CVrankSurv_fct: Main function of SurvRank.

In SurvRank: Rank Based Survival Modelling

Description

Main input function for SurvRank.

Usage

CVrankSurv_fct(data, t.times, cv.out, cv.in, fs.method = "lasso.rank",
  nr.var = 10, sd1 = 0.95, ncl = 1, weig.t = T, n1 = 0.1,
  c.time = 10, ...)

Arguments

data	input of data as a list in the format: list.name$x data.frame of covariates. list.name$y response as a survival object, derived from Surv.
t.times	number of times the cross-validation should be repeated
cv.out	number of folds in outer cross validation loop (for estimation of the predictive accuracy)
cv.in	number of folds in inner cross validation loop (for model selection on the training set)
fs.method	Defaults to "lasso.rank". Ranking method to be applied. One of c("lasso.rank","conc.rank","rf.rank","boost.rank","cox.rank","rpart.rank","randcox.rank","wang.rank")
nr.var	Number of variables up to which stepwise selection should be carried out. Has to be smaller than n number of observations.
sd1	factor to which sparser solutions should be chosen. Not maximum Survival AUC in inner loop is used in stepwise selection, instead max(survAUC)*sd1 leading to sparser solutions
ncl	Defaults to 1. Number of clusters for parallel execution.
weig.t	Defaults to TRUE. Should a weighting of features be performed.
n1	used in weighting function if weig.t=T. Find details in weighting_fct
c.time	as defined in package survAUC time; a positive number restricting the upper limit of the time range under consideration.
...	arguments that can be passed to underlying functions, not used now

Details

details to follow

Value

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

method	ranking method
accuracy$ranking	full ranking of all model estimations
accuracy$pred.in	averaged inner AUCs of stepwise selection
accuracy$pred.out	predictions of testset
accuracy$used.rank	only used features according to stepwise selection
accuracy$used.rank1se	only used features according to stepwise selection with factor sd1
accuracy$auc.out	matrix of dimension cv.out times t.times of survival AUCs.
accuracy$auc.out1se	matrix of dimension cv.out times t.times of survival AUCs with factor sd1.
rank.mat	matrix of ranks per feature. If not selected, it is set to number of features.
out.mat	0/1 matrix for features selected
out.mat1se	0/1 matrix for features selected with factor sd1 application
top1se	unweighted toplist with factor sd1
toplist	unweighted toplist
weighted	weighted toplist with applied weighting function
rank	toplist of ranked features according to ranks

Examples

## Simulating a survival data set
N=100; p=10; n=4
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
## Ranking the features according to their significance in the univariate cox models
out.cox<-CVrankSurv_fct(data,2,3,3,fs.method="cox.rank")
## Ranking the features according to the LASSO algorithm
## Not run: 
out.lasso<-CVrankSurv_fct(data,2,5,5,fs.method="lasso.rank")
## End(Not run)

SurvRank documentation built on May 30, 2017, 2:53 a.m.