select_control: Set Parameters for Selection Step of Fuzzy Forests
In OHDSI/FuzzyForest: Fuzzy Forests

Description Usage Arguments Value Note References Examples

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

1 2	select_control(drop_fraction = 0.25, number_selected = 5, mtry_factor = 1, min_ntree = 5000, ntree_factor = 10)

`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`).

An object of type selection_control.

This work was partially funded by NSF IIS 1251151.

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.

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)