# sbfControl

0th

Percentile

##### Control Object for Selection By Filtering (SBF)

Controls the execution of models with simple filters for feature selection

Keywords
utilities
##### 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''
p
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.
##### 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:

• x
{ 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}

##### Value

• a list that echos the specified arguments

##### code

nbSBF

##### itemize

• score

##### item

• x
• y
• x
• y

sbf, caretSBF, lmSBF, rfSBF, treebagSBF, ldaSBF and nbSBF