# Control ctree Hyper Parameters

##### Control for Conditional Inference Trees

Various parameters that control aspects of the `ctree' fit.

- Keywords
- misc

##### 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.

##### Details

The arguments `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.

##### Value

An object of class `TreeControl`

.

*Documentation reproduced from package party, version 1.3-1, License: GPL-2*