fit-methods

fit,StatModel,LearningSample-method

Fit a `StatModel' model to objects of class `LearningSample'.

methods

Fit Methods

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. The methods are described in
Hothorn et al. (2006) <doi:10.1198/106186006X133933>,
Zeileis et al. (2008) <doi:10.1198/106186008X319331> and
Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>.

Torsten Hothorn

party

A Laboratory for Recursive Partytioning

Kurt Hornik

Carolin Strobl

Achim Zeileis

Fit Methods function

<dl>
 <dt>fit</dt>
<dd><code>signature(model = "StatModel", data = "LearningSample")</code>:
 fit <code>model</code> to <code>data</code>.</dd> 
</dl>

Methods

Fit a `StatModel' model to objects of class `LearningSample'.

Fit `StatModel' Objects to Data — Fit Methods

<dl>
 <dt>fit</dt>
<dd><code>signature(model = "StatModel", data = "LearningSample")</code>:
 fit <code>model</code> to <code>data</code>.</dd>

 
</dl>

Fit Methods: Fit `StatModel' Objects to Data

Description

Arguments

Methods