sbfControl: Control Object for Selection By Filtering (SBF)

Description

Controls the execution of models with simple filters for feature selection

Usage

sbfControl(functions = NULL, 
           method = "boot", 
           saveDetails = FALSE, 
           number = ifelse(method %in% c("cv", "repeatedcv"), 10, 25),
           repeats = ifelse(method %in% c("cv", "repeatedcv"), 1, number),
           verbose = TRUE, 
           returnResamp = "all", 
           p = 0.75, 
           index = NULL, 
           workers = 1, 
           computeFunction = lapply, 
           computeArgs = NULL)

Arguments

functions

a list of functions for model fitting, prediction and variable filtering (see Details below)

method

The external resampling method: boot, cv, LOOCV or LGOCV (for repeated training/test splits

number

Either the number of folds or number of resampling iterations

repeats

For repeated k-fold cross-validation only: the number of complete sets of folds to compute

saveDetails

a logical to save the predictions and variable importances from the selection process

verbose

a logical to print a log for each external resampling iteration

returnResamp

A character string indicating how much of the resampled summary metrics should be saved. Values can be ``all'' or ``none''

For leave-group out cross-validation: the training percentage

index

a list with elements for each external resampling iteration. Each list element is the sample rows used for training at that iteration.

workers

an integer that specifies how many machines/processors will be used

computeFunction

a function that is lapply or emulates lapply. It must have arguments X, FUN and .... computeFunction can be used to build models in parall

computeArgs

Extra arguments to pass into the ... slore in computeFunction. See the examples in sbf.

Value

a list that echos the specified arguments

code

nbSBF

itemize

score

item

Details

Simple filter-based feature selection requires function to be specified for some operations.

The fit function builds the model based on the current data set. The arguments for the function must be:

{ the current training set of predictor data with the appropriate subset of variables (i.e. after filtering)} y{ the current outcome data (either a numeric or factor vector)} ...{ optional arguments to pass to the fit function in the call to sbf}