# pamr.cv

##### A function to cross-validate the nearest shrunken centroid classifier

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

A vector of the thresholds tried in the shrinkage

The number of cross-validation errors for each threshold value

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

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

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

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

The cross-validation folds used

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

The calling sequence used

##### Examples

```
# NOT RUN {
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)
# }
```

*Documentation reproduced from package pamr, version 1.56.1, License: GPL-2*