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fuzzyforest (version 1.0.0)

Fuzzy Forests

Description

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.

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Version

Install

install.packages('fuzzyforest')

Monthly Downloads

191

Version

1.0.0

License

GPL-3

Maintainer

Daniel Conn

Last Published

February 18th, 2016

Functions in fuzzyforest (1.0.0)

predict.fuzzy_forest

Predict method for fuzzy_forest object. Obtains predictions from fuzzy forest algorithm.
fuzzy_forest

Fuzzy Forest Object
example_ff

Fuzzy Forest Example
print.fuzzy_forest

Print fuzzy_forest object. Prints output from fuzzy forests algorithm.
ff

Fits fuzzy forest algorithm.
Liver_Expr

Liver Expression Data from Female Mice
modplot

Plots relative importance of modules.
screen_control

Set Parameters for Screening Step of Fuzzy Forests
wff

Fits WGCNA based fuzzy forest algorithm.
ctg

Cardiotocography Data Set
fuzzyforest

fuzzyforest: an implementation of the fuzzy forest algorithm in R.
WGCNA_control

Set Parameters for WGCNA Step of Fuzzy Forests
iterative_RF

Fits iterative random forest algorithm.
select_control

Set Parameters for Selection Step of Fuzzy Forests
select_RF

Carries out the selection step of fuzzyforest algorithm.