customSuperPc: Univarate shrinkage prediction in the Cox model
In bernau/survHDExtra: Addtitional Features for package survHD

Description Usage Arguments Value Author(s) References See Also Examples

See http://cran.r-project.org/web/packages/superpc/index.html for more details

Usually, this function is not called directly but embedded in a call to customSurv from the main package survHD (see examples).

Note that "lambda", if specified, corresponds to the "threshold" argument in superpc.

1	customSuperPc(Xlearn, Ylearn, learnind,...)

`Xlearn`	Gene expression data (a `data.frame`)
`Ylearn`	Survival Response, an object of class `Surv`.
`learnind`	An index vector specifying the observations that belong to the learning set. May be `missing`; in that case, the learning set consists of all observations and predictions are made on the learning set.
`...`	Further arguments that shall be passed to `superpc.train` function from the superpc R package (http://cran.r-project.org/web/packages/superpc/index.html)

An object of class ModelLearned.

Levi Waldron lwaldron@hsph.harvard.edu Christoph Bernau bernau@ibe.med.uni-muenchen.de Markus Riester <riester@jimmy.harvard.edu>

Bair E, Tibshirani R: Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data. PLoS Biology 2004, 2:e108.

learnSurvival

require(survHD)
require(survival)
require(survHDExtra)	
	set.seed(20)
	nsamples <- 100
	X <- matrix(rnorm(nsamples*1000),nrow=nsamples)
	colnames(X) <- make.names(1:ncol(X))
	rownames(X) <- make.names(1:nrow(X))
	time <- rexp(nsamples)
	cens <- sample(0:1,size=nsamples,replace=TRUE)
	y <- Surv(time,cens)
ModelLearned<-customSurv(X=X,y=y,learnind=1:75,customSurvModel=customSuperPc)	
##linear risk score
predict(ModelLearned@model,newdata=X[-(1:75),],type='lp')
##survival probabilities at each uncensored time
mytimegrid <- sort(c(0.0,y[y[,2]==1,1]))
SurvivalProbs <- predict(ModelLearned,X[-(1:75),],type='SurvivalProbs',timegrid=mytimegrid,gbm=TRUE)@SurvivalProbs
plot(SurvivalProbs,xlab="Time", ylab="Probability of Survival")