superpc.predict.red.cv {superpc}R Documentation

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, n.shrinkage = 30, 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
n.shrinkage Number of shrinkage values to consider. Default 20.
sign.wt Allowable signs for feature weights. Can be "both", "positive", or "negative". Default: "both"

Details

Value

shrinkages Shrinkage values used
lrtest.shrink Likeihood ratio tests for reduced models
num.features Number of features used in each shrunken model
n.components Number of supervised principal components used
v.preval.red Outcome predictor from reduced models. Array of n.shrinkage by (number of test observations) by n.components
n.components Number of supervised principal component used
sign.wt Allowable signs for feature weights
type Type of outcome
call calling sequence

Note

~~further notes~~

Author(s)

Eric Bair and Robert Tibshirani

References

~put references to the literature/web site here ~

See Also

Examples


set.seed(332)
#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")
aa<-superpc.cv(a, data)

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)

fit.redcv<- superpc.predict.red.cv(fit.red, aa,  data,  .6)


[Package Contents]