Argument of: randomForestSRC::rfsrc

Type of bootstrap when by.root is in effect.  Choices are swr (sampling with replacement, the default action) and swor (sampling without replacement).

Argument of: randomForestSRC::rfsrc

Requested size of bootstrap when by.root is in effect (if missing the default action is the usual bootstrap).

Argument of: randomForestSRC::rfsrc

Should terminal node membership and inbag information be returned?

Argument of: randomForestSRC::rfsrc

Action taken if the data contains NA's. Possible values are na.omit or na.impute. The default na.omit removes the entire record if even one of its entries is NA (for x-variables this applies only to those specifically listed in 'formula').  Selecting na.impute imputes the data. See below for more details regarding missing data imputation.

Argument of: randomForestSRC::rfsrc

Number of iterations of the missing data algorithm. Performance measures such as out-of-bag (OOB) error rates tend to become optimistic if nimpute is greater than 1.

Argument of: randomForestSRC::rfsrc

Integer value used for survival to constrain ensemble calculations to a grid of ntime time points.  Alternatively if a vector of values of length greater than one is supplied, it is assumed these are the time points to be used to constrain the calculations (note that the constrained time points used will be the observed event times closest to the user supplied time points).  If no value is specified, the default action is to use all observed event times.

Argument of: randomForestSRC::rfsrc

Proximity of cases as measured by the frequency of sharing the same terminal node.  This is an nxn matrix, which can be large.  Choices are inbag, oob, all, TRUE, or FALSE. Setting proximity = TRUE is equivalent to proximity = "inbag".

Argument of: randomForestSRC::rfsrc

Vector of non-negative weights where entry k, after normalizing, is the probability of selecting variable k as a candidate for splitting a node.  Default is to use uniform weights.  Vector must be of dimension p, where p equals the number of variables, otherwise the default is invoked. It is generally better to use real weights rather than integers.  With larger sizes of p, the slightly different sampling algorithms used in the two scenarios can result in dramatically different execution times.