superpc.cv: Cross-validation for supervised principal components

In superpc: Supervised Principal Components

Description

This function uses a form of cross-validation to estimate the optimal feature threshold in supervised principal components

Usage

    superpc.cv(fit,
               data, 
               n.threshold=20,
               n.fold=NULL,
               folds=NULL,
               n.components=3, 
               min.features=5, 
               max.features=nrow(data$x),
               compute.fullcv= TRUE,
               compute.preval=TRUE, 
               xl.mode=c("regular","firsttime","onetime","lasttime"), 
               xl.time=NULL,
               xl.prevfit=NULL)

Arguments

fit	Object returned by superpc.train
data	Data object of form described in superpc.train documentation
n.threshold	Number of thresholds to consider. Default 20.
n.fold	Number of cross-validation folds. default is around 10 (program pick a convenient value based on the sample size
folds	List of indices of cross-validation folds (optional)
n.components	Number of cross-validation components to use: 1,2 or 3.
min.features	Minimum number of features to include in determining range for threshold. Default 5.
max.features	Maximum number of features to include in determining range for threshold. Default is total number of features in the dataset
compute.fullcv	Should full cross-validation be done?
compute.preval	Should full pre-validation be done?
xl.mode	Used by Excel interface only
xl.time	Used by Excel interface only
xl.prevfit	Used by Excel interface only

Details

This function uses a form of cross-validation to estimate the optimal feature threshold in supervised principal components. To avoid prolems with fitting Cox models to samll validation datastes, it uses the "pre-validation" approach of Tibshirani and Efron (2002)

Value

threshold	Vector of thresholds considered
nonzero	Number of features exceeding each value of the threshold
scor.preval	Likelihood ratio scores from pre-validation
scor	Full CV scores
folds	Indices of CV folds used
featurescores.folds	Feature scores for each fold
v.preval	The pre-validated predictors
type	problem type
call	calling sequence

Author(s)

• "Eric Bair, Ph.D."

• "Jean-Eudes Dazard, Ph.D."

• "Rob Tibshirani, Ph.D."

Maintainer: "Jean-Eudes Dazard, Ph.D."

References

• E. Bair and R. Tibshirani (2004). "Semi-supervised methods to predict patient survival from gene expression data." PLoS Biol, 2(4):e108.

• E. Bair, T. Hastie, D. Paul, and R. Tibshirani (2006). "Prediction by supervised principal components." J. Am. Stat. Assoc., 101(473):119-137.

Examples

## Not run: 
set.seed(332)

#generate some data
x <- matrix(rnorm(50*30), ncol=30)
y <- 10 + svd(x[1:50,])$v[,1] + .1*rnorm(30)
censoring.status <- sample(c(rep(1,20), rep(0,10)))

featurenames <- paste("feature", as.character(1:50), sep="")
data <- list(x=x, 
             y=y, 
             censoring.status=censoring.status, 
             featurenames=featurenames)

a <- superpc.train(data, type="survival")
aa <- superpc.cv(a, data)

## End(Not run)

superpc documentation built on Oct. 24, 2020, 1:07 a.m.