Usage
ctree_control(teststat = c("quad", "max"),
testtype = c("Bonferroni", "MonteCarlo",
"Univariate", "Teststatistic"),
mincriterion = 0.95, minsplit = 20, minbucket = 7,
stump = FALSE, nresample = 9999, maxsurrogate = 0,
mtry = 0, savesplitstats = TRUE, maxdepth = 0, remove_weights = FALSE)
Arguments
teststat
a character specifying the type of the test statistic
to be applied.
testtype
a character specifying how to compute the distribution of
the test statistic.
mincriterion
the value of the test statistic (for testtype == "Teststatistic"
),
or 1 - p-value (for other values of testtype
) that
must be exceeded in order to implement a split.
minsplit
the minimum sum of weights in a node in order to be considered
for splitting.
minbucket
the minimum sum of weights in a terminal node.
stump
a logical determining whether a stump (a tree with three
nodes only) is to be computed.
nresample
number of Monte-Carlo replications to use when the
distribution of the test statistic is simulated.
maxsurrogate
number of surrogate splits to evaluate. Note the
currently only surrogate splits in ordered
covariables are implemented.
mtry
number of input variables randomly sampled as candidates
at each node for random forest like algorithms. The default
mtry = 0
means that no random selection takes place.
savesplitstats
a logical determining if the process of standardized
two-sample statistics for split point estimate
is saved for each primary split.
maxdepth
maximum depth of the tree. The default maxdepth = 0
means that no restrictions are applied to tree sizes.
remove_weights
a logical determining if weights attached to nodes shall be removed
after fitting the tree.