riskscore_fct: Main function of SurvRank.
In SurvRank: Rank Based Survival Modelling

Description Usage Arguments Details Value Examples

Main input function for SurvRank.

1 2	riskscore_fct(cv.ob, data, th = 0.5, surv.tab = c(0.5), f = NA, fix.var = NA, list.t = "weighted", ncl = 1, plt = F, ...)

`cv.ob`	output of the `CVrankSurv_fct`
`data`	same list used as input in `CVrankSurv_fct`
`th`	Defaults to 0.5. Threshold of used features. th=0.5 majority vote approach
`surv.tab`	Defaults to c(0.5). Calculates for selected features survival curves. `surv.tab` determines quantiles of predictions.
`f`	Defaults to NA. ranking approach function. One of `fsSurvRankConc`, `fsSurvRankGlmnet`, `fsSurvRankRf`, `fsSurvRankBoost`, `fsSurvRankCox`, `fsSurvRankRandCox`, `fsSurvRankRpart`, `fsSurvRankWang` or NA, no calculation
`fix.var`	Defauts to NA. not NA, fixed number of features is calculated
`list.t`	Defauls to "weighted". Which toplist should be chosen? Possible choices are "weighted", "unweighted", "rank", "top1se","cluster" or "final"
`ncl`	Defaults to 1. Number of clusters for parallel execution.
`plt`	Default=F. Should plot of survival curves be generated?
`...`	arguments that can be passed to underlying functions, not used now

details to follow

Output of the riskscore_fct, basically a list containing the following elements

`selnames`	toplist of features that have been chosen
`fixR`	Matrix of survival AUCs with fixed number of features, but not fixed features!! (could also be calculated before)
`model`	cox model output for selected features, according to `list.t`
`aic`	AIC criterion of cox model
`sum.model`	summary object of the fitted cox model
`concordance`	concordance measure of fitted cox model
`sfit`	survfit object of the cox model)
`pfit`	predictions of the cox model (fitted values)
`sfit.tab`	survfit object according to `surv.tab` seperation
`sfit.cox`	Cox model on the groups generated by `surv.tab`
`sfit.diff`	surfdiff: Tests if there is a difference between two or more survival curves using the G-rho family of tests, or for a single curve against a known alternative

Additionally two plots are generated: if f is not NA, a boxplot of the survival AUCs, averaged for cross-validation iterations. The second plot shows the resulting survival curves according to surv.tab.

## Simulating a survival data set
N=100; p=10; n=3
x=data.frame(matrix(rnorm(N*p),nrow=N,p))
beta=rnorm(n)
mx=matrix(rnorm(N*n),N,n)
fx=mx[,seq(n)]%*%beta/3
hx=exp(fx)
ty=rexp(N,hx)
tcens=1-rbinom(n=N,prob=.3,size=1)
y=Surv(ty,tcens)
data=list()
data$x<-x; data$y<-y
out<-CVrankSurv_fct(data,2,3,3,fs.method="cox.rank")
## Using the weighted toplist
risk<-riskscore_fct(out,data,list.t="weighted")
## Selected names
risk$selnames