varimp.output-class

Functions to implements random forest method for model based
recursive partitioning. The mob() function, developed by Zeileis et al. (2008),
within 'party' package, is modified to construct model-based decision trees based
on random forests methodology. The main input function mobforest.analysis() takes
all input parameters to construct trees, compute out-of-bag errors, predictions,
and overall accuracy of forest. The algorithm performs parallel computation
using cluster functions within 'parallel' package.

Kasey Jones

mobForest

Model Based Random Forest Analysis

varimp.output-class function

<p>Objects can be created by calls of the
form <code><a rd-options="" href="/link/varimp.output?package=mobForest&version=1.3.1" data-mini-rdoc="mobForest::varimp.output">varimp.output</a></code>. </p>

Objects from the Class

Class <code>"varimp.output"</code> of mobforest model — varimp.output-class

<p>Objects can be created by calls of the
form <code><a rd-options='' href='varimp.output'>varimp.output</a></code>. </p>

varimp.output-class: Class `"varimp.output"` of mobforest model

Description

Arguments

Objects from the Class

References

See Also