superpc: superpc.predict.red.cv – R documentation

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superpc.predict.red.cv

Cross-validation of feature selection for 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

Supervised Principal Components

v1.12

GPL (>= 3) | file LICENSE

Authors

Eric Bair [aut], Jean-Eudes Dazard [cre, ctb], Rob Tibshirani [ctb]

Initial release

2020-10-19