survcompare (version 0.2.0)

survsrfens_cv: Cross-validates predictive performance for SRF Ensemble

Description

Cross-validates predictive performance for SRF Ensemble

Usage

survsrfens_cv(
  df,
  predict.factors,
  fixed_time = NaN,
  outer_cv = 3,
  inner_cv = 3,
  repeat_cv = 2,
  randomseed = NaN,
  return_models = FALSE,
  useCoxLasso = FALSE,
  tuningparams = list(),
  max_grid_size = 10,
  verbose = FALSE,
  suppresswarn = TRUE
)

Value

list of outputs

Arguments

df

data frame with the data, "time" and "event" for survival outcome

predict.factors

list of predictor names

fixed_time

at which performance metrics are computed

outer_cv

number of folds in outer CV, default 3

inner_cv

number of folds for model tuning CV, default 3

repeat_cv

number of CV repeats, if NaN, runs once

randomseed

random seed

return_models

TRUE/FALSE, if TRUE returns all trained models

useCoxLasso

TRUE/FALSE, default is FALSE

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

suppresswarn

TRUE/FALSE, TRUE by default

Examples

# \donttest{
rfcores_old <- options()$rf.cores; options(rf.cores=1)
df <- simulate_nonlinear()
ens_cv <- survsrfens_cv(df, names(df)[1:4])
summary(ens_cv)
options(rf.cores=rfcores_old)
# }