survcompare (version 0.2.0)

survsrf_train: Fits randomForestSRC, with tuning by mtry, nodedepth, and nodesize. Underlying model is by Ishwaran et al(2008) https://www.randomforestsrc.org/articles/survival.html Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. The Annals of Applied Statistics. 2008;2:841–60.

Description

Fits randomForestSRC, with tuning by mtry, nodedepth, and nodesize. Underlying model is by Ishwaran et al(2008) https://www.randomforestsrc.org/articles/survival.html Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. The Annals of Applied Statistics. 2008;2:841–60.

Usage

survsrf_train(
  df_train,
  predict.factors,
  fixed_time = NaN,
  tuningparams = list(),
  max_grid_size = 10,
  inner_cv = 3,
  randomseed = NaN,
  verbose = TRUE
)

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

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

inner_cv

number of cross-validation folds for hyperparameters' tuning

randomseed

random seed to control tuning including data splits

verbose

TRUE/FALSE, FALSE by default

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)