Usage
itree.control(minsplit = 20, minbucket = round(minsplit/3), maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, xval = 10,surrogatestyle = 0, maxdepth = 30, impscale=3,interp_param1=0,interp_param2=0,cp,...)
Arguments
minsplit
the minimum number of observations that must exist in a node in order for
a split to be attempted.
minbucket
the minimum number of observations in any terminal minbucket or minsplit is specified,
the code either sets minsplit to minbucket*3
or minbucket to minsplit/3, as appropriate.
maxcompete
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.
maxsurrogate
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.
usesurrogate
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.
xval
number of cross-validations. This uses the xval method in rpart, which cross-
validated across a set of cp values. For one-sided methods cp is not defined
and so passing cp>0 results in an error. If a penalty is used, that same
penalty and penalization constant is used in the cross-validations.
surrogatestyle
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.
maxdepth
Set the maximum depth of any node of the final tree, with the root
node counted as depth 0. Values greater than 30 itree will
give nonsense results on 32-bit machines.
impscale
How to scale the 'improve' function so that the interpretability penalty can be between 0 and 1.
impscale=1 means scale by root node's impurity, =2 means scale by parent node's impurity.
=3 means no scaling. Currently if a penalization method is used and impscale is not
specified, we default to impscale=2. The default is recommended.
interp_param1
First interpretability parameter controlling the tradeoff between
reducing loss and introducing less interpretable splits into the tree. Higher values
means greater penalty for less interpretable splits.
interp_param2
Second interpretability parameter. Currently not used.
cp
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 Rsquare 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. Note that this is not defined for one-sided methods,
so passing cp=.01 with method="purity", for instance, results in an
error. To control the size of purity or extremes trees, use minsplit
and/or minbucket.
...
mop up other arguments.