survsrfens_train {survcompare} | R Documentation |
Fits an ensemble of Cox-PH and Survival Random Forest (SRF) with internal CV to tune SRF hyperparameters.
Description
Details: the function trains Cox model, then adds its out-of-the-box predictions to Survival Random Forest as an additional predictor to mimic stacking procedure used in Machine Learning and reduce over-fitting. #' Cox model is fitted to .9 data to predict the rest .1 for each 1/10s fold; these out-of-the-bag predictions are passed on to SRF
Usage
survsrfens_train(
df_train,
predict.factors,
fixed_time = NaN,
inner_cv = 3,
randomseed = NaN,
tuningparams = list(),
useCoxLasso = FALSE,
max_grid_size = 10,
var_importance_calc = FALSE,
verbose = FALSE
)
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 |
tuningparams |
if given, list of hyperparameters, list(mtry=c(), nodedepth=c(),nodesize=c()), otherwise a wide default grid is used |
useCoxLasso |
if CoxLasso is used (TRUE) or not (FALSE, default) |
max_grid_size |
number of random grid searches for model tuning |
var_importance_calc |
if variable importance is computed |
verbose |
FALSE (default)/TRUE |
Value
trained object of class survsrf_ens