In this example > # we show how certain estimates can be found using resamples taken from > # the tilted distribution. > grav1 <- gravity[as.numeric(gravity[,2]) >= 7, ] > grav.fun <- function(dat, w, orig) { + strata <- tapply(dat[, 2], as.numeric(dat[, 2])) + d <- dat[, 1] + ns <- tabulate(strata) + w <- w/tapply(w, strata, sum)[strata] + mns <- as.vector(tapply(d * w, strata, sum)) # drop names + mn2 <- tapply(d * d * w, strata, sum) + s2hat <- sum((mn2 - mns^2)/ns) + c(mns[2] - mns[1], s2hat, (mns[2] - mns[1] - orig)/sqrt(s2hat)) + } > grav.z0 <- grav.fun(grav1, rep(1, 26), 0) > grav.L <- empinf(data = grav1, statistic = grav.fun, stype = "w", + strata = grav1[,2], index = 3, orig = grav.z0[1]) > grav.tilt <- exp.tilt(grav.L, grav.z0[3], strata = grav1[, 2]) > grav.tilt.boot <- boot(grav1, grav.fun, R = 199, stype = "w", + strata = grav1[, 2], weights = grav.tilt$p, + orig = grav.z0[1]) > # Since the weights are needed for all calculations, we shall calculate > # them once only. > grav.w <- imp.weights(grav.tilt.boot) > grav.mom <- imp.moments(grav.tilt.boot, w = grav.w, index = 3) > grav.p <- imp.prob(grav.tilt.boot, w = grav.w, index = 3, t0 = grav.z0[3]) > unlist(grav.p) t0 raw rat reg 1.8401182 1.0222447 0.9778246 0.9767292 > grav.q <- imp.quantile(grav.tilt.boot, w = grav.w, index = 3, + alpha = c(0.9, 0.95, 0.975, 0.99)) > as.data.frame(grav.q) alpha raw rat reg 1 0.900 3.048484 1.170707 5.056004 2 0.950 3.237935 1.565928 5.056004 3 0.975 3.629448 1.895876 5.056004 4 0.990 3.629448 2.258157 5.056004 > > > > cleanEx() > nameEx("abc.ci") > ### * abc.ci > > flush(stderr()); flush(stdout()) > > ### Name: abc.ci > ### Title: Nonparametric ABC Confidence Intervals > ### Aliases: abc.ci > ### Keywords: nonparametric htest > > ### ** Examples > > ## Don't show: > op <- options(digits = 5) > ## End(Don't show) > # 90% and 95% confidence intervals for the correlation > # coefficient between the columns of the bigcity data > > abc.ci(bigcity, corr, conf=c(0.90,0.95)) conf [1,] 0.90 0.95815 0.99173 [2,] 0.95 0.94937 0.99307 > > # A 95% confidence interval for the difference between the means of > # the last two samples in gravity > mean.diff <- function(y, w) + { gp1 <- 1:table(as.numeric(y$series))[1] + sum(y[gp1, 1] * w[gp1]) - sum(y[-gp1, 1] * w[-gp1]) + } > grav1 <- gravity[as.numeric(gravity[, 2]) >= 7, ] > abc.ci(grav1, mean.diff, strata = grav1$series) [1] 0.95000 -6.70758 -0.39394 > ## Don't show: > options(op) > ## End(Don't show) > > > > cleanEx() > nameEx("boot") > ### * boot > > flush(stderr()); flush(stdout()) > > ### Name: boot > ### Title: Bootstrap Resampling > ### Aliases: boot boot.return c.boot > ### Keywords: nonparametric htest > > ### ** Examples > > ## Don't show: > op <- options(digits = 5) > ## End(Don't show) > # Usual bootstrap of the ratio of means using the city data > ratio <- function(d, w) sum(d$x * w)/sum(d$u * w) > boot(city, ratio, R = 999, stype = "w") ORDINARY NONPARAMETRIC BOOTSTRAP Call: boot(data = city, statistic = ratio, R = 999, stype = "w") Bootstrap Statistics : original bias std. error t1* 1.5203 0.0396 0.21671 > > > # Stratified resampling for the difference of means. In this > # example we will look at the difference of means between the final > # two series in the gravity data. > diff.means <- function(d, f) + { n <- nrow(d) + gp1 <- 1:table(as.numeric(d$series))[1] + m1 <- sum(d[gp1,1] * f[gp1])/sum(f[gp1]) + m2 <- sum(d[-gp1,1] * f[-gp1])/sum(f[-gp1]) + ss1 <- sum(d[gp1,1]^2 * f[gp1]) - (m1 * m1 * sum(f[gp1])) + ss2 <- sum(d[-gp1,1]^2 * f[-gp1]) - (m2 * m2 * sum(f[-gp1])) + c(m1 - m2, (ss1 + ss2)/(sum(f) - 2)) + } > grav1 <- gravity[as.numeric(gravity[,2]) >= 7,] > boot(grav1, diff.means, R = 999, stype = "f", strata = grav1[,2]) STRATIFIED BOOTSTRAP Call: boot(data = grav1, statistic = diff.means, R = 999, stype = "f", strata = grav1[, 2]) Bootstrap Statistics : original bias std. error t1* -2.8462 0.002541 1.5467 t2* 16.8462 -1.457663 6.7591 > > # In this example we show the use of boot in a prediction from > # regression based on the nuclear data. This example is taken > # from Example 6.8 of Davison and Hinkley (1997). Notice also > # that two extra arguments to 'statistic' are passed through boot. > nuke <- nuclear[, c(1, 2, 5, 7, 8, 10, 11)] > nuke.lm <- glm(log(cost) ~ date+log(cap)+ne+ct+log(cum.n)+pt, data = nuke) > nuke.diag <- glm.diag(nuke.lm) > nuke.res <- nuke.diag$res * nuke.diag$sd > nuke.res <- nuke.res - mean(nuke.res) > > # We set up a new data frame with the data, the standardized > # residuals and the fitted values for use in the bootstrap. > nuke.data <- data.frame(nuke, resid = nuke.res, fit = fitted(nuke.lm)) > > # Now we want a prediction of plant number 32 but at date 73.00 > new.data <- data.frame(cost = 1, date = 73.00, cap = 886, ne = 0, + ct = 0, cum.n = 11, pt = 1) > new.fit <- predict(nuke.lm, new.data) > > nuke.fun <- function(dat, inds, i.pred, fit.pred, x.pred) + { + lm.b <- glm(fit+resid[inds] ~ date+log(cap)+ne+ct+log(cum.n)+pt, + data = dat) + pred.b <- predict(lm.b, x.pred) + c(coef(lm.b), pred.b - (fit.pred + dat$resid[i.pred])) + } > > nuke.boot <- boot(nuke.data, nuke.fun, R = 999, m = 1, + fit.pred = new.fit, x.pred = new.data) > # The bootstrap prediction squared error would then be found by > mean(nuke.boot$t[, 8]^2) [1] 0.088157 > # Basic bootstrap prediction limits would be > new.fit - sort(nuke.boot$t[, 8])[c(975, 25)] [1] 6.1603 7.2988 > > > # Finally a parametric bootstrap. For this example we shall look > # at the air-conditioning data. In this example our aim is to test > # the hypothesis that the true value of the index is 1 (i.e. that > # the data come from an exponential distribution) against the > # alternative that the data come from a gamma distribution with > # index not equal to 1. > air.fun <- function(data) { + ybar <- mean(data$hours) + para <- c(log(ybar), mean(log(data$hours))) + ll <- function(k) { + if (k <= 0) 1e200 else lgamma(k)-k*(log(k)-1-para[1]+para[2]) + } + khat <- nlm(ll, ybar^2/var(data$hours))$estimate + c(ybar, khat) + } > > air.rg <- function(data, mle) { + # Function to generate random exponential variates. + # mle will contain the mean of the original data + out <- data + out$hours <- rexp(nrow(out), 1/mle) + out + } > > air.boot <- boot(aircondit, air.fun, R = 999, sim = "parametric", + ran.gen = air.rg, mle = mean(aircondit$hours)) > > # The bootstrap p-value can then be approximated by > sum(abs(air.boot$t[,2]-1) > abs(air.boot$t0[2]-1))/(1+air.boot$R) [1] 0.461 > ## Don't show: > options(op) > ## End(Don't show) > > > > cleanEx() > nameEx("boot.array") > ### * boot.array > > flush(stderr()); flush(stdout()) > > ### Name: boot.array > ### Title: Bootstrap Resampling Arrays > ### Aliases: boot.array > ### Keywords: nonparametric > > ### ** Examples > > # A frequency array for a nonparametric bootstrap > city.boot <- boot(city, corr, R = 40, stype = "w") > boot.array(city.boot) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 1 1 3 1 1 0 1 0 1 1 [2,] 0 0 1 3 1 2 1 1 1 0 [3,] 0 0 0 1 3 2 0 1 2 1 [4,] 0 2 2 2 0 1 0 1 0 2 [5,] 1 1 1 0 1 2 0 2 2 0 [6,] 0 1 2 0 2 1 0 1 2 1 [7,] 3 0 0 1 1 2 0 1 1 1 [8,] 0 4 1 1 1 0 1 2 0 0 [9,] 0 1 4 0 1 0 2 2 0 0 [10,] 1 1 1 1 1 1 1 2 0 1 [11,] 0 0 3 0 2 1 1 1 0 2 [12,] 2 3 1 0 0 1 0 0 2 1 [13,] 1 0 0 1 2 0 2 1 1 2 [14,] 0 0 2 3 0 0 2 0 2 1 [15,] 2 0 1 1 1 1 0 2 1 1 [16,] 1 1 0 2 2 1 1 1 1 0 [17,] 1 0 1 2 2 1 2 1 0 0 [18,] 0 2 0 0 2 2 0 1 1 2 [19,] 1 1 0 2 0 1 2 0 1 2 [20,] 0 0 0 1 2 1 1 1 0 4 [21,] 0 0 2 0 1 1 3 0 0 3 [22,] 1 3 2 2 0 1 1 0 0 0 [23,] 0 0 3 1 2 1 2 0 1 0 [24,] 0 1 1 2 1 2 0 1 0 2 [25,] 0 0 2 1 0 0 3 2 1 1 [26,] 0 2 4 1 1 1 0 0 0 1 [27,] 2 3 1 0 2 0 0 2 0 0 [28,] 1 1 0 1 1 0 0 4 2 0 [29,] 2 2 0 1 1 0 1 0 1 2 [30,] 0 1 0 1 1 2 0 1 3 1 [31,] 1 2 0 2 2 0 1 1 0 1 [32,] 1 4 0 1 0 2 0 1 1 0 [33,] 0 1 0 5 1 0 0 1 1 1 [34,] 0 2 0 1 3 1 1 1 0 1 [35,] 0 2 3 1 1 1 0 0 1 1 [36,] 1 2 1 1 1 1 2 0 1 0 [37,] 1 1 1 0 0 1 2 2 2 0 [38,] 2 3 0 3 0 1 0 0 1 0 [39,] 0 0 1 2 3 0 0 2 1 1 [40,] 0 1 1 0 3 0 2 2 0 1 > > perm.cor <- function(d,i) cor(d$x,d$u[i]) > city.perm <- boot(city, perm.cor, R = 40, sim = "permutation") > boot.array(city.perm, indices = TRUE) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 7 2 8 10 6 4 9 3 1 5 [2,] 10 4 6 8 5 3 2 1 9 7 [3,] 5 10 6 2 3 8 1 9 7 4 [4,] 10 1 4 8 9 5 3 6 7 2 [5,] 1 3 5 8 2 9 6 7 10 4 [6,] 10 4 3 2 1 9 8 5 7 6 [7,] 2 5 1 4 10 9 3 8 7 6 [8,] 5 9 6 3 1 2 4 10 8 7 [9,] 8 2 7 10 4 3 1 6 9 5 [10,] 1 9 3 2 8 6 7 4 5 10 [11,] 6 7 10 5 3 9 2 8 4 1 [12,] 9 5 1 6 10 8 3 2 7 4 [13,] 9 7 3 1 8 5 4 6 2 10 [14,] 7 1 3 2 9 6 10 4 5 8 [15,] 4 9 6 3 2 1 10 8 7 5 [16,] 9 6 1 7 5 2 8 4 10 3 [17,] 7 2 3 6 10 4 1 9 5 8 [18,] 2 4 3 1 5 8 6 10 9 7 [19,] 4 9 6 1 10 3 7 2 5 8 [20,] 6 3 7 5 8 4 2 10 1 9 [21,] 9 10 2 6 7 5 8 4 3 1 [22,] 10 4 5 9 8 3 1 2 6 7 [23,] 9 2 5 1 10 4 6 8 7 3 [24,] 9 4 3 6 8 2 1 10 5 7 [25,] 10 3 8 2 5 7 1 9 6 4 [26,] 7 8 3 9 4 1 5 2 10 6 [27,] 4 8 1 5 3 6 10 9 7 2 [28,] 8 5 7 4 10 3 2 1 9 6 [29,] 8 10 2 4 7 3 9 6 1 5 [30,] 1 5 7 9 3 6 4 10 2 8 [31,] 10 9 7 5 4 1 2 6 3 8 [32,] 8 5 1 6 3 7 10 4 9 2 [33,] 1 6 8 5 2 7 9 4 10 3 [34,] 8 5 7 1 9 2 6 3 10 4 [35,] 4 5 9 2 7 6 8 3 1 10 [36,] 5 9 10 1 3 7 2 8 4 6 [37,] 8 2 7 9 10 1 3 4 6 5 [38,] 5 8 1 7 4 3 9 10 6 2 [39,] 10 7 6 4 8 1 3 5 9 2 [40,] 2 1 8 3 4 6 9 10 7 5 > > > > cleanEx() > nameEx("boot.ci") > ### * boot.ci > > flush(stderr()); flush(stdout()) > > ### Name: boot.ci > ### Title: Nonparametric Bootstrap Confidence Intervals > ### Aliases: boot.ci > ### Keywords: nonparametric htest > > ### ** Examples > > # confidence intervals for the city data > ratio <- function(d, w) sum(d$x * w)/sum(d$u * w) > city.boot <- boot(city, ratio, R = 999, stype = "w", sim = "ordinary") > boot.ci(city.boot, conf = c(0.90, 0.95), + type = c("norm", "basic", "perc", "bca")) BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 999 bootstrap replicates CALL : boot.ci(boot.out = city.boot, conf = c(0.9, 0.95), type = c("norm", "basic", "perc", "bca")) Intervals : Level Normal Basic 90% ( 1.124, 1.837 ) ( 1.059, 1.740 ) 95% ( 1.056, 1.905 ) ( 0.932, 1.799 ) Level Percentile BCa 90% ( 1.301, 1.982 ) ( 1.301, 1.984 ) 95% ( 1.242, 2.109 ) ( 1.243, 2.110 ) Calculations and Intervals on Original Scale > > # studentized confidence interval for the two sample > # difference of means problem using the final two series > # of the gravity data. > diff.means <- function(d, f) + { n <- nrow(d) + gp1 <- 1:table(as.numeric(d$series))[1] + m1 <- sum(d[gp1,1] * f[gp1])/sum(f[gp1]) + m2 <- sum(d[-gp1,1] * f[-gp1])/sum(f[-gp1]) + ss1 <- sum(d[gp1,1]^2 * f[gp1]) - (m1 * m1 * sum(f[gp1])) + ss2 <- sum(d[-gp1,1]^2 * f[-gp1]) - (m2 * m2 * sum(f[-gp1])) + c(m1 - m2, (ss1 + ss2)/(sum(f) - 2)) + } > grav1 <- gravity[as.numeric(gravity[,2]) >= 7, ] > grav1.boot <- boot(grav1, diff.means, R = 999, stype = "f", + strata = grav1[ ,2]) > boot.ci(grav1.boot, type = c("stud", "norm")) BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 999 bootstrap replicates CALL : boot.ci(boot.out = grav1.boot, type = c("stud", "norm")) Intervals : Level Normal Studentized 95% (-5.880, 0.183 ) (-7.059, -0.101 ) Calculations and Intervals on Original Scale > > # Nonparametric confidence intervals for mean failure time > # of the air-conditioning data as in Example 5.4 of Davison > # and Hinkley (1997) > mean.fun <- function(d, i) + { m <- mean(d$hours[i]) + n <- length(i) + v <- (n-1)*var(d$hours[i])/n^2 + c(m, v) + } > air.boot <- boot(aircondit, mean.fun, R = 999) > boot.ci(air.boot, type = c("norm", "basic", "perc", "stud")) BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 999 bootstrap replicates CALL : boot.ci(boot.out = air.boot, type = c("norm", "basic", "perc", "stud")) Intervals : Level Normal Basic 95% ( 35.5, 181.9 ) ( 26.0, 170.6 ) Level Studentized Percentile 95% ( 47.9, 294.5 ) ( 45.6, 190.2 ) Calculations and Intervals on Original Scale > > # Now using the log transformation > # There are two ways of doing this and they both give the > # same intervals. > > # Method 1 > boot.ci(air.boot, type = c("norm", "basic", "perc", "stud"), + h = log, hdot = function(x) 1/x) BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 999 bootstrap replicates CALL : boot.ci(boot.out = air.boot, type = c("norm", "basic", "perc", "stud"), h = log, hdot = function(x) 1/x) Intervals : Level Normal Basic 95% ( 4.035, 5.469 ) ( 4.118, 5.546 ) Level Studentized Percentile 95% ( 3.959, 5.808 ) ( 3.820, 5.248 ) Calculations and Intervals on Transformed Scale > > # Method 2 > vt0 <- air.boot$t0[2]/air.boot$t0[1]^2 > vt <- air.boot$t[, 2]/air.boot$t[ ,1]^2 > boot.ci(air.boot, type = c("norm", "basic", "perc", "stud"), + t0 = log(air.boot$t0[1]), t = log(air.boot$t[,1]), + var.t0 = vt0, var.t = vt) BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 999 bootstrap replicates CALL : boot.ci(boot.out = air.boot, type = c("norm", "basic", "perc", "stud"), var.t0 = vt0, var.t = vt, t0 = log(air.boot$t0[1]), t = log(air.boot$t[, 1])) Intervals : Level Normal Basic 95% ( 4.069, 5.435 ) ( 4.118, 5.546 ) Level Studentized Percentile 95% ( 3.959, 5.808 ) ( 3.820, 5.248 ) Calculations and Intervals on Original Scale > > > > cleanEx() > nameEx("censboot") > ### * censboot > > flush(stderr()); flush(stdout()) > > ### Name: censboot > ### Title: Bootstrap for Censored Data > ### Aliases: censboot cens.return > ### Keywords: survival > > ### ** Examples > > library(survival) Attaching package: ‘survival’ The following object is masked from ‘package:boot’: aml > # Example 3.9 of Davison and Hinkley (1997) does a bootstrap on some > # remission times for patients with a type of leukaemia. The patients > # were divided into those who received maintenance chemotherapy and > # those who did not. Here we are interested in the median remission > # time for the two groups. > data(aml, package = "boot") # not the version in survival. > aml.fun <- function(data) { + surv <- survfit(Surv(time, cens) ~ group, data = data) + out <- NULL + st <- 1 + for (s in 1:length(surv$strata)) { + inds <- st:(st + surv$strata[s]-1) + md <- min(surv$time[inds[1-surv$surv[inds] >= 0.5]]) + st <- st + surv$strata[s] + out <- c(out, md) + } + out + } > aml.case <- censboot(aml, aml.fun, R = 499, strata = aml$group) Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf > > # Now we will look at the same statistic using the conditional > # bootstrap and the weird bootstrap. For the conditional bootstrap > # the survival distribution is stratified but the censoring > # distribution is not. > > aml.s1 <- survfit(Surv(time, cens) ~ group, data = aml) > aml.s2 <- survfit(Surv(time-0.001*cens, 1-cens) ~ 1, data = aml) > aml.cond <- censboot(aml, aml.fun, R = 499, strata = aml$group, + F.surv = aml.s1, G.surv = aml.s2, sim = "cond") Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf > > > # For the weird bootstrap we must redefine our function slightly since > # the data will not contain the group number. > aml.fun1 <- function(data, str) { + surv <- survfit(Surv(data[, 1], data[, 2]) ~ str) + out <- NULL + st <- 1 + for (s in 1:length(surv$strata)) { + inds <- st:(st + surv$strata[s] - 1) + md <- min(surv$time[inds[1-surv$surv[inds] >= 0.5]]) + st <- st + surv$strata[s] + out <- c(out, md) + } + out + } > aml.wei <- censboot(cbind(aml$time, aml$cens), aml.fun1, R = 499, + strata = aml$group, F.surv = aml.s1, sim = "weird") Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) : no non-missing arguments to min; returning Inf > > # Now for an example where a cox regression model has been fitted > # the data we will look at the melanoma data of Example 7.6 from > # Davison and Hinkley (1997). The fitted model assumes that there > # is a different survival distribution for the ulcerated and > # non-ulcerated groups but that the thickness of the tumour has a > # common effect. We will also assume that the censoring distribution > # is different in different age groups. The statistic of interest > # is the linear predictor. This is returned as the values at a > # number of equally spaced points in the range of interest. > data(melanoma, package = "boot") > library(splines)# for ns > mel.cox <- coxph(Surv(time, status == 1) ~ ns(thickness, df=4) + strata(ulcer), + data = melanoma) > mel.surv <- survfit(mel.cox) > agec <- cut(melanoma$age, c(0, 39, 49, 59, 69, 100)) > mel.cens <- survfit(Surv(time - 0.001*(status == 1), status != 1) ~ + strata(agec), data = melanoma) > mel.fun <- function(d) { + t1 <- ns(d$thickness, df=4) + cox <- coxph(Surv(d$time, d$status == 1) ~ t1+strata(d$ulcer)) + ind <- !duplicated(d$thickness) + u <- d$thickness[!ind] + eta <- cox$linear.predictors[!ind] + sp <- smooth.spline(u, eta, df=20) + th <- seq(from = 0.25, to = 10, by = 0.25) + predict(sp, th)$y + } > mel.str <- cbind(melanoma$ulcer, agec) > > # this is slow! > mel.mod <- censboot(melanoma, mel.fun, R = 499, F.surv = mel.surv, + G.surv = mel.cens, cox = mel.cox, strata = mel.str, sim = "model") > # To plot the original predictor and a 95% pointwise envelope for it > mel.env <- envelope(mel.mod)$point > th <- seq(0.25, 10, by = 0.25) > plot(th, mel.env[1, ], ylim = c(-2, 2), + xlab = "thickness (mm)", ylab = "linear predictor", type = "n") > lines(th, mel.mod$t0, lty = 1) > matlines(th, t(mel.env), lty = 2) > > > > cleanEx() detaching ‘package:splines’, ‘package:survival’ > nameEx("control") > ### * control > > flush(stderr()); flush(stdout()) > > ### Name: control > ### Title: Control Variate Calculations > ### Aliases: control > ### Keywords: nonparametric > > ### ** Examples > > # Use of control variates for the variance of the air-conditioning data > mean.fun <- function(d, i) + { m <- mean(d$hours[i]) + n <- nrow(d) + v <- (n-1)*var(d$hours[i])/n^2 + c(m, v) + } > air.boot <- boot(aircondit, mean.fun, R = 999) > control(air.boot, index = 2, bias.adj = TRUE) [1] -6.298101 > air.cont <- control(air.boot, index = 2) > # Now let us try the variance on the log scale. > air.cont1 <- control(air.boot, t0 = log(air.boot$t0[2]), + t = log(air.boot$t[, 2])) > > > > cleanEx() > nameEx("cv.glm") > ### * cv.glm > > flush(stderr()); flush(stdout()) > > ### Name: cv.glm > ### Title: Cross-validation for Generalized Linear Models > ### Aliases: cv.glm > ### Keywords: regression > > ### ** Examples > > # leave-one-out and 6-fold cross-validation prediction error for > # the mammals data set. > data(mammals, package="MASS") > mammals.glm <- glm(log(brain) ~ log(body), data = mammals) > (cv.err <- cv.glm(mammals, mammals.glm)$delta) [1] 0.4918650 0.4916571 > (cv.err.6 <- cv.glm(mammals, mammals.glm, K = 6)$delta) [1] 0.4851491 0.4834641 > > # As this is a linear model we could calculate the leave-one-out > # cross-validation estimate without any extra model-fitting. > muhat <- fitted(mammals.glm) > mammals.diag <- glm.diag(mammals.glm) > (cv.err <- mean((mammals.glm$y - muhat)^2/(1 - mammals.diag$h)^2)) [1] 0.491865 > > > # leave-one-out and 11-fold cross-validation prediction error for > # the nodal data set. Since the response is a binary variable an > # appropriate cost function is > cost <- function(r, pi = 0) mean(abs(r-pi) > 0.5) > > nodal.glm <- glm(r ~ stage+xray+acid, binomial, data = nodal) > (cv.err <- cv.glm(nodal, nodal.glm, cost, K = nrow(nodal))$delta) [1] 0.1886792 0.1886792 > (cv.11.err <- cv.glm(nodal, nodal.glm, cost, K = 11)$delta) [1] 0.2264151 0.2217871 > > > > cleanEx() > nameEx("empinf") > ### * empinf > > flush(stderr()); flush(stdout()) > > ### Name: empinf > ### Title: Empirical Influence Values > ### Aliases: empinf > ### Keywords: nonparametric math > > ### ** Examples > > # The empirical influence values for the ratio of means in > # the city data. > ratio <- function(d, w) sum(d$x *w)/sum(d$u*w) > empinf(data = city, statistic = ratio) [1] -1.04367815 -0.58417763 -0.37092459 -0.18958996 0.03164142 0.10544878 [7] 0.09236345 0.20365074 1.02178280 0.73381132 > city.boot <- boot(city, ratio, 499, stype="w") > empinf(boot.out = city.boot, type = "reg") 1 1 1 1 1 1 -1.13619987 -0.69728210 -0.45301061 -0.27615882 0.02108999 0.14896336 1 1 1 1 0.09746429 0.20622340 1.18798956 0.90092079 > > # A statistic that may be of interest in the difference of means > # problem is the t-statistic for testing equality of means. In > # the bootstrap we get replicates of the difference of means and > # the variance of that statistic and then want to use this output > # to get the empirical influence values of the t-statistic. > grav1 <- gravity[as.numeric(gravity[,2]) >= 7,] > grav.fun <- function(dat, w) { + strata <- tapply(dat[, 2], as.numeric(dat[, 2])) + d <- dat[, 1] + ns <- tabulate(strata) + w <- w/tapply(w, strata, sum)[strata] + mns <- as.vector(tapply(d * w, strata, sum)) # drop names + mn2 <- tapply(d * d * w, strata, sum) + s2hat <- sum((mn2 - mns^2)/ns) + c(mns[2] - mns[1], s2hat) + } > > grav.boot <- boot(grav1, grav.fun, R = 499, stype = "w", + strata = grav1[, 2]) > > # Since the statistic of interest is a function of the bootstrap > # statistics, we must calculate the bootstrap replicates and pass > # them to empinf using the t argument. > grav.z <- (grav.boot$t[,1]-grav.boot$t0[1])/sqrt(grav.boot$t[,2]) > empinf(boot.out = grav.boot, t = grav.z) 1 1 1 1 1 1 1 -2.9326019 -1.3760327 -2.4400720 -1.2175846 0.2795352 -0.8258764 -0.8156286 1 1 1 1 1 1 2 -0.5573332 -1.1275252 -3.1603140 1.2840693 3.5434781 9.3458860 2.6692589 2 2 2 2 2 2 2 4.4496570 3.6948000 0.9929002 -3.0100985 -3.2237464 -2.5493305 -0.6551745 2 2 2 2 2 1.9065308 0.4980530 -1.6219628 -1.6980508 -1.4528364 > > > > cleanEx() > nameEx("envelope") > ### * envelope > > flush(stderr()); flush(stdout()) > > ### Name: envelope > ### Title: Confidence Envelopes for Curves > ### Aliases: envelope > ### Keywords: dplot htest > > ### ** Examples > > # Testing whether the final series of measurements of the gravity data > # may come from a normal distribution. This is done in Examples 4.7 > # and 4.8 of Davison and Hinkley (1997). > grav1 <- gravity$g[gravity$series == 8] > grav.z <- (grav1 - mean(grav1))/sqrt(var(grav1)) > grav.gen <- function(dat, mle) rnorm(length(dat)) > grav.qqboot <- boot(grav.z, sort, R = 999, sim = "parametric", + ran.gen = grav.gen) > grav.qq <- qqnorm(grav.z, plot.it = FALSE) > grav.qq <- lapply(grav.qq, sort) > plot(grav.qq, ylim = c(-3.5, 3.5), ylab = "Studentized Order Statistics", + xlab = "Normal Quantiles") > grav.env <- envelope(grav.qqboot, level = 0.9) > lines(grav.qq$x, grav.env$point[1, ], lty = 4) > lines(grav.qq$x, grav.env$point[2, ], lty = 4) > lines(grav.qq$x, grav.env$overall[1, ], lty = 1) > lines(grav.qq$x, grav.env$overall[2, ], lty = 1) > > > > cleanEx() > nameEx("exp.tilt") > ### * exp.tilt > > flush(stderr()); flush(stdout()) > > ### Name: exp.tilt > ### Title: Exponential Tilting > ### Aliases: exp.tilt > ### Keywords: nonparametric smooth > > ### ** Examples > > # Example 9.8 of Davison and Hinkley (1997) requires tilting the resampling > # distribution of the studentized statistic to be centred at the observed > # value of the test statistic 1.84. This can be achieved as follows. > grav1 <- gravity[as.numeric(gravity[,2]) >=7 , ] > grav.fun <- function(dat, w, orig) { + strata <- tapply(dat[, 2], as.numeric(dat[, 2])) + d <- dat[, 1] + ns <- tabulate(strata) + w <- w/tapply(w, strata, sum)[strata] + mns <- as.vector(tapply(d * w, strata, sum)) # drop names + mn2 <- tapply(d * d * w, strata, sum) + s2hat <- sum((mn2 - mns^2)/ns) + c(mns[2]-mns[1], s2hat, (mns[2]-mns[1]-orig)/sqrt(s2hat)) + } > grav.z0 <- grav.fun(grav1, rep(1, 26), 0) > grav.L <- empinf(data = grav1, statistic = grav.fun, stype = "w", + strata = grav1[,2], index = 3, orig = grav.z0[1]) > grav.tilt <- exp.tilt(grav.L, grav.z0[3], strata = grav1[,2]) > boot(grav1, grav.fun, R = 499, stype = "w", weights = grav.tilt$p, + strata = grav1[,2], orig = grav.z0[1]) STRATIFIED WEIGHTED BOOTSTRAP Call: boot(data = grav1, statistic = grav.fun, R = 499, stype = "w", strata = grav1[, 2], weights = grav.tilt$p, orig = grav.z0[1]) Bootstrap Statistics : original bias std. error mean(t*) t1* 2.846154 -0.3661063 1.705171 5.702944 t2* 2.392353 -0.3538294 1.002889 3.444050 t3* 0.000000 -0.5160619 1.314298 1.473456 > > > > cleanEx() > nameEx("glm.diag.plots") > ### * glm.diag.plots > > flush(stderr()); flush(stdout()) > > ### Name: glm.diag.plots > ### Title: Diagnostics plots for generalized linear models > ### Aliases: glm.diag.plots > ### Keywords: regression dplot hplot > > ### ** Examples > > # In this example we look at the leukaemia data which was looked at in > # Example 7.1 of Davison and Hinkley (1997) > data(leuk, package = "MASS") > leuk.mod <- glm(time ~ ag-1+log10(wbc), family = Gamma(log), data = leuk) > leuk.diag <- glm.diag(leuk.mod) > glm.diag.plots(leuk.mod, leuk.diag) > > > > cleanEx() > nameEx("jack.after.boot") > ### * jack.after.boot > > flush(stderr()); flush(stdout()) > > ### Name: jack.after.boot > ### Title: Jackknife-after-Bootstrap Plots > ### Aliases: jack.after.boot > ### Keywords: hplot nonparametric > > ### ** Examples > > # To draw the jackknife-after-bootstrap plot for the head size data as in > # Example 3.24 of Davison and Hinkley (1997) > frets.fun <- function(data, i) { + pcorr <- function(x) { + # Function to find the correlations and partial correlations between + # the four measurements. + v <- cor(x) + v.d <- diag(var(x)) + iv <- solve(v) + iv.d <- sqrt(diag(iv)) + iv <- - diag(1/iv.d) %*% iv %*% diag(1/iv.d) + q <- NULL + n <- nrow(v) + for (i in 1:(n-1)) + q <- rbind( q, c(v[i, 1:i], iv[i,(i+1):n]) ) + q <- rbind( q, v[n, ] ) + diag(q) <- round(diag(q)) + q + } + d <- data[i, ] + v <- pcorr(d) + c(v[1,], v[2,], v[3,], v[4,]) + } > frets.boot <- boot(log(as.matrix(frets)), frets.fun, R = 999) > # we will concentrate on the partial correlation between head breadth > # for the first son and head length for the second. This is the 7th > # element in the output of frets.fun so we set index = 7 > jack.after.boot(frets.boot, useJ = FALSE, stinf = FALSE, index = 7) > > > > cleanEx() > nameEx("k3.linear") > ### * k3.linear > > flush(stderr()); flush(stdout()) > > ### Name: k3.linear > ### Title: Linear Skewness Estimate > ### Aliases: k3.linear > ### Keywords: nonparametric > > ### ** Examples > > # To estimate the skewness of the ratio of means for the city data. > ratio <- function(d, w) sum(d$x * w)/sum(d$u * w) > k3.linear(empinf(data = city, statistic = ratio)) [1] 7.831452e-05 > > > > cleanEx() > nameEx("linear.approx") > ### * linear.approx > > flush(stderr()); flush(stdout()) > > ### Name: linear.approx > ### Title: Linear Approximation of Bootstrap Replicates > ### Aliases: linear.approx > ### Keywords: nonparametric > > ### ** Examples > > # Using the city data let us look at the linear approximation to the > # ratio statistic and its logarithm. We compare these with the > # corresponding plots for the bigcity data > > ratio <- function(d, w) sum(d$x * w)/sum(d$u * w) > city.boot <- boot(city, ratio, R = 499, stype = "w") > bigcity.boot <- boot(bigcity, ratio, R = 499, stype = "w") > op <- par(pty = "s", mfrow = c(2, 2)) > > # The first plot is for the city data ratio statistic. > city.lin1 <- linear.approx(city.boot) > lim <- range(c(city.boot$t,city.lin1)) > plot(city.boot$t, city.lin1, xlim = lim, ylim = lim, + main = "Ratio; n=10", xlab = "t*", ylab = "tL*") > abline(0, 1) > > # Now for the log of the ratio statistic for the city data. > city.lin2 <- linear.approx(city.boot,t0 = log(city.boot$t0), + t = log(city.boot$t)) > lim <- range(c(log(city.boot$t),city.lin2)) > plot(log(city.boot$t), city.lin2, xlim = lim, ylim = lim, + main = "Log(Ratio); n=10", xlab = "t*", ylab = "tL*") > abline(0, 1) > > # The ratio statistic for the bigcity data. > bigcity.lin1 <- linear.approx(bigcity.boot) > lim <- range(c(bigcity.boot$t,bigcity.lin1)) > plot(bigcity.lin1, bigcity.boot$t, xlim = lim, ylim = lim, + main = "Ratio; n=49", xlab = "t*", ylab = "tL*") > abline(0, 1) > > # Finally the log of the ratio statistic for the bigcity data. > bigcity.lin2 <- linear.approx(bigcity.boot,t0 = log(bigcity.boot$t0), + t = log(bigcity.boot$t)) > lim <- range(c(log(bigcity.boot$t),bigcity.lin2)) > plot(bigcity.lin2, log(bigcity.boot$t), xlim = lim, ylim = lim, + main = "Log(Ratio); n=49", xlab = "t*", ylab = "tL*") > abline(0, 1) > > par(op) > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("lines.saddle.distn") > ### * lines.saddle.distn > > flush(stderr()); flush(stdout()) > > ### Name: lines.saddle.distn > ### Title: Add a Saddlepoint Approximation to a Plot > ### Aliases: lines.saddle.distn > ### Keywords: aplot smooth nonparametric > > ### ** Examples > > # In this example we show how a plot such as that in Figure 9.9 of > # Davison and Hinkley (1997) may be produced. Note the large number of > # bootstrap replicates required in this example. > expdata <- rexp(12) > vfun <- function(d, i) { + n <- length(d) + (n-1)/n*var(d[i]) + } > exp.boot <- boot(expdata,vfun, R = 9999) > exp.L <- (expdata - mean(expdata))^2 - exp.boot$t0 > exp.tL <- linear.approx(exp.boot, L = exp.L) > hist(exp.tL, nclass = 50, probability = TRUE) > exp.t0 <- c(0, sqrt(var(exp.boot$t))) > exp.sp <- saddle.distn(A = exp.L/12,wdist = "m", t0 = exp.t0) > > # The saddlepoint approximation in this case is to the density of > # t-t0 and so t0 must be added for the plot. > lines(exp.sp, h = function(u, t0) u+t0, J = function(u, t0) 1, + t0 = exp.boot$t0) > > > > cleanEx() > nameEx("norm.ci") > ### * norm.ci > > flush(stderr()); flush(stdout()) > > ### Name: norm.ci > ### Title: Normal Approximation Confidence Intervals > ### Aliases: norm.ci > ### Keywords: htest > > ### ** Examples > > # In Example 5.1 of Davison and Hinkley (1997), normal approximation > # confidence intervals are found for the air-conditioning data. > air.mean <- mean(aircondit$hours) > air.n <- nrow(aircondit) > air.v <- air.mean^2/air.n > norm.ci(t0 = air.mean, var.t0 = air.v) conf [1,] 0.95 46.93055 169.2361 > exp(norm.ci(t0 = log(air.mean), var.t0 = 1/air.n)[2:3]) [1] 61.38157 190.31782 > > # Now a more complicated example - the ratio estimate for the city data. > ratio <- function(d, w) + sum(d$x * w)/sum(d$u *w) > city.v <- var.linear(empinf(data = city, statistic = ratio)) > norm.ci(t0 = ratio(city,rep(0.1,10)), var.t0 = city.v) conf [1,] 0.95 1.167046 1.873579 > > > > cleanEx() > nameEx("plot.boot") > ### * plot.boot > > flush(stderr()); flush(stdout()) > > ### Name: plot.boot > ### Title: Plots of the Output of a Bootstrap Simulation > ### Aliases: plot.boot > ### Keywords: hplot nonparametric > > ### ** Examples > > # We fit an exponential model to the air-conditioning data and use > # that for a parametric bootstrap. Then we look at plots of the > # resampled means. > air.rg <- function(data, mle) rexp(length(data), 1/mle) > > air.boot <- boot(aircondit$hours, mean, R = 999, sim = "parametric", + ran.gen = air.rg, mle = mean(aircondit$hours)) > plot(air.boot) > > # In the difference of means example for the last two series of the > # gravity data > grav1 <- gravity[as.numeric(gravity[, 2]) >= 7, ] > grav.fun <- function(dat, w) { + strata <- tapply(dat[, 2], as.numeric(dat[, 2])) + d <- dat[, 1] + ns <- tabulate(strata) + w <- w/tapply(w, strata, sum)[strata] + mns <- as.vector(tapply(d * w, strata, sum)) # drop names + mn2 <- tapply(d * d * w, strata, sum) + s2hat <- sum((mn2 - mns^2)/ns) + c(mns[2] - mns[1], s2hat) + } > > grav.boot <- boot(grav1, grav.fun, R = 499, stype = "w", strata = grav1[, 2]) > plot(grav.boot) > # now suppose we want to look at the studentized differences. > grav.z <- (grav.boot$t[, 1]-grav.boot$t0[1])/sqrt(grav.boot$t[, 2]) > plot(grav.boot, t = grav.z, t0 = 0) > > # In this example we look at the one of the partial correlations for the > # head dimensions in the dataset frets. > frets.fun <- function(data, i) { + pcorr <- function(x) { + # Function to find the correlations and partial correlations between + # the four measurements. + v <- cor(x) + v.d <- diag(var(x)) + iv <- solve(v) + iv.d <- sqrt(diag(iv)) + iv <- - diag(1/iv.d) %*% iv %*% diag(1/iv.d) + q <- NULL + n <- nrow(v) + for (i in 1:(n-1)) + q <- rbind( q, c(v[i, 1:i], iv[i,(i+1):n]) ) + q <- rbind( q, v[n, ] ) + diag(q) <- round(diag(q)) + q + } + d <- data[i, ] + v <- pcorr(d) + c(v[1,], v[2,], v[3,], v[4,]) + } > frets.boot <- boot(log(as.matrix(frets)), frets.fun, R = 999) > plot(frets.boot, index = 7, jack = TRUE, stinf = FALSE, useJ = FALSE) > > > > cleanEx() > nameEx("saddle") > ### * saddle > > flush(stderr()); flush(stdout()) > > ### Name: saddle > ### Title: Saddlepoint Approximations for Bootstrap Statistics > ### Aliases: saddle > ### Keywords: smooth nonparametric > > ### ** Examples > > # To evaluate the bootstrap distribution of the mean failure time of > # air-conditioning equipment at 80 hours > saddle(A = aircondit$hours/12, u = 80) $spa pdf cdf 0.01005866 0.24446677 $zeta.hat [1] -0.02580078 > > # Alternatively this can be done using a conditional poisson > saddle(A = cbind(aircondit$hours/12,1), u = c(80, 12), + wdist = "p", type = "cond") Warning in dpois(y, mu, log = TRUE) : non-integer x = 10.090909 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.909091 Warning in dpois(y, mu, log = TRUE) : non-integer x = 10.090909 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.909091 $spa pdf cdf 0.01005943 0.24438736 $zeta.hat A1 A2 -0.02580805 0.89261577 $zeta2.hat [1] 0.6931472 > > # To use the Lugananni-Rice approximation to this > saddle(A = cbind(aircondit$hours/12,1), u = c(80, 12), + wdist = "p", type = "cond", + LR = TRUE) Warning in dpois(y, mu, log = TRUE) : non-integer x = 10.090909 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.909091 Warning in dpois(y, mu, log = TRUE) : non-integer x = 10.090909 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.909091 $spa pdf cdf 0.01005943 0.24447362 $zeta.hat A1 A2 -0.02580805 0.89261577 $zeta2.hat [1] 0.6931472 > > # Example 9.16 of Davison and Hinkley (1997) calculates saddlepoint > # approximations to the distribution of the ratio statistic for the > # city data. Since the statistic is not in itself a linear combination > # of random Variables, its distribution cannot be found directly. > # Instead the statistic is expressed as the solution to a linear > # estimating equation and hence its distribution can be found. We > # get the saddlepoint approximation to the pdf and cdf evaluated at > # t = 1.25 as follows. > jacobian <- function(dat,t,zeta) + { + p <- exp(zeta*(dat$x-t*dat$u)) + abs(sum(dat$u*p)/sum(p)) + } > city.sp1 <- saddle(A = city$x-1.25*city$u, u = 0) > city.sp1$spa[1] <- jacobian(city, 1.25, city.sp1$zeta.hat) * city.sp1$spa[1] > city.sp1 $spa pdf cdf 0.05565040 0.02436306 $zeta.hat [1] -0.02435547 > > > > cleanEx() > nameEx("saddle.distn") > ### * saddle.distn > > flush(stderr()); flush(stdout()) > > ### Name: saddle.distn > ### Title: Saddlepoint Distribution Approximations for Bootstrap Statistics > ### Aliases: saddle.distn > ### Keywords: nonparametric smooth dplot > > ### ** Examples > > # The bootstrap distribution of the mean of the air-conditioning > # failure data: fails to find value on R (and probably on S too) > air.t0 <- c(mean(aircondit$hours), sqrt(var(aircondit$hours)/12)) > ## Not run: saddle.distn(A = aircondit$hours/12, t0 = air.t0) > > # alternatively using the conditional poisson > saddle.distn(A = cbind(aircondit$hours/12, 1), u = 12, wdist = "p", + type = "cond", t0 = air.t0) Warning in dpois(y, mu, log = TRUE) : non-integer x = 11.344718 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.655282 Warning in dpois(y, mu, log = TRUE) : non-integer x = 11.344718 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.655282 Warning in dpois(y, mu, log = TRUE) : non-integer x = 11.832240 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.167760 Warning in dpois(y, mu, log = TRUE) : non-integer x = 11.832240 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.167760 Warning in dpois(y, mu, log = TRUE) : non-integer x = 7.444538 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.555462 Warning in dpois(y, mu, log = TRUE) : non-integer x = 7.444538 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.555462 Warning in dpois(y, mu, log = TRUE) : non-integer x = 5.494449 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.505551 Warning in dpois(y, mu, log = TRUE) : non-integer x = 5.494449 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.505551 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.594269 Warning in dpois(y, mu, log = TRUE) : non-integer x = 10.405731 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.594269 Warning in dpois(y, mu, log = TRUE) : non-integer x = 10.405731 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.544359 Warning in dpois(y, mu, log = TRUE) : non-integer x = 8.455641 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.544359 Warning in dpois(y, mu, log = TRUE) : non-integer x = 8.455641 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.519404 Warning in dpois(y, mu, log = TRUE) : non-integer x = 7.480596 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.519404 Warning in dpois(y, mu, log = TRUE) : non-integer x = 7.480596 Warning in dpois(y, mu, log = TRUE) : non-integer x = 11.561394 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.438606 Warning in dpois(y, mu, log = TRUE) : non-integer x = 11.561394 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.438606 Warning in dpois(y, mu, log = TRUE) : non-integer x = 11.290549 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.709451 Warning in dpois(y, mu, log = TRUE) : non-integer x = 11.290549 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.709451 Warning in dpois(y, mu, log = TRUE) : non-integer x = 11.019703 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.980297 Warning in dpois(y, mu, log = TRUE) : non-integer x = 11.019703 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.980297 Warning in dpois(y, mu, log = TRUE) : non-integer x = 10.748857 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.251143 Warning in dpois(y, mu, log = TRUE) : non-integer x = 10.748857 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.251143 Warning in dpois(y, mu, log = TRUE) : non-integer x = 10.478011 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.521989 Warning in dpois(y, mu, log = TRUE) : non-integer x = 10.478011 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.521989 Warning in dpois(y, mu, log = TRUE) : non-integer x = 10.207165 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.792835 Warning in dpois(y, mu, log = TRUE) : non-integer x = 10.207165 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.792835 Warning in dpois(y, mu, log = TRUE) : non-integer x = 9.936320 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.063680 Warning in dpois(y, mu, log = TRUE) : non-integer x = 9.936320 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.063680 Warning in dpois(y, mu, log = TRUE) : non-integer x = 9.665474 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.334526 Warning in dpois(y, mu, log = TRUE) : non-integer x = 9.665474 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.334526 Warning in dpois(y, mu, log = TRUE) : non-integer x = 8.785225 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.214775 Warning in dpois(y, mu, log = TRUE) : non-integer x = 8.785225 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.214775 Warning in dpois(y, mu, log = TRUE) : non-integer x = 8.175822 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.824178 Warning in dpois(y, mu, log = TRUE) : non-integer x = 8.175822 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.824178 Warning in dpois(y, mu, log = TRUE) : non-integer x = 7.566419 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.433581 Warning in dpois(y, mu, log = TRUE) : non-integer x = 7.566419 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.433581 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.957016 Warning in dpois(y, mu, log = TRUE) : non-integer x = 5.042984 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.957016 Warning in dpois(y, mu, log = TRUE) : non-integer x = 5.042984 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.347613 Warning in dpois(y, mu, log = TRUE) : non-integer x = 5.652387 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.347613 Warning in dpois(y, mu, log = TRUE) : non-integer x = 5.652387 Warning in dpois(y, mu, log = TRUE) : non-integer x = 5.738210 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.261790 Warning in dpois(y, mu, log = TRUE) : non-integer x = 5.738210 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.261790 Warning in dpois(y, mu, log = TRUE) : non-integer x = 5.128807 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.871193 Warning in dpois(y, mu, log = TRUE) : non-integer x = 5.128807 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.871193 Saddlepoint Distribution Approximations Call : saddle.distn(A = cbind(aircondit$hours/12, 1), u = 12, wdist = "p", type = "cond", t0 = air.t0) Quantiles of the Distribution 0.1% 27.4 0.5% 35.4 1.0% 39.7 2.5% 46.7 5.0% 53.5 10.0% 62.5 20.0% 75.3 50.0% 104.5 80.0% 139.0 90.0% 158.8 95.0% 175.9 97.5% 191.2 99.0% 209.6 99.5% 222.4 99.9% 249.5 Smoothing spline used 20 points in the range 9.8 to 304.7. > > # Distribution of the ratio of a sample of size 10 from the bigcity > # data, taken from Example 9.16 of Davison and Hinkley (1997). > ratio <- function(d, w) sum(d$x *w)/sum(d$u * w) > city.v <- var.linear(empinf(data = city, statistic = ratio)) > bigcity.t0 <- c(mean(bigcity$x)/mean(bigcity$u), sqrt(city.v)) > Afn <- function(t, data) cbind(data$x - t*data$u, 1) > ufn <- function(t, data) c(0,10) > saddle.distn(A = Afn, u = ufn, wdist = "b", type = "cond", + t0 = bigcity.t0, data = bigcity) Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Warning in eval(expr, envir, enclos) : non-integer counts in a binomial glm! Saddlepoint Distribution Approximations Call : saddle.distn(A = Afn, u = ufn, wdist = "b", type = "cond", t0 = bigcity.t0, data = bigcity) Quantiles of the Distribution 0.1% 1.070 0.5% 1.092 1.0% 1.104 2.5% 1.122 5.0% 1.139 10.0% 1.158 20.0% 1.184 50.0% 1.237 80.0% 1.304 90.0% 1.348 95.0% 1.392 97.5% 1.436 99.0% 1.494 99.5% 1.537 99.9% 1.636 Smoothing spline used 20 points in the range 1.014 to 1.96. > > # From Example 9.16 of Davison and Hinkley (1997) again, we find the > # conditional distribution of the ratio given the sum of city$u. > Afn <- function(t, data) cbind(data$x-t*data$u, data$u, 1) > ufn <- function(t, data) c(0, sum(data$u), 10) > city.t0 <- c(mean(city$x)/mean(city$u), sqrt(city.v)) > saddle.distn(A = Afn, u = ufn, wdist = "p", type = "cond", t0 = city.t0, + data = city) Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.866400 Warning in dpois(y, mu, log = TRUE) : non-integer x = 8.511350 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.622251 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.866400 Warning in dpois(y, mu, log = TRUE) : non-integer x = 8.511350 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.622251 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.210844 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.107208 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.681949 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.210844 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.107208 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.681949 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.038622 Warning in dpois(y, mu, log = TRUE) : non-integer x = 5.809279 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.152100 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.038622 Warning in dpois(y, mu, log = TRUE) : non-integer x = 5.809279 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.152100 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.452511 Warning in dpois(y, mu, log = TRUE) : non-integer x = 7.160314 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.387175 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.452511 Warning in dpois(y, mu, log = TRUE) : non-integer x = 7.160314 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.387175 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.159455 Warning in dpois(y, mu, log = TRUE) : non-integer x = 7.835832 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.004713 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.159455 Warning in dpois(y, mu, log = TRUE) : non-integer x = 7.835832 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.004713 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.155629 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.115412 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.728960 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.155629 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.115412 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.728960 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.205022 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.133273 Warning in dpois(y, mu, log = TRUE) : non-integer x = 7.661705 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.205022 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.133273 Warning in dpois(y, mu, log = TRUE) : non-integer x = 7.661705 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.722845 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.033105 Warning in dpois(y, mu, log = TRUE) : non-integer x = 7.244049 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.722845 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.033105 Warning in dpois(y, mu, log = TRUE) : non-integer x = 7.244049 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.787431 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.388294 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.824275 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.787431 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.388294 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.824275 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.415407 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.940756 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.643837 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.415407 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.940756 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.643837 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.043383 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.493218 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.463399 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.043383 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.493218 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.463399 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.671359 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.045680 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.282961 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.671359 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.045680 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.282961 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.299336 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.598142 Warning in dpois(y, mu, log = TRUE) : non-integer x = 5.102523 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.299336 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.598142 Warning in dpois(y, mu, log = TRUE) : non-integer x = 5.102523 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.433935 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.409277 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.156788 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.433935 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.409277 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.156788 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.360158 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.271008 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.368834 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.360158 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.271008 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.368834 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.319252 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.639889 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.040859 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.319252 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.639889 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.040859 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.278346 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.008770 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.712884 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.278346 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.008770 Warning in dpois(y, mu, log = TRUE) : non-integer x = 4.712884 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.237440 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.377650 Warning in dpois(y, mu, log = TRUE) : non-integer x = 5.384909 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.237440 Warning in dpois(y, mu, log = TRUE) : non-integer x = 1.377650 Warning in dpois(y, mu, log = TRUE) : non-integer x = 5.384909 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.196534 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.746531 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.056935 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.196534 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.746531 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.056935 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.155629 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.115412 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.728960 Warning in dpois(y, mu, log = TRUE) : non-integer x = 3.155629 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.115412 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.728960 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.734728 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.299661 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.965612 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.734728 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.299661 Warning in dpois(y, mu, log = TRUE) : non-integer x = 6.965612 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.228786 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.666383 Warning in dpois(y, mu, log = TRUE) : non-integer x = 7.104831 Warning in dpois(y, mu, log = TRUE) : non-integer x = 2.228786 Warning in dpois(y, mu, log = TRUE) : non-integer x = 0.666383 Warning in dpois(y, mu, log = TRUE) : non-integer x = 7.104831 Saddlepoint Distribution Approximations Call : saddle.distn(A = Afn, u = ufn, wdist = "p", type = "cond", t0 = city.t0, data = city) Quantiles of the Distribution 0.1% 1.216 0.5% 1.236 1.0% 1.248 2.5% 1.272 5.0% 1.301 10.0% 1.340 20.0% 1.393 50.0% 1.502 80.0% 1.618 90.0% 1.680 95.0% 1.732 97.5% 1.777 99.0% 1.830 99.5% 1.866 99.9% 1.938 Smoothing spline used 20 points in the range 1.182 to 2.061. > > > > cleanEx() > nameEx("simplex") > ### * simplex > > flush(stderr()); flush(stdout()) > > ### Name: simplex > ### Title: Simplex Method for Linear Programming Problems > ### Aliases: simplex > ### Keywords: optimize > > ### ** Examples > > # This example is taken from Exercise 7.5 of Gill, Murray and Wright (1991). > enj <- c(200, 6000, 3000, -200) > fat <- c(800, 6000, 1000, 400) > vitx <- c(50, 3, 150, 100) > vity <- c(10, 10, 75, 100) > vitz <- c(150, 35, 75, 5) > simplex(a = enj, A1 = fat, b1 = 13800, A2 = rbind(vitx, vity, vitz), + b2 = c(600, 300, 550), maxi = TRUE) Linear Programming Results Call : simplex(a = enj, A1 = fat, b1 = 13800, A2 = rbind(vitx, vity, vitz), b2 = c(600, 300, 550), maxi = TRUE) Maximization Problem with Objective Function Coefficients x1 x2 x3 x4 200 6000 3000 -200 Optimal solution has the following values x1 x2 x3 x4 0.0 0.0 13.8 0.0 The optimal value of the objective function is 41400. > > > > cleanEx() > nameEx("smooth.f") > ### * smooth.f > > flush(stderr()); flush(stdout()) > > ### Name: smooth.f > ### Title: Smooth Distributions on Data Points > ### Aliases: smooth.f > ### Keywords: smooth nonparametric > > ### ** Examples > > # Example 9.8 of Davison and Hinkley (1997) requires tilting the resampling > # distribution of the studentized statistic to be centred at the observed > # value of the test statistic 1.84. In the book exponential tilting was used > # but it is also possible to use smooth.f. > grav1 <- gravity[as.numeric(gravity[, 2]) >= 7, ] > grav.fun <- function(dat, w, orig) { + strata <- tapply(dat[, 2], as.numeric(dat[, 2])) + d <- dat[, 1] + ns <- tabulate(strata) + w <- w/tapply(w, strata, sum)[strata] + mns <- as.vector(tapply(d * w, strata, sum)) # drop names + mn2 <- tapply(d * d * w, strata, sum) + s2hat <- sum((mn2 - mns^2)/ns) + c(mns[2] - mns[1], s2hat, (mns[2]-mns[1]-orig)/sqrt(s2hat)) + } > grav.z0 <- grav.fun(grav1, rep(1, 26), 0) > grav.boot <- boot(grav1, grav.fun, R = 499, stype = "w", + strata = grav1[, 2], orig = grav.z0[1]) > grav.sm <- smooth.f(grav.z0[3], grav.boot, index = 3) > > # Now we can run another bootstrap using these weights > grav.boot2 <- boot(grav1, grav.fun, R = 499, stype = "w", + strata = grav1[, 2], orig = grav.z0[1], + weights = grav.sm) > > # Estimated p-values can be found from these as follows > mean(grav.boot$t[, 3] >= grav.z0[3]) [1] 0.01402806 > imp.prob(grav.boot2, t0 = -grav.z0[3], t = -grav.boot2$t[, 3]) $t0 [1] -1.840118 $raw [1] 0.02163715 $rat [1] 0.02099078 $reg [1] 0.02174393 > > > # Note that for the importance sampling probability we must > # multiply everything by -1 to ensure that we find the correct > # probability. Raw resampling is not reliable for probabilities > # greater than 0.5. Thus > 1 - imp.prob(grav.boot2, index = 3, t0 = grav.z0[3])$raw [1] -0.009155757 > # can give very strange results (negative probabilities). > > > > cleanEx() > nameEx("tilt.boot") > ### * tilt.boot > > flush(stderr()); flush(stdout()) > > ### Name: tilt.boot > ### Title: Non-parametric Tilted Bootstrap > ### Aliases: tilt.boot > ### Keywords: nonparametric > > ### ** Examples > > # Note that these examples can take a while to run. > > # Example 9.9 of Davison and Hinkley (1997). > grav1 <- gravity[as.numeric(gravity[,2]) >= 7, ] > grav.fun <- function(dat, w, orig) { + strata <- tapply(dat[, 2], as.numeric(dat[, 2])) + d <- dat[, 1] + ns <- tabulate(strata) + w <- w/tapply(w, strata, sum)[strata] + mns <- as.vector(tapply(d * w, strata, sum)) # drop names + mn2 <- tapply(d * d * w, strata, sum) + s2hat <- sum((mn2 - mns^2)/ns) + c(mns[2]-mns[1],s2hat,(mns[2]-mns[1]-orig)/sqrt(s2hat)) + } > grav.z0 <- grav.fun(grav1, rep(1, 26), 0) > tilt.boot(grav1, grav.fun, R = c(249, 375, 375), stype = "w", + strata = grav1[,2], tilt = TRUE, index = 3, orig = grav.z0[1]) TILTED BOOTSTRAP Exponential tilting used First 249 replicates untilted, Next 375 replicates tilted to -2.821, Next 375 replicates tilted to 1.636. Call: tilt.boot(data = grav1, statistic = grav.fun, R = c(249, 375, 375), stype = "w", strata = grav1[, 2], tilt = TRUE, index = 3, orig = grav.z0[1]) Bootstrap Statistics : original bias std. error t1* 2.846154 -0.4487564 2.500644 t2* 2.392353 -0.3221155 1.187574 t3* 0.000000 -0.8862944 2.208945 > > > # Example 9.10 of Davison and Hinkley (1997) requires a balanced > # importance resampling bootstrap to be run. In this example we > # show how this might be run. > acme.fun <- function(data, i, bhat) { + d <- data[i,] + n <- nrow(d) + d.lm <- glm(d$acme~d$market) + beta.b <- coef(d.lm)[2] + d.diag <- boot::glm.diag(d.lm) + SSx <- (n-1)*var(d$market) + tmp <- (d$market-mean(d$market))*d.diag$res*d.diag$sd + sr <- sqrt(sum(tmp^2))/SSx + c(beta.b, sr, (beta.b-bhat)/sr) + } > acme.b <- acme.fun(acme, 1:nrow(acme), 0) > acme.boot1 <- tilt.boot(acme, acme.fun, R = c(499, 250, 250), + stype = "i", sim = "balanced", alpha = c(0.05, 0.95), + tilt = TRUE, index = 3, bhat = acme.b[1]) > > > > cleanEx() > nameEx("tsboot") > ### * tsboot > > flush(stderr()); flush(stdout()) > > ### Name: tsboot > ### Title: Bootstrapping of Time Series > ### Aliases: tsboot ts.return > ### Keywords: nonparametric ts > > ### ** Examples > > lynx.fun <- function(tsb) { + ar.fit <- ar(tsb, order.max = 25) + c(ar.fit$order, mean(tsb), tsb) + } > > # the stationary bootstrap with mean block length 20 > lynx.1 <- tsboot(log(lynx), lynx.fun, R = 99, l = 20, sim = "geom") > > # the fixed block bootstrap with length 20 > lynx.2 <- tsboot(log(lynx), lynx.fun, R = 99, l = 20, sim = "fixed") > > # Now for model based resampling we need the original model > # Note that for all of the bootstraps which use the residuals as their > # data, we set orig.t to FALSE since the function applied to the residual > # time series will be meaningless. > lynx.ar <- ar(log(lynx)) > lynx.model <- list(order = c(lynx.ar$order, 0, 0), ar = lynx.ar$ar) > lynx.res <- lynx.ar$resid[!is.na(lynx.ar$resid)] > lynx.res <- lynx.res - mean(lynx.res) > > lynx.sim <- function(res,n.sim, ran.args) { + # random generation of replicate series using arima.sim + rg1 <- function(n, res) sample(res, n, replace = TRUE) + ts.orig <- ran.args$ts + ts.mod <- ran.args$model + mean(ts.orig)+ts(arima.sim(model = ts.mod, n = n.sim, + rand.gen = rg1, res = as.vector(res))) + } > > lynx.3 <- tsboot(lynx.res, lynx.fun, R = 99, sim = "model", n.sim = 114, + orig.t = FALSE, ran.gen = lynx.sim, + ran.args = list(ts = log(lynx), model = lynx.model)) > > # For "post-blackening" we need to define another function > lynx.black <- function(res, n.sim, ran.args) { + ts.orig <- ran.args$ts + ts.mod <- ran.args$model + mean(ts.orig) + ts(arima.sim(model = ts.mod,n = n.sim,innov = res)) + } > > # Now we can run apply the two types of block resampling again but this > # time applying post-blackening. > lynx.1b <- tsboot(lynx.res, lynx.fun, R = 99, l = 20, sim = "fixed", + n.sim = 114, orig.t = FALSE, ran.gen = lynx.black, + ran.args = list(ts = log(lynx), model = lynx.model)) > > lynx.2b <- tsboot(lynx.res, lynx.fun, R = 99, l = 20, sim = "geom", + n.sim = 114, orig.t = FALSE, ran.gen = lynx.black, + ran.args = list(ts = log(lynx), model = lynx.model)) > > # To compare the observed order of the bootstrap replicates we > # proceed as follows. > table(lynx.1$t[, 1]) 2 3 4 5 7 8 10 11 12 13 14 16 19 38 4 6 3 1 9 1 1 1 > table(lynx.1b$t[, 1]) 2 3 4 5 6 7 8 11 12 14 15 6 2 22 6 4 6 3 40 7 1 2 > table(lynx.2$t[, 1]) 2 3 4 5 6 7 8 10 11 13 12 18 51 5 2 3 1 2 4 1 > table(lynx.2b$t[, 1]) 2 3 4 5 6 7 8 9 10 11 12 13 15 21 2 1 21 4 1 10 4 1 3 45 3 1 2 1 > table(lynx.3$t[, 1]) 2 3 4 5 6 7 8 9 10 11 12 13 14 15 4 8 11 2 1 4 2 2 2 54 6 1 1 1 > # Notice that the post-blackened and model-based bootstraps preserve > # the true order of the model (11) in many more cases than the others. > > > > cleanEx() > nameEx("var.linear") > ### * var.linear > > flush(stderr()); flush(stdout()) > > ### Name: var.linear > ### Title: Linear Variance Estimate > ### Aliases: var.linear > ### Keywords: nonparametric > > ### ** Examples > > # To estimate the variance of the ratio of means for the city data. > ratio <- function(d,w) sum(d$x * w)/sum(d$u * w) > var.linear(empinf(data = city, statistic = ratio)) [1] 0.03248701 > > > > ### *