`na.action` has been set to `"na.impute"` by default to allow missing data support.

Argument of: randomForestSRC::rfsrc

Number of trees in the forest.

Argument of: randomForestSRC::rfsrc

Number of variables randomly selected as candidates for splitting a node.  The default is p/3 for regression, where p equals the number of variables.  For all other families (including unsupervised settings), the default is sqrt(p).  Values are always rounded up.

Argument of: randomForestSRC::rfsrc

Forest average number of unique cases (data points) in a terminal node.  The defaults are: survival (15), competing risk (15), regression (5), classification (1), mixed outcomes (3), unsupervised (3).  It is recommended to experiment with different nodesize values.

Argument of: randomForestSRC::rfsrc

Maximum depth to which a tree should be grown.  The default behaviour is that this parameter is ignored.

Argument of: randomForestSRC::rfsrc

Splitting rule used to grow trees.  See below for details.

Argument of: randomForestSRC::rfsrc

Non-negative integer value.  When zero or NULL, deterministic splitting for an x-variable is in effect.  When non-zero, a maximum of nsplit split points are randomly chosen among the possible split points for the x-variable. This significantly increases speed.

Argument of: randomForestSRC::rfsrc

Method for computing variable importance (VIMP). Because VIMP is computationally expensive, the default action is importance="none" (VIMP can always be recovered later using the functions vimp or predict). Setting importance=TRUE implements permutation VIMP. See below for more details.

Argument of: randomForestSRC::rfsrc

Bootstrap protocol.  The default is by.root which bootstraps the data by sampling with replacement at the root node before growing the tree (for sampling without replacement, see the option samptype).    If by.node is choosen, the data is bootstrapped at each node during the grow process.  If none is chosen, the data is not bootstrapped at all.  If by.user is choosen, the bootstrap specified by samp is used.  It is not possible to return OOB ensembles or prediction error if by.node or none are in effect.