survsrfstack_cv: Cross-validates stacked ensemble of the CoxPH and Survival Random Forest models

Description

Cross-validates stacked ensemble of the CoxPH and Survival Random Forest models

Usage

survsrfstack_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,
  impute = 0,
  impute_method = "missForest"
)

Arguments

df: data, "time" and "event" should describe survival outcome
predict.factors: list of predictor names
fixed_time: time at which performance is maximized
outer_cv: number of cross-validation folds for model validation
inner_cv: number of cross-validation folds for hyperparameters' tuning
repeat_cv: number of CV repeats, if NaN, runs once
randomseed: random seed to control tuning including data splits
return_models: TRUE/FALSE, if TRUE returns all CV objects
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
suppresswarn: TRUE/FALSE, TRUE by default
impute: 0/1/2/3 for no imputation / option 1 (proper way) / option 2 (faster way) / option 3 (complete cases), more in documentation and vignette
impute_method: "missForest"