LearningSample-class

  
    Objects of this class represent data for fitting tree-based models.


classes

LearningSample Class

A computational toolbox for recursive partitioning.
The core of the package is ctree(), an implementation of
conditional inference trees which embed tree-structured
regression models into a well defined theory of conditional
inference procedures. This non-parametric class of regression
trees is applicable to all kinds of regression problems, including
nominal, ordinal, numeric, censored as well as multivariate response
variables and arbitrary measurement scales of the covariates.
Based on conditional inference trees, cforest() provides an
implementation of Breiman's random forests. The function mob()
implements an algorithm for recursive partitioning based on
parametric models (e.g. linear models, GLMs or survival
regression) employing parameter instability tests for split
selection. Extensible functionality for visualizing tree-structured
regression models is available.

Torsten Hothorn

party

A Laboratory for Recursive Partytioning

LearningSample Class function

Objects can be created by calls of the form <code>new("LearningSample", ...)</code>.

Objects from the Class

<dl>
    <dt><code>responses</code>:</dt><dd>Object of class <code>"VariableFrame"</code> with the
                             response variables. </dd> <dt><code>inputs</code>:</dt><dd>Object of class <code>"VariableFrame"</code> with the
                          input variables.</dd> <dt><code>weights</code>:</dt><dd>Object of class <code>"numeric"</code>, a vector of
                           case counts or weights. </dd> <dt><code>nobs</code>:</dt><dd>Object of class <code>"integer"</code>, the number of
                        observations. </dd> <dt><code>ninputs</code>:</dt><dd>Object of class <code>"integer"</code>, the number of
                           input variables.</dd> </dl> 

Slots

No methods defined with class "LearningSample" in the signature.

LearningSample Class: Class "LearningSample"

Description

Arguments

Objects from the Class

Slots

Methods