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SurvRank (version 0.1)
Rank Based Survival Modelling
Description
Estimation of the prediction accuracy in a unified survival AUC approach. Model selection and prediction estimation based on a survival AUC. Stepwise model selection, based on several ranking approaches.
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Version
Version
0.1
Install
install.packages('SurvRank')
Monthly Downloads
2
Version
0.1
License
GPL-2
Maintainer
Michael Laimighofer
Last Published
August 26th, 2015
Functions in SurvRank (0.1)
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fsSurvRankRpart
Rpart ranking function
fsRankBoostAUC_fct
Wrapper function using additive models with component-wise boosting as ranking method
riskscore_fct
Main function of SurvRank.
CVrankSurv_fct
Main function of SurvRank.
fsSurvRankGlmnet
L1 (lasso) ranking function
risk_newdat
Main function of SurvRank.
fsRankRfAUC_fct
Wrapper function using the random forests as ranking method
fsSurvRankRf
Random forest ranking function
fsSurvRankBoost
Boost ranking function
fsRankConcAUC_fct
Wrapper function using the concordance measure as ranking method
fixRank_fct
Wrapper function using f as ranking method
fsSurvRankRandCox
Random Cox ranking function
innerAUC_fct
Calculates inner and outer survival AUCs of training and testset
glmnetRank
Ranks features of a previously fitted
glmnet
object
plot_CVsurv
Main function of SurvRank
fsSurvRankWang
Random Cox ranking function
fsSurvRankConc
Concordance ranking function
fin_surv_model_fct
Building the final survRank model
fsRankLassoAUC_fct
Wrapper function using the L1 norm (lasso) as ranking method
fsSurvRankCox
Cox ranking function
fsRankCoxAUC_fct
Wrapper function using the univariate Cox models as ranking method
fsRankWangAUC_fct
Wrapper function using univariate cox models for a randomly selected subset of the data as ranking method
fsRankMonsterAUC_fct
Wrapper function using cox models with randomly selected nmax variables as ranking method
crossvalFolds
Creating stratified cross validation folds
fsRankRpartAUC_fct
Wrapper function using recursive partitioning and regression trees as ranking method
msSurv_fct
Function selects for the ranking function approaches
weighting_fct
Weigthing function for selected features