iterative_RF

A data.frame.
Each column corresponds to a feature vectors.

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

drop_fraction

A number between 0 and 1. Proportion features
from each module to retain at screening step.

keep_fraction

A positive number.  Mtry for each random forest
is set to
<code>ceiling</code>($\sqrt{p}$<code>mtry_factor</code>)
where <code>p</code> is the current number of features.

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

Fits iterative random forest algorithm.  Returns
data.frame with variable importances and top rated features.
For now this is an internal function that I've used to explore how
recursive feature elimination works in simulations.  It may be exported at
a later time.

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.

iterative_RF: Fits iterative random forest algorithm.

Description

Usage

Arguments

Value