# Recursive Partitioning and Regression Trees Object

### Description

These are objects representing fitted `itree`

trees.

### Value

`frame` |
data frame with one row for each node in the tree.
The For classification problems, information about total counts (or weights,
if weights are unequal) appear in the Also included in the frame are |

`where` |
integer vector, the same length as the number of observations in the root node,
containing the row number of |

`splits` |
a numeric matrix describing the splits. The row label is the name of the split
variable, and columns are |

`csplit` |
this will be present only if one of the split variables is a factor. There
is one row for each such split, and column |

`method` |
the method used to grow the tree. |

`penalty` |
the penalty function for splitting on a specific variable at a specific node given the variables used in the branch leading to this node. |

`cptable` |
the table of optimal prunings based on a complexity parameter. |

`terms` |
an object of mode |

`call` |
an image of the call that produced the object, but with the arguments
all named and with the actual formula included as the formula argument.
To re-evaluate the call, say Optional components include the matrix of predictors ( |

### Structure

The following components must be included in a legitimate `itree`

object.
Of these, only the `where`

component has the same length as
the data used to fit the `itree`

object. The requirements here are the same
as those in rpart except itree objects have a `penalty`

parameter.

### See Also

`itree`

.