Hi,

I am running 'superpc' to model a supervised principal component predictor
to predict survival and I am having problems extracting feature weights to
derive a formula for calculating a score. I am new to R so please pardon if
the solution is obvious.

After running superpc.predict.red to form the reduced model, I am unable to
extract feature weights that are used to construct the reduced predictor.

According to documentation for 'superpc', "wt" corresponding to "Weight for
each feature, in constructing the reduced predictor" is one of the values
of the output list from superpc.predict.red. However, after running
superpc.predict.red, I do not find this value in the output.

The same is true when I run the example script provided in the
documentation, as below:

library(superpc)
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)


fit.red does not include a value called "wt".

I am trying to derive a formula based on the weights of selected features
which will to assign a supervised principal components score, with which
survival outcome can be determined.

Am I doing something wrong? Any advice on the use of this function will be
much appreciated.



Thanks,

Yukti

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