survcompare (version 0.2.0)

survsrfstack_train: Trains the stacked ensemble of the CoxPH and Survival Random Forest

Description

Trains the stacked ensemble of the CoxPH and Survival Random Forest

Usage

survsrfstack_train(
  df_train,
  predict.factors,
  fixed_time = NaN,
  inner_cv = 3,
  randomseed = NaN,
  useCoxLasso = FALSE,
  tuningparams = list(),
  max_grid_size = 10,
  verbose = FALSE
)

Value

output = list(bestparams, allstats, model)

Arguments

df_train

data, "time" and "event" should describe survival outcome

predict.factors

list of predictor names

fixed_time

time at which performance is maximized

inner_cv

number of cross-validation folds for hyperparameters' tuning

randomseed

random seed to control tuning including data splits

useCoxLasso

if CoxLasso is used (TRUE) or not (FALSE, default)

tuningparams

if given, list of hyperparameters, list(mtry=c(), nodedepth=c(),nodesize=c()), otherwise a wide default grid is used

max_grid_size

number of random grid searches for model tuning

verbose

FALSE(default)/TRUE

Examples

d <-simulate_nonlinear(100)
p<- names(d)[1:4]
tuningparams = list(
 "mtry" = c(5,10,15),
 "nodedepth" = c(5,10,15,20),
 "nodesize" =    c(20,30,50)
)
m_srf<- survsrf_train(d,p,tuningparams=tuningparams)