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

Description Usage Arguments Details Value Examples

Main input function for SurvRank.

1 2	risk_newdat(dat_new, sel_names, dat_old, cv.out = 10, c.time = NA, detail = NA, plot = F, surv.tab = c(0.5), mcox = T)

`dat_new`	a new data set that is not used for the model building but only for prediction
`sel_names`	the variables that were selected (from riskscore_fct) (see `CVrankSurv_fct`)
`dat_old`	the data set used to fit the survival model
`cv.out`	number of cross-validation folds for the prediction
`c.time`	as defined in UnoCsurvAUC time; a positive number restricting the upper limit of the time range under consideration
`detail`	TRUE do the prediction and Uno's C-Statistic computation for the models using 1:`sel_names` variables FALSE only save the statistics for the different cross validation folds
`plot`	TRUE do a plot of the survival curves FALSE no plot
`surv.tab`	Defaults to c(0.5). Calculates for selected features survival curves. `surv.tab` determines quantiles of predictions.
`mcox`	TRUE a cox model is fitted FALSE a Cox model with ridge penalty using `cv.out` cross-validation folds is fitted

details to follow

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

`unocv`	Matrix of censoring-adjusted C-statistic by Uno et al. for the different cross-validation folds and if `detail=T` as well for different number of variables
`unoi`	if `detail=T` Vector of censoring-adjusted C-statistic by Uno et al. for the different number of variables, if `detail=FALSE` it correspons to `uno_new`
`rs`	model prediction for the new data set
`sfit.tab`	survfit object according to `surv.tab` seperation
`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
`model`	model output for `dat_old` and using the variables given by `sel_names`
`uno_new`	the censoring-adjusted C-statistic by Uno et al. using the prediction for `dat_new`

Additionally if plot is T, the survival curves given by sfit.tab are plotted

## 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
## CV object
out<-CVrankSurv_fct(data,2,3,3,fs.method="cox.rank")
## The variables selected from the \code{\link{riskscore_fct}}
selected<-riskscore_fct(out,data,list.t="weighted")$selnames
## Applying the risk_newdat function
x=data.frame(matrix(rnorm(N*p),nrow=N,p))
beta=rnorm(n)
mx=matrix(rnorm(N*n),N,n)
fx=mx[,seq(n)]%*%beta/3
hx=exp(fx)
ty=rexp(N,hx)
tcens=1-rbinom(n=N,prob=.3,size=1)
y=Surv(ty,tcens)
data_new=list()
data_new$x<-x; data_new$y<-y
risk<-risk_newdat(data_new,selected,data)