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)
testtype == "Teststatistic"
),
or 1 - p-value (for other values of testtype
) that
must be exceeded in order to implement a split.mtry = 0
means that no random selection takes place.maxdepth = 0
means that no restrictions are applied to tree sizes.TreeControl
.teststat
, testtype
and mincriterion
determine how the global null hypothesis of independence between all input
variables and the response is tested (see ctree
). The
argument nresample
is the number of Monte-Carlo replications to be
used when testtype = "MonteCarlo"
. A split is established when the sum of the weights in both daugther nodes
is larger than minsplit
, this avoids pathological splits at the
borders. When stump = TRUE
, a tree with at most two terminal nodes
is computed.
The argument mtry > 0
means that a random forest like `variable
selection', i.e., a random selection of mtry
input variables, is
performed in each node.
It might be informative to look at scatterplots of input variables against
the standardized two-sample split statistics, those are available when
savesplitstats = TRUE
. Each node is then associated with a vector
whose length is determined by the number of observations in the learning
sample and thus much more memory is required.