survsrfens_cv {survcompare} | R Documentation |
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,
impute = 0,
impute_method = "missForest"
)
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 |
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" |
Value
list of outputs
Examples
df <- simulate_nonlinear()
ens_cv <- survsrfens_cv(df, names(df)[1:4])
summary(ens_cv)