crtree: Classification and regression trees based on the rpart...
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crtree

R Documentation

Classification and regression trees based on the rpart package

Description

Classification and regression trees based on the rpart package

Usage

crtree(
  dataset,
  rvar,
  evar,
  type = "",
  lev = "",
  wts = "None",
  minsplit = 2,
  minbucket = round(minsplit/3),
  cp = 0.001,
  pcp = NA,
  nodes = NA,
  K = 10,
  seed = 1234,
  split = "gini",
  prior = NA,
  adjprob = TRUE,
  cost = NA,
  margin = NA,
  check = "",
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Arguments

`dataset`	Dataset
`rvar`	The response variable in the model
`evar`	Explanatory variables in the model
`type`	Model type (i.e., "classification" or "regression")
`lev`	The level in the response variable defined as _success_
`wts`	Weights to use in estimation
`minsplit`	The minimum number of observations that must exist in a node in order for a split to be attempted.
`minbucket`	the minimum number of observations in any terminal <leaf> node. If only one of minbucket or minsplit is specified, the code either sets minsplit to minbucket*3 or minbucket to minsplit/3, as appropriate.
`cp`	Minimum proportion of root node deviance required for split (default = 0.001)
`pcp`	Complexity parameter to use for pruning
`nodes`	Maximum size of tree in number of nodes to return
`K`	Number of folds use in cross-validation
`seed`	Random seed used for cross-validation
`split`	Splitting criterion to use (i.e., "gini" or "information")
`prior`	Adjust the initial probability for the selected level (e.g., set to .5 in unbalanced samples)
`adjprob`	Setting a prior will rescale the predicted probabilities. Set adjprob to TRUE to adjust the probabilities back to their original scale after estimation
`cost`	Cost for each treatment (e.g., mailing)
`margin`	Margin associated with a successful treatment (e.g., a purchase)
`check`	Optional estimation parameters (e.g., "standardize")
`data_filter`	Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000")
`arr`	Expression to arrange (sort) the data on (e.g., "color, desc(price)")
`rows`	Rows to select from the specified dataset
`envir`	Environment to extract data from

Details

See https://radiant-rstats.github.io/docs/model/crtree.html for an example in Radiant

Value

A list with all variables defined in crtree as an object of class tree

Examples

crtree(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>% summary()
result <- crtree(titanic, "survived", c("pclass", "sex")) %>% summary()
result <- crtree(diamonds, "price", c("carat", "clarity"), type = "regression") %>% str()

radiant.model documentation built on Oct. 16, 2023, 9:06 a.m.