Argument of: randomForestSRC::rfsrc

Should the forest object be returned?  Used for prediction on new data and required by many of the functions used to parse the RF-SRC object.  It is recommended not to change the default setting.

Argument of: randomForestSRC::rfsrc

Return variables used for splitting?  Default is FALSE.  Possible values are all.trees which returns a vector where each element records the number of times a split occurred on a variable, and by.tree which is a matrix recording the number of times a split occurred on a variable in a specific tree.

Argument of: randomForestSRC::rfsrc

Records the minimal depth for each variable. Default is FALSE.  Possible values are all.trees which returns a matrix recording the minimal depth for a variable (columns) for a specific case (rows) averaged over the forest, and by.tree which returns a three-dimensional array recording minimal depth for a specific case (first dimension) for a variable (second dimension) for a specific tree (third dimension).

Argument of: randomForestSRC::rfsrc

Negative integer specifying seed for the random number generator.

Argument of: randomForestSRC::rfsrc

Number of seconds between updates to the user on approximate time to completion.

Argument of: randomForestSRC::rfsrc

Should split statistics be returned?  Values can be parsed using stat.split.

Fraction of all pairs of subjects whose predicted survival times are correctly ordered among all subjects that can actually be ordered. In other words, it is the probability of concordance between the predicted and the observed survival. Corrected by weighting with IPCW as suggested by Uno. Implemented in survAUC::UnoC.

Integrated brier score using Kaplan-Meier estimator for weighting

Only works for methods for which probabilities are provided via pec::predictSurvProb. Currently these are only coxph and randomForestSRC. To set an upper time limit, set argument max.time (defaults to max time in test data). Implemented in pec::pec

Amount of features of feature group 'clin' used for the model