Argument of: rpart::rpart.control

controls the selection of a best surrogate. If set to 0 (default) the program uses the total number of correct classification for a potential surrogate variable, if set to 1 it uses the percent correct, calculated over the non-missing values of the surrogate. The first option more severely penalizes covariates with a large number of missing values.

Argument of: rpart::rpart.control

Set the maximum depth of any node of the final tree, with the root node counted as depth 0.  Values greater than  30 rpart will give nonsense results on 32-bit machines.

Argument of: rpart::rpart

optional parameters for the splitting function. Anova splitting has no parameters. Poisson splitting has a single parameter, the coefficient of variation of the prior distribution on the rates.  The default value is 1. Exponential splitting has the same parameter as Poisson. For classification splitting, the list can contain any of: the vector of prior probabilities (component prior), the loss matrix (component loss) or the splitting index (component split).  The priors must be positive and sum to 1.  The loss matrix must have zeros on the diagonal and positive off-diagonal elements.  The splitting index can be gini or information.  The default priors are proportional to the data counts, the losses default to 1, and the split defaults to gini.

Defined as: mean(y), where y is the vector of costs for the predicted classes.