select_control: Set Parameters for Selection Step of Fuzzy Forests

Description Usage Arguments Value Note References Examples

View source: R/tuning_parameters.R

Description

Creates selection_control object for controlling how feature selection will be carried out after features from different modules have been combined.

Usage

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select_control(drop_fraction = 0.25, number_selected = 5, mtry_factor = 1,
  min_ntree = 5000, ntree_factor = 10)

Arguments

drop_fraction

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

number_selected

A positive number. Number of features that will be selected by fuzzyforests.

mtry_factor

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

min_ntree

Minimum number of trees grown in each random forest.

ntree_factor

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

Value

An object of type selection_control.

Note

This work was partially funded by NSF IIS 1251151.

References

Daniel Conn, Tuck Ngun, Christina M. Ramirez (2015). Fuzzy Forests: a New WGCNA Based Random Forest Algorithm for Correlated, High-Dimensional Data, Journal of Statistical Software, Manuscript in progress.

Examples

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drop_fraction <- .25
number_selected <- 10
mtry_factor <- 1
min_ntree <- 5000
ntree_factor <- 5
select_params <- select_control(drop_fraction=drop_fraction,
                                number_selected=number_selected,
                                mtry_factor=mtry_factor,
                                min_ntree=min_ntree,
                                ntree_factor=ntree_factor)

OHDSI/FuzzyForest documentation built on May 9, 2017, 3:26 p.m.