multi_class_lr

mod1_size

mod2_size

beta

<p>Function to generate multi-class data from a multinomial logistic
regression.  Assumes there are 5 classes.  Only supports two modules for now.
Currently this function is used for testing.</p>

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.

Daniel Conn

fuzzyforest

Fuzzy Forests

Tuck Ngun

Christina M. Ramirez

multi_class_lr function

Multinomial Logistic Regression — multi_class_lr

Multinomial Logistic Regression

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multi_class_lr: Multinomial Logistic Regression

Description

Usage

Arguments

Value