This function uses a form of crossvalidation to estimate the optimal feature threshold in supervised principal components
1 2 3 4 
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 crossvalidation folds. default is around 10 (program pick a convenient value based on the sample size 
folds 
List of indices of crossvalidation folds (optional) 
n.components 
Number of crossvalidation 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 crossvalidation be done? 
compute.preval 
Should full prevalidation be done? 
xl.mode 
Used by Excel interface only 
xl.time 
Used by Excel interface only 
xl.prevfit 
Used by Excel interface only 
This function uses a form of crossvalidation to estimate the optimal feature threshold in supervised principal components. To avoid prolems with fitting Cox models to samll validation datastes, it uses the "prevalidation" approach of Tibshirani and Efron (2002)
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 prevalidation 
scor 
Full CV scores 
folds 
Indices of CV folds used 
featurescores.folds 
Feature scores for each fold 
v.preval 
The prevalidated predictors 
type 
problem type 
call 
calling sequence 
Eric Bair and Robert Tibshirani
1 2 3 4 5 6 7 8 9 10 11  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)

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