R: Data Preprocessing for Extensible Trees.

extree_data {partykit} | R Documentation

## Data Preprocessing for Extensible Trees.

### Description

A routine for preprocessing data before an extensible tree can be grown by `extree_fit`.

### Usage

    extree_data(formula, data, subset, na.action = na.pass, weights, offset,
                cluster, strata, scores = NULL, yx = c("none", "matrix"),
                ytype = c("vector", "data.frame", "matrix"),
                nmax = c(yx = Inf, z = Inf), ...)

### Arguments

`formula` | a formula describing the model of the form `y1 + y2 + ... ~ x1 + x2 + ... | z1 + z2 + ...`.
`data` | an optional data.frame containing the variables in the model.
`subset` | an optional vector specifying a subset of observations to be used in the fitting process.
`na.action` | a function which indicates what should happen when the data contain missing values.
`weights` | an optional vector of weights.
`offset` | an optional offset vector.
`cluster` | an optional factor describing clusters. The interpretation depends on the specific tree algorithm.
`strata` | an optional factor describing strata. The interpretation depends on the specific tree algorithm.
`scores` | an optional named list of numeric scores to be assigned to ordered factors in the `z` part of the formula.
`yx` | a character indicating if design matrices shall be computed.
`ytype` | a character indicating how response variables shall be stored.
`nmax` | a numeric vector of length two with the maximal number of bins in the response and `x`-part (first element) and the `z` part. Use `Inf` to switch-off binning.
`...` | additional arguments.

### Details

This internal functionality will be the basis of implementations of other tree algorithms in future versions. Currently, only `ctree` relies on this function.

### Value

An object of class `extree_data`.

### Examples

      data("iris")

      ed <- extree_data(Species ~ Sepal.Width + Sepal.Length | Petal.Width + Petal.Length,
                        data = iris, nmax = c("yx" = 25, "z" = 10), yx = "matrix")

      ### the model.frame
      mf <- model.frame(ed)
      all.equal(mf, iris[, names(mf)])

      ### binned y ~ x part
      model.frame(ed, yxonly = TRUE)

      ### binned Petal.Width
      ed[[4, type = "index"]]

      ### response
      ed$yx$y

      ### model matrix
      ed$yx$x

[Package partykit version 1.2-7 Index]