varimp.output

<p>a matrix containing decrease in predictive accuracy for
all variables for each tree</p>

varimp_matrix

<p>Values of variable 'm' in the oob cases are randomly permuted and R2
obtained through variable-m-permuted oob data is subtracted from R2
obtained on untouched oob data. The average of this number over all the
trees in the forest is the raw importance score for variable m.</p>

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

varimp.output: Variable importance matrix containing the decrease in predictive accuracy after permuting the variables across all trees

Description

Usage

Arguments

Value

References