Description Usage Arguments Value Note References Examples

View source: R/tuning_parameters.R

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, |

`min_ntree` |
Minimum number of trees grown in each random forest. |

`ntree_factor` |
A number greater than 1. |

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.

1 2 3 4 5 6 7 8 9 10 | ```
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.

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