honest.rparttree {htetree}

Honest recursive partitioning Tree

Description

The recursive partitioning function, for R

Usage

honest.rparttree(
  formula,
  data,
  weights,
  subset,
  est_data,
  est_weights,
  na.action = na.rpart,
  method,
  model = FALSE,
  x = FALSE,
  y = TRUE,
  parms,
  control,
  cost,
  ...
)

Arguments

formula

a formula, with a response and features but no interaction terms. If this a a data frome, that is taken as the model frame (see model.frame).

data

an optional data frame that includes the variables named in the formula.

weights

optional case weights.

subset

optional expression saying that only a subset of the rows of the data should be used in the fit.

est_data

data frame to be used for leaf estimates; the estimation sample. Must contain the variables used in training the tree.

est_weights

optional case weights for estimation sample

na.action

the default action deletes all observations for which y is missing, but keeps those in which one or more predictors are missing.

method

one of "anova", "poisson", "class" or "exp". If method is missing then the routine tries to make an intelligent guess. If y is a survival object, then method = "exp" is assumed, if y has 2 columns then method = "poisson" is assumed, if y is a factor then method = "class" is assumed, otherwise method = "anova" is assumed. It is wisest to specify the method directly, especially as more criteria may added to the function in future.

Alternatively, method can be a list of functions named init, split and eval. Examples are given in the file ‘tests/usersplits.R’ in the sources, and in the vignettes ‘User Written Split Functions’.

model

model frame of causalTree, same as rpart

x

keep a copy of the x matrix in the result.

y

keep a copy of the dependent variable in the result. If missing and model is supplied this defaults to FALSE.

parms

optional parameters for the splitting function.
Anova splitting has no parameters.
Poisson splitting has a single parameter, the coefficient of variation of the prior distribution on the rates. The default value is 1.
Exponential splitting has the same parameter as Poisson.
For classification splitting, the list can contain any of: the vector of prior probabilities (component prior), the loss matrix (component loss) or the splitting index (component split). The priors must be positive and sum to 1. The loss matrix must have zeros on the diagonal and positive off-diagonal elements. The splitting index can be gini or information. The default priors are proportional to the data counts, the losses default to 1, and the split defaults to gini.

control

a list of options that control details of the rpart algorithm. See rpart.control.

cost

a vector of non-negative costs, one for each variable in the model. Defaults to one for all variables. These are scalings to be applied when considering splits, so the improvement on splitting on a variable is divided by its cost in deciding which split to choose.

...

arguments to rpart.control may also be specified in the call to causalTree. They are checked against the list of valid arguments. An example of a commonly set parameter would be xval, which sets the number of cross-validation samples. The parameter minsize is implemented differently in causalTree than in rpart; we require a minimum of minsize treated observations and a minimum of minsize control observations in each leaf.

Value

An object of class rpart after running an honest recursive partitioning tree. .