superpc.predict.red.cv {superpc} | R Documentation |
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.
superpc.predict.red.cv(fitred, fitcv, data, threshold, sign.wt="both")
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" |
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 |
~~further notes~~
Eric Bair and Robert Tibshirani
~put references to the literature/web site here ~
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) censoring.status<- sample(c(rep(1,30),rep(0,10))) censoring.status.test<- sample(c(rep(1,30),rep(0,10))) featurenames <- paste("feature",as.character(1:1000),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<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous") fit.red<- superpc.predict.red(a,data, data.test, threshold= .6) fit.redcv<- superpc.predict.red.cv(fit.red, aa, data, threshold= .6) superpc.plotred.lrtest(fit.redcv)