Various parameters that control aspects of the rpart fit.
rpart.control(minsplit = 20, minbucket = round(minsplit/3), cp = 0.01,
maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, xval = 10,
surrogatestyle = 0, maxdepth = 30, …)the minimum number of observations that must exist in a node in order for a split to be attempted.
the minimum number of observations in any terminal <leaf> node.
If only one of minbucket or minsplit is specified,
the code either sets minsplit to minbucket*3
or minbucket to minsplit/3, as appropriate.
complexity parameter. Any split that does not decrease the overall
lack of fit by a factor of cp is not attempted. For instance,
with anova splitting, this means that the overall R-squared must
increase by cp at each step. The main role of this parameter
is to save computing time by pruning off splits that are obviously
not worthwhile. Essentially,the user informs the program that any
split which does not improve the fit by cp will likely be
pruned off by cross-validation, and that hence the program need
not pursue it.
the number of competitor splits retained in the output. It is useful to know not just which split was chosen, but which variable came in second, third, etc.
the number of surrogate splits retained in the output. If this is set to zero the compute time will be reduced, since approximately half of the computational time (other than setup) is used in the search for surrogate splits.
how to use surrogates in the splitting process. 0 means
display only; an observation with a missing value for the primary
split rule is not sent further down the tree. 1 means use
surrogates, in order, to split subjects missing the primary variable;
if all surrogates are missing the observation is not split. For value
2 ,if all surrogates are missing, then send the observation in
the majority direction. A value of 0 corresponds to the action
of tree, and 2 to the recommendations of Breiman
et.al (1984).
number of cross-validations.
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.
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.
mop up other arguments.
A list containing the options.