superpc.listfeatures {superpc}R Documentation

Return a list of the important predictors

Description

Return a list of the important predictor

Usage

superpc.listfeatures(data, train.obj, fitred, component.number, shrinkage)

Arguments

data Data object
train.obj Object returned by superpc.train
fitred Object returned by superpc.predict.red
component.number Index of principal component to use (1,2, or 3)
shrinkage Amount of shrinkage

Details

Value

Returns matrix of features and their importance scores, in order of decreasing absolute value of importance score. The importance score is the correlation of the reduced predictor and the full supervised PC predictor. It also lists the raw score- for survival data, this is the Cox score for that feature; for regression, it is the standardized regression coefficient.

Author(s)

Eric Bair and Rob Tibshirani

Examples

#generate some data

x<-matrix(rnorm(1000*40),ncol=40)
y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
ytest<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
status<- sample(c(rep(1,30),rep(0,10)))
status.test<- sample(c(rep(1,30),rep(0,10)))
featurenames <- paste("feature",as.character(1:1000),sep="")
data<-list(x=x,y=y, status=status, featurenames=featurenames)
data.test<-list(x=x,y=ytest, status=status.test, featurenames= featurenames)

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

fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")

fit.red<- superpc.predict.red(a,data, data.test, .6)
superpc.listfeatures(data, a,  fit.red,1,shrinkage=0.3)


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