version 1.57

cran · Jul 2, 2024 · 2c0fd18 · 2c0fd18
1 parent ba29ab0
commit 2c0fd18
Show file tree

Hide file tree

Showing 59 changed files with 1,888 additions and 1,076 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,16 +1,24 @@
 Package: pamr
 Title: Pam: Prediction Analysis for Microarrays
-Version: 1.56.2.1
-Author: T. Hastie, R. Tibshirani, Balasubramanian Narasimhan, Gil Chu
+Version: 1.57
+Authors@R: c(
+	     person("Trevor", "Hastie", role=c("aut"), email = "hastie@stanford.edu"),
+	     person("Rob", "Tibshirani", role=c("aut"), email = "tibs@stanford.edu"),
+	     person("Balasubramanian", "Narasimhan", role=c("aut", "cre"), email = "naras@stanford.edu"),
+	     person("Gilbert","Chu", role=c("aut")))
 Description: Some functions for sample classification in microarrays.
-Maintainer: ORPHANED
-Depends: R (>= 2.10), cluster, survival
+Depends: R (>= 3.5)
 License: GPL-2
-Packaged: 2024-06-25 08:11:07 UTC; ripley
+Date: 2024-06-28
+RoxygenNote: 7.3.1
+Encoding: UTF-8
+Imports: cluster, survival
+NeedsCompilation: no
+Packaged: 2024-06-30 23:41:08 UTC; naras
+Author: Trevor Hastie [aut],
+  Rob Tibshirani [aut],
+  Balasubramanian Narasimhan [aut, cre],
+  Gilbert Chu [aut]
+Maintainer: Balasubramanian Narasimhan <naras@stanford.edu>
 Repository: CRAN
-NeedsCompilation: yes
-Date/Publication: 2024-06-27 11:13:24 UTC
-X-CRAN-Original-Maintainer: Rob Tibshirani <tibs@stanford.edu>
-X-CRAN-Comment: Orphaned and corrected on 2024-06-27
- .
- Deleted Fortran constructs not corrected by maintainer.
+Date/Publication: 2024-07-01 06:40:02 UTC
diff --git a/MD5 b/MD5
@@ -1,68 +1,69 @@
-d3eeea0771c618d674aa9c8b8fcf76c3 *DESCRIPTION
-a013f5a35633b0ac531127f2199d0148 *NAMESPACE
+54b70d1546dc1e7474fb655999e4cd80 *DESCRIPTION
+b7464cbd8ed652f8e4f8493b4272c88e *NAMESPACE
 3e9a5a7e57f9186f93a6e6e3b6e3f231 *R/balanced.folds.R
 a9fa703b4c6ffaccb04deb2eba336b0b *R/descendants.R
 149e3ba609aa2df762c95357513d0d7d *R/diag.disc.R
 1331130ea581680bb6ed25effabc6680 *R/enlist.R
 297b45527274666ae02bd43de701a357 *R/error.nsc.R
+a608274634e8adc95b99c97d873fdbfc *R/khan.R
 30e6bd3b3bd2ca2e7d3ee42f7413715d *R/nsc.R
 1ebd0eaf47e6ff25968dc25c251c525d *R/nsccv.R
-b4e92f62767de1a8d76016b6e6bda8e6 *R/pamr.adaptthresh.R
-5badf534c3c1bb052420a438094b7021 *R/pamr.confusion.R
+ff1daa13bc6226b6968cd75f2f00a619 *R/pamr-package.R
+d281bcf2955223e1ffac18cb1cb1fbdf *R/pamr.adaptthresh.R
+56fde15aa1048f7cc4fcb624710bb3eb *R/pamr.confusion.R
 d5e65f72eba8d21cdcc0822a03783db6 *R/pamr.cube.root.R
-7eb0a7ed5298e7c130bf0de7fffe5693 *R/pamr.cv.R
-be011eb4978494c79e3294b65eb511df *R/pamr.decorrelate.R
-f418dda5b8684a29b5c1130a30a10411 *R/pamr.fdr.R
-218250beddb0346957d7dbd5f7d22663 *R/pamr.geneplot.R
-7cf30def6c45e7b33da029fcddba7e3a *R/pamr.indeterminate.R
-947d0bd705caaac28b6fe68123a3bb28 *R/pamr.listgenes.R
-7f77199047b031973b43c11069c41c3c *R/pamr.makeclasses.R
-0e01cbdc69f7b1911acc9609b5b14280 *R/pamr.menu.R
+bd8755a65a6fbc221257fa41e3cc61cc *R/pamr.cv.R
+407532327db1757f572290198b725f39 *R/pamr.decorrelate.R
+d9b6f292da2cef112d7309ea82735b49 *R/pamr.fdr.R
+2848a9550f89a61dc80953e84cedebce *R/pamr.geneplot.R
+2ebebf7c7f792265bd215bdc49d7bd9f *R/pamr.indeterminate.R
+8c3476aff169274982100e8a7b07ea37 *R/pamr.listgenes.R
+d55e288700bdb43fc425ac5d00783cc7 *R/pamr.makeclasses.R
+4af465c9be2ccf37f27dfa03481c135e *R/pamr.menu.R
 96a47d5eae6dad1c2167d7db9d49428c *R/pamr.pairscore.R
-c069c279f8745599a102fe6b5fdf28a9 *R/pamr.plotcen.R
-a45b2868fc505b3bff184a2f8a219861 *R/pamr.plotcv.R
-fe9866581c3177a2d9a7ba52559bc70b *R/pamr.plotcvprob.R
-9a549ca6e18c35feba216bbf43a71cab *R/pamr.plotfdr.R
-0ab0b9e4d06a1ce3fe7b70b6a2c150ba *R/pamr.plotstrata.R
-f1ce85500d3cf3a421d2d5a28e0f12b9 *R/pamr.predict.R
-89a6a0290506f376d86553ab624f322b *R/pamr.predictmany.R
-547018d9891c13beccf26e849273464d *R/pamr.survfuns.R
+eb712f427e19fe0c467eb5c2513ae132 *R/pamr.plotcen.R
+5948681c344f228b4a18f7ec331080be *R/pamr.plotcv.R
+16020531ac3e7b74d85489dc3ce19b9d *R/pamr.plotcvprob.R
+606f6e62ed1f2d8fc575cd00b58ea859 *R/pamr.plotfdr.R
+f40d19f80d884fcbc7dad24871893acf *R/pamr.plotstrata.R
+b642b0eb6fc424c17ce360c7a966a672 *R/pamr.predict.R
+c1a439de98c05397a9502a536d279ff8 *R/pamr.predictmany.R
+2be894d2d664dd16dd546060bdbe5fe8 *R/pamr.survfuns.R
 9bc80d974889da5cc972c0bf9b640a99 *R/pamr.to.excel.R
-a03b7b7a5798e384fb14c64cb6390b23 *R/pamr.train.R
+d5a6369445441ef2227eadebf2a0aa0a *R/pamr.train.R
 b1716c64ae0996cf8580a191fbaa5d8c *R/permute.rows.R
-61c7b7912cbf1a427638d0449badb6ed *R/print.nsc.R
-fb42d5cf3756816e3f17fdbfdda70ec7 *R/print.nsccv.R
-0ce75c063d9470dc420d88de60c37ba3 *R/print.pamrcved.R
-5552b33a42fe849a479b5f1934747587 *R/print.pamrtrained.R
+ae76cc9e561bea09b81a7aa5ef38332d *R/print.nsc.R
+1168237c83e8415f5cd1f041ab444df3 *R/print.nsccv.R
+b3670284e92441164c8bbf4c9fc17452 *R/print.pamrcved.R
+42b4778b0df66eb716bfc59e7a890cd6 *R/print.pamrtrained.R
 ddae48d3df6c057148c51d2e8ffecd51 *R/roc.R
 9c93192a4d22d57200ad8aaf2ffdabb1 *R/softmax.R
 1dbf2b705691fbdd2fb0b5b26986c941 *R/softshrink.R
-a1f4d9a9fd31b8b664b6f75dccaaa0ee *R/which.is.max.R
-e8096e76722eca5b2df0acfe386c0db3 *data/datalist
-d64f28c69388d3d18445fd02cf0fe9b8 *data/khan.rda
-8d0a0be984ab3d9ef53067a53be8791f *man/khan.Rd
-2f8f6d4104e43e4b477c0d768b875000 *man/pamr.adaptthresh.Rd
-182f266a7d60ed6ef80fd6520c60478e *man/pamr.batchadjust.Rd
-2495998feb8aa73579d9e6c4f4f85747 *man/pamr.confusion.Rd
-d3a2f25e50020835fd41042f6a08bcc8 *man/pamr.confusion.survival.Rd
-4db2df1c52220dd6f727ae5e5ce6652f *man/pamr.cv.Rd
-a32286faa78aaeda7d39cc94f2c1f514 *man/pamr.decorrelate.Rd
-290eb352615c7867bf6a6e75fc5411b2 *man/pamr.fdr.Rd
-73c6ed8df9b0091cbe918dcd36c3a22b *man/pamr.geneplot.Rd
-adcbe5d48f776a4d6eea201db47a092b *man/pamr.indeterminate.Rd
-14b1bb322665cb376daad7778155b822 *man/pamr.internal.Rd
-0e8d75a6965aa1c306295fe5e49ffc07 *man/pamr.listgenes.Rd
-93117743e724d1672958e95d278931e8 *man/pamr.makeclasses.Rd
-61a9997c8b19b7f732b609f398bd3833 *man/pamr.menu.Rd
-d35ab5006075ce3698adba3bd193d09d *man/pamr.plotcen.Rd
-e0d98b03bde4fbf7d4a3ada55152655b *man/pamr.plotcv.Rd
-a9743d4c693612b997d2a91ef032cc4e *man/pamr.plotcvprob.Rd
-de992ac0e3c303ab442f884dd6f01434 *man/pamr.plotfdr.Rd
-53af94398af4d65ccdc8d71c1d060ca7 *man/pamr.plotstrata.Rd
-df0609e914feec892925fd4900cd0727 *man/pamr.plotsurvival.Rd
-de6bffcebbc9258e2ad73314149322ca *man/pamr.predict.Rd
-7a633ff47d6177b5912126cb3b579eef *man/pamr.predictmany.Rd
-229fd68a5bf9a2bd13104c8cc4f4def2 *man/pamr.surv.to.class2.Rd
-d83c019e2e26f6ef89686ecf41ad19db *man/pamr.test.errors.surv.compute.Rd
-c01e178393464f8dbce6127a1a4aef37 *man/pamr.train.Rd
-2912caf7cbd1ce59d1deb8bac53c69aa *src/knnimpute.f
+fd11965b198231953a1a12f7fde82c3a *R/which.is.max.R
+47397e8338269919f0ec71e773b290c0 *README.md
+f1907c9b0b94299ac988d9b1d3114006 *data/khan.rda
+001f53fa65db721d0026ae3d4239b500 *man/khan.Rd
+022f20786decee0c532db35e6af2416e *man/pamr-package.Rd
+a0ee61365e9fa22b000b1dc73374abf9 *man/pamr.adaptthresh.Rd
+6d3315c8f2a6e50b63c99c922c4d8309 *man/pamr.batchadjust.Rd
+20a555265122c5c7dfbc7355d38d38e7 *man/pamr.confusion.Rd
+ff817fc81df1e986f7eba24b8a359e48 *man/pamr.confusion.survival.Rd
+921aa6db6abd1ce8eab8268d297318df *man/pamr.cv.Rd
+360c8fef4ff0345958e344496e274a77 *man/pamr.decorrelate.Rd
+fa894fa96b30610b5aa5cb2f9c64a21f *man/pamr.fdr.Rd
+8dd0eb85624b6cf3998f86bae6abe61f *man/pamr.geneplot.Rd
+75289417aa97f797b03ebe399e1820a8 *man/pamr.indeterminate.Rd
+a3cfb0e548ca08aa59cc9df7bfefd6ba *man/pamr.listgenes.Rd
+e3e3c8dab08e8c3c2722e27f6f0ecadb *man/pamr.makeclasses.Rd
+56bc025c2430eac0cf97be3ceb34f3a4 *man/pamr.menu.Rd
+7ad56b3dcc2bdfb39cd3281df265fb3b *man/pamr.plotcen.Rd
+f64890bb0edbac94b740bc023ad3833e *man/pamr.plotcv.Rd
+1aad62db19ee8c7b1bb255822dd9f8be *man/pamr.plotcvprob.Rd
+73b741568139920d920fa079e1564d5f *man/pamr.plotfdr.Rd
+3630fa2c449414e3c905d55b783b8151 *man/pamr.plotstrata.Rd
+95dec0072a00b09cf1026a4ed0770c71 *man/pamr.plotsurvival.Rd
+f32356238efa5997680a63528aa4d342 *man/pamr.predict.Rd
+6b7cf9a849dbf666c2c47d5893237a69 *man/pamr.predictmany.Rd
+73901ccdbb542f611b7e5a4ac403c667 *man/pamr.surv.to.class2.Rd
+e013067590dbc84e86488b3229862ff3 *man/pamr.test.errors.surv.compute.Rd
+cc8c90fab47bcabb3f32d4527c8331ed *man/pamr.train.Rd
diff --git a/NAMESPACE b/NAMESPACE
@@ -1,47 +1,56 @@
-useDynLib(pamr)
-S3method(print,pamrtrained)
-S3method(print,pamrcved)
-
-import(survival, cluster)
-export(pamr.adaptthresh, 
-        pamr.batchadjust, 
-        pamr.confusion, 
-        pamr.cv, 
-        pamr.fdr,
-        pamr.decorrelate,
-        pamr.geneplot, 
-        pamr.listgenes,
-        pamr.makeclasses, 
-        pamr.menu, 
-        pamr.cube.root, 
-        pamr.indeterminate,
-        pamr.pairscore,
-        pamr.plotcen, 
-        pamr.plotcvprob,
-        pamr.plotcv, 
-        pamr.plotfdr, 
-        pamr.predictmany, 
-        pamr.predict, 
-        pamr.score.to.class1,
-        pamr.score.to.class2, 
-        pamr.surv.to.class2, 
-        pamr.confusion.survival,
-        pamr.plotsurvival, 
-        pamr.plotstrata,
-        pamr.pvalue.survival, 
-        pamr.train,
-        print.pamrtrained,
-        print.pamrcved,
-        pamr.test.errors.surv.compute,
-        print.pamrcved,
-        print.pamrtrained)
-
-importFrom("grDevices", "dev.off", "savePlot")
-     importFrom("graphics", "abline", "axis", "box", "legend", "lines",
-     "locator", "mtext", "par", "plot", "points", "segments",
-     "text", "title")
-     importFrom("stats", "approx", "dist", "hclust", "kmeans", "lm",
-     "median", "model.matrix", "pchisq", "predict", "quantile",
-     "runif", "update", "var")
- importFrom("utils", "menu")
+# Generated by roxygen2: do not edit by hand
 
+export(pamr.adaptthresh)
+export(pamr.batchadjust)
+export(pamr.confusion)
+export(pamr.confusion.survival)
+export(pamr.cv)
+export(pamr.decorrelate)
+export(pamr.fdr)
+export(pamr.geneplot)
+export(pamr.indeterminate)
+export(pamr.listgenes)
+export(pamr.makeclasses)
+export(pamr.menu)
+export(pamr.plotcen)
+export(pamr.plotcv)
+export(pamr.plotcvprob)
+export(pamr.plotfdr)
+export(pamr.plotstrata)
+export(pamr.plotsurvival)
+export(pamr.predict)
+export(pamr.predictmany)
+export(pamr.surv.to.class2)
+export(pamr.test.errors.surv.compute)
+export(pamr.train)
+import(cluster)
+import(survival)
+importFrom(grDevices,dev.off)
+importFrom(grDevices,savePlot)
+importFrom(graphics,abline)
+importFrom(graphics,axis)
+importFrom(graphics,box)
+importFrom(graphics,legend)
+importFrom(graphics,lines)
+importFrom(graphics,locator)
+importFrom(graphics,mtext)
+importFrom(graphics,par)
+importFrom(graphics,plot)
+importFrom(graphics,points)
+importFrom(graphics,segments)
+importFrom(graphics,text)
+importFrom(graphics,title)
+importFrom(stats,approx)
+importFrom(stats,dist)
+importFrom(stats,hclust)
+importFrom(stats,kmeans)
+importFrom(stats,lm)
+importFrom(stats,median)
+importFrom(stats,model.matrix)
+importFrom(stats,pchisq)
+importFrom(stats,predict)
+importFrom(stats,quantile)
+importFrom(stats,runif)
+importFrom(stats,update)
+importFrom(stats,var)
+importFrom(utils,menu)
diff --git a/R/khan.R b/R/khan.R
@@ -0,0 +1,13 @@
+#' Khan microarray data
+#' 
+#' The \code{khan} data frame has 2308 rows and 65 columns. These are one of
+#' the datasets data used in the Tibshirani et al paper in PNAS on nearest
+#' shrunken centroids.
+#'
+#' The first two columns of gene ids and names and the remaining
+#' columns are gene expression values for 63 samples. An attribute
+#' `cancer_type` contains the cancer type for each sample.
+#'
+#' @name khan
+#' @keywords datasets
+NULL
diff --git a/R/pamr-package.R b/R/pamr-package.R
@@ -0,0 +1,19 @@
+#' Prediction Analysis of Microarrays
+#'
+#' @description Functions for training and predicting using shrunken
+#'   centroids. While originally developed in the context of
+#'   microarray data analysis (<doi:10.1073/pnas.082099299>), the
+#'   method is broadly applicable.
+#' @aliases pamr.cube.root pamr.pairscore pamr.pvalue.survival
+#'   pamr.score.to.class1 pamr.score.to.class2 print.nsc print.nsccv
+#'   print.pamrcved print.pamrtrained
+#' @import survival cluster
+#' @importFrom grDevices dev.off savePlot
+#' @importFrom graphics abline axis box legend lines locator mtext par plot points segments text title
+#' @importFrom stats approx dist hclust kmeans lm median model.matrix pchisq predict quantile runif update var
+#' @importFrom utils  menu
+#' @keywords internal
+"_PACKAGE"
+
+
+
diff --git a/R/pamr.adaptthresh.R b/R/pamr.adaptthresh.R
@@ -1,3 +1,48 @@
+#' A function to adaptive choose threshold scales, for use in pamr.train
+#' 
+#' A function to adaptive choose threshold scales, for use in pamr.train
+#' 
+#' \code{pamr.adaptthresh} Adaptively searches for set of good threshold
+#' scales.  The baseline (default) scale is 1 for each class. The idea is that
+#' for easy to classify classes, the threshold scale can be increased without
+#' increasing the error rate for that class, and resulting in fewer genes
+#' needed for the classification rule. The scalings from pamr.adaptthresh are
+#' then used in pamr.train, and pamr.cv. The results may be better than those
+#' obtained with the default values of threshold.scale.
+#' 
+#' @param object The result of a call to pamr.train
+#' @param ntries Number of iterations to use in algorithm
+#' @param reduction.factor Amount by which a scaling is reduced in one step of
+#' the algorithm
+#' @param full.out Should full output be returned? Default FALSE
+#' @author Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, and
+#' Gilbert Chu
+#' @references
+#' 
+#' Robert Tibshirani, Trevor Hastie, Balasubramanian Narasimhan, and Gilbert
+#' Chu. "Diagnosis of multiple cancer types by shrunken centroids of gene
+#' expression" PNAS 2002 99:6567-6572 (May 14).
+#' 
+#' Robert Tibshirani, Trevor Hastie, Balasubramanian Narasimhan, and Gilbert
+#' Chu (2002).  Class prediction by nearest shrunken centroids,with
+#' applications to DNA microarrays. Stanford tech report.
+#' @examples
+#' 
+#' suppressWarnings(RNGversion("3.5.0"))
+#' set.seed(120)
+#' x <- matrix(rnorm(1000*20),ncol=20)
+#' y <- sample(c(1:4),size=20,replace=TRUE)
+#' mydata <- list(x=x,y=y)
+#' mytrain <-   pamr.train(mydata)
+#' new.scales <- pamr.adaptthresh(mytrain)
+#' 
+#'  
+#' mytrain2 <- pamr.train(mydata, threshold.scale=new.scales)
+#' 
+#' myresults2 <- pamr.cv(mytrain2, mydata)
+#' 
+#' 
+#' @export pamr.adaptthresh
 pamr.adaptthresh <- function(object, ntries = 10, reduction.factor = 0.9, full.out = FALSE) {
   errors <- error.nsc(object)
   threshold <- object$threshold   
@@ -44,6 +89,38 @@ pamr.adaptthresh <- function(object, ntries = 10, reduction.factor = 0.9, full.o
     opt.scale
 }
 
+
+
+#' A function to mean-adjust microarray data by batches
+#' 
+#' A function to mean-adjust microarray data by batches
+#' 
+#' \code{pamr.batchadjust} does a genewise one-way ANOVA adjustment for
+#' expression values.  Let \eqn{x(i,j)} be the expression for gene \eqn{i} in sample \eqn{j}.
+#' Suppose sample \eqn{j} in in batch \eqn{b}, and let \eqn{B} be the set of all samples in batch
+#' \eqn{b}. Then \code{pamr.batchadjust} adjusts \eqn{x(i,j)} to \eqn{x(i,j) - mean[x(i,j)]}
+#' where the mean is taken over all samples \eqn{j} in \eqn{B}.
+#' 
+#' @param data The input data. A list with components: x- an expression genes
+#' in the rows, samples in the columns, and y- a vector of the class labels for
+#' each sample, and batchlabels- a vector of batch labels for each sample.
+#' @return A data object of the same form as the input data, with x replaced by
+#' the adjusted x
+#' @author Trevor Hastie,Robert Tibshirani, Balasubramanian Narasimhan, and
+#' Gilbert Chu
+#' @examples
+#' 
+#' suppressWarnings(RNGversion("3.5.0"))
+#' set.seed(120)
+#' #generate some data
+#' x <- matrix(rnorm(1000*20),ncol=20)
+#' y <- sample(c(1:4),size=20,replace=TRUE)
+#' batchlabels <- sample(c(1:5),size=20,replace=TRUE)
+#' mydata <- list(x=x,y=factor(y),batchlabels=factor(batchlabels))
+#' 
+#' mydata2 <- pamr.batchadjust(mydata)
+#' 
+#' @export pamr.batchadjust
 pamr.batchadjust <- function(data) {
   if (is.null(data$batchlabels)) {
     stop("batch labels are not in data object")