superpc (version 1.09)

superpc.cv: Cross-validation for supervised principal components

Description

This function uses a form of cross-validation to estimate the optimal feature threshold in supervised principal components

Usage

superpc.cv(fit, data, n.threshold = 20,  n.fold = NULL, folds = NULL,   n.components = 3, min.features = 5, max.features = nrow(data$x),  compute.fullcv =  TRUE,
                 compute.preval = TRUE, xl.mode = c("regular",
                 "firsttime", "onetime", "lasttime"), xl.time = NULL,
                 xl.prevfit = NULL)

Arguments

fit

Object returned by superpc.train

data

Data object of form described in superpc.train documentation

n.threshold

Number of thresholds to consider. Default 20.

n.fold

Number of cross-validation folds. default is around 10 (program pick a convenient value based on the sample size

folds

List of indices of cross-validation folds (optional)

n.components

Number of cross-validation components to use: 1,2 or 3.

min.features

Minimum number of features to include, in determining range for threshold. Default 5.

max.features

Maximum number of features to include, in determining range for threshold. Default is total number of features in the dataset

compute.fullcv

Should full cross-validation be done?

compute.preval

Should full pre-validation be done?

xl.mode

Used by Excel interface only

xl.time

Used by Excel interface only

xl.prevfit

Used by Excel interface only

Value

• list(threshold = th, nonzero = nonzero, scor = out, scor.preval = out.preval, folds = folds, featurescores.folds = featurescores.folds, v.preval = cur2, type = type, call = this.call)
• threshold
  Vector of thresholds considered
• nonzero
  Number of features exceeding each value of the threshold
• scor.preval
  Likelihood ratio scores from pre-validation
• scor
  Full CV scores
• folds
  Indices of CV folds used
• featurescores.folds
  Feature scores for each fold
• v.preval
  The pre-validated predictors
• type
  problem type
• call
  calling sequence

Details

This function uses a form of cross-validation to estimate the optimal feature threshold in supervised principal components. To avoid prolems with fitting Cox models to samll validation datastes, it uses the "pre-validation" approach of Tibshirani and Efron (2002)

Examples

set.seed(332)
x<-matrix(rnorm(1000*40),ncol=40)
y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
censoring.status<- 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)


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