superpc.predict.red.cv: Cross-validation of feature selection for supervised...

In superpc: Supervised Principal Components

Description

Applies superpc.predict.red to cross-validation folds generates in superpc.cv. Uses the output to evaluate reduced models, and compare them to the full supervised principal components predictor.

Usage

    superpc.predict.red.cv(fitred, 
                           fitcv, 
                           data, 
                           threshold, 
                           sign.wt="both")

Arguments

fitred	Output of superpc.predict.red
fitcv	Output of superpc.cv
data	Training data object
threshold	Feature score threshold; usually estimated from superpc.cv
sign.wt	Signs of feature weights allowed: "both", "pos", or "neg"

Value

lrtest.reduced	Likelihood ratio tests for reduced models
components	Number of supervised principal components used
v.preval.red	Outcome predictor from reduced models. Array of num.reduced.models by (number of test observations)
type	Type of outcome
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*20), ncol=20)
y <- 10 + svd(x[1:10,])$v[,1] + .1*rnorm(20)
ytest <- 10 + svd(x[1:10,])$v[,1] + .1*rnorm(20)
censoring.status <- sample(c(rep(1,15), rep(0,5)))
censoring.status.test <- sample(c(rep(1,15), rep(0,5)))

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

a <- superpc.train(data, type="survival")
aa <- superpc.cv(a, data)
fit.red <- superpc.predict.red(a,
                               data, 
                               data.test, 
                               threshold=.6)
fit.redcv <- superpc.predict.red.cv(fit.red, 
                                    aa, 
                                    data, 
                                    threshold=.6)

## End(Not run)

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