superpc.predict.red: Feature selection for supervised principal components

In superpc: Supervised Principal Components

Description

Forms reduced models to approximate the supervised principal component predictor.

Usage

    superpc.predict.red(fit, 
                        data, 
                        data.test, 
                        threshold, 
                        n.components=3, 
                        n.shrinkage=20, 
                        shrinkages=NULL,
                        compute.lrtest=TRUE,
                        sign.wt="both",
                        prediction.type=c("continuous", "discrete"), 
                        n.class=2)
                        

Arguments

fit	Object returned by superpc.train
data	Training data object, of form described in superpc.train dcoumentation
data.test	Test data object; same form as train
threshold	Feature score threshold; usually estimated from superpc.cv
n.components	Number of principal components to examine; should equal 1,2, etc up to the number of components used in training
n.shrinkage	Number of shrinkage values to consider. Default 20.
shrinkages	Shrinkage values to consider. Default NULL.
compute.lrtest	Should the likelihood ratio test be computed? Default TRUE
sign.wt	Signs of feature weights allowed: "both", "pos", or "neg"
prediction.type	Type of prediction: "continuous" (Default) or "discrete". In the latter, superprc score is divided into n.class groups
n.class	Number of groups for discrete predictor. Default 2.

Details

Soft-thresholding by each of the "shrinkages" values is applied to the PC loadings. This reduce the number of features used in the model. The reduced predictor is then used in place of the supervised PC predictor.

Value

shrinkages	Shrinkage values used
lrtest.reduced	Likelihood ratio tests for reduced models
num.features	Number of features used in each reduced model
feature.list	List of features used in each reduced model
coef	Least squares coefficients for each reduced model
import	Importance scores for features
wt	Weight for each feature, in constructing the reduced predictor
v.test	Outcome predictor from reduced models. Array of n.shrinkage by (number of test observations)
v.test.1df	Outcome combined predictor from reduced models. Array of n.shrinkage by (number of test observations)
n.components	Number of principal components used
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

set.seed(332)

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

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

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