survcompare (version 0.1.2)

survensemble_train: 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

survensemble_train(
  df_train,
  predict.factors,
  fixed_time = NaN,
  inner_cv = 3,
  randomseed = NULL,
  srf_tuning = list(),
  fast_version = TRUE,
  oob = TRUE,
  useCoxLasso = FALSE,
  var_importance_calc = 1
)

Value

trained object of class survensemble

Arguments

df_train

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

predict.factors

list of the column names to be used as predictors

fixed_time

for which the performance is maximized

inner_cv

number of inner cycles for model tuning

randomseed

random seed

srf_tuning

list of mtry, nodedepth and nodesize, to use default supply empty list()

fast_version

TRUE/FALSE, TRUE by default

oob

FALSE/TRUE, TRUE by default

useCoxLasso

FALSE/TRUE, FALSE by default

var_importance_calc

FALSE/TRUE, TRUE by default