pamr.cv {pamr}

A function to cross-validate the nearest shrunken centroid classifier

Description

A function to cross-validate the nearest shrunken centroid classifier produced by pamr.train

Usage

pamr.cv(fit, data, nfold = NULL, folds = NULL, ...)

Arguments

fit

The result of a call to pamr.train

data

A list with at least two components: x- an expression genes in the rows, samples in the columns), and y- a vector of the class labels for each sample. Same form as data object used by pamr.train.

nfold

Number of cross-validation folds. Default is the smallest class size

folds

A list with nfold components, each component a vector of indices of the samples in that fold. By default a (random) balanced cross-validation is used

...

Any additional arguments that are to be passed to pamr.train

Details

pamr.cv carries out cross-validation for a nearest shrunken centroid classifier.

Value

A list with components

threshold

A vector of the thresholds tried in the shrinkage

errors

The number of cross-validation errors for each threshold value

loglik

The cross-validated multinomial log-likelihood value for each threshold value

size

A vector of the number of genes that survived the thresholding, for each threshold value tried.

.

yhat

A matrix of size n by nthreshold, containing the cross-validated class predictions for each threshold value, in each column

prob

A matrix of size n by nthreshold, containing the cross-validated class probabilities for each threshold value, in each column

folds

The cross-validation folds used

cv.objects

Train objects (output of pamr.train), from each of the CV folds

call

The calling sequence used

Author(s)

Trevor Hastie,Robert Tibshirani, Balasubramanian Narasimhan, and Gilbert Chu

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=factor(y), geneid=as.character(1:nrow(x)),
 genenames=paste("g",as.character(1:nrow(x)),sep=""))

mytrain <-   pamr.train(mydata)
mycv <- pamr.cv(mytrain,mydata)