Description Usage Arguments Details Value See Also Examples

Classification and regression trees based on the rpart package

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`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 = 0.00001) |

`nodes` |
Maximum size of tree in number of nodes to return. If equal to NA no pruning is done |

`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 connection (e.g., email or mailing) |

`margin` |
Margin on each customer 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") |

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

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

`summary.crtree`

to summarize results

`plot.crtree`

to plot results

`predict.crtree`

for prediction

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