pamr.cv: A function to cross-validate the nearest shrunken centroid...

In pamr: Pam: Prediction Analysis for Microarrays

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)

Example output

Loading required package: cluster
Loading required package: survival
1234567891011121314151617181920212223242526272829301234Fold 1 :123456789101112131415161718192021222324252627282930
Fold 2 :123456789101112131415161718192021222324252627282930
Fold 3 :123456789101112131415161718192021222324252627282930
Fold 4 :123456789101112131415161718192021222324252627282930

pamr documentation built on May 2, 2019, 12:35 p.m.