superpc.predict.red {superpc} | R Documentation |
Forms reduced models involving a subset of the features, by soft-thresholding of correlations between the features and the supervised principal component predictor.
superpc.predict.red(fit, data, data.test, threshold, n.components = 1, n.shrinkage = 20, compute.lrtest = TRUE, sign.wt = "both")
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 use. Should be 1,2 or 3. |
n.shrinkage |
Number of shrinkage values to consider. Default 20. |
compute.lrtest |
Should the likelihood ratio test be computed? Default TRUE |
sign.wt |
Allowable signs for feature weights. Can be "both", "positive", or "negative". Default: "both" |
~Describe the value returned If it is a LIST, use
shrinkages |
Shrinkage values used |
lrtest.shrink |
Likeihood ratio tests for reduced models |
corr.with.full |
Correlation of each reducted predictor with full superivsed PC predictor |
num.features |
Number of features used in each shrunken model |
features.list |
Indiced of features used |
import |
Importance scores for features |
v.test |
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 |
~~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) 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.fit.to.outcome(a, data.test, fit.red$v.test[5,,])