select_RF

A data.frame.
Each column corresponds to a feature vectors.
Could include additional covariates not a part of
the original modules.

A number between 0 and 1. Percentage of features
dropped at each iteration.

drop_fraction

Number of features selected by fuzzyforest.

number_selected

In the case of regression, <code>mtry</code> is set to
<code>ceiling</code>(\(\sqrt(p)\)*<code>mtry_factor</code>).
In the case of classification, <code>mtry</code> is set to
<code>ceiling</code>((p/3)*<code>mtry_factor</code>). If either
of these numbers is greater than p, <code>mtry</code> is
set to p.

mtry_factor

A number greater than 1. <code>ntree</code> for each
random is <code>ntree_factor</code> times the number
of features. For each random forest, <code>ntree</code>
is set to <code>max</code>(<code>min_ntree</code>,
<code>ntree_factor</code>*<code>p</code>).

ntree_factor

Minimum number of trees grown in each random forest.

min_ntree

Number of processors used to fit random forests.

num_processors

nodesize

Carries out the selection step of fuzzyforest algorithm. Returns
data.frame with variable importances and top rated features.

Fuzzy forests, a new algorithm based on random forests,
is designed to reduce the bias seen in random forest feature selection
caused by the presence of correlated features. Fuzzy forests uses
recursive feature elimination random forests to select
features from separate blocks of correlated features where the
correlation within each block of features is high
and the correlation between blocks of features is low.
One final random forest is fit using the surviving features.
This package fits random forests using the 'randomForest' package and
allows for easy use of 'WGCNA' to split features into distinct blocks.
See D. Conn, Ngun, T., C. Ramirez, and G. Li (2019) <doi:10.18637/jss.v091.i09>
for further details.

select_RF: Carries out the selection step of fuzzyforest algorithm.

Description

Usage

Arguments

Value