superpc (version 1.09)

superpc.predict.red: Feature selection for 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.

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

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.

References

~put references to the literature/web site here ~

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)
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")

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)
superpc.plotred.lrtest(fit.red)

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