sbfControl: Control Object for Selection By Filtering (SBF)
In caret: Classification and Regression Training

Description Usage Arguments Details Value Author(s) See Also Examples

Controls the execution of models with simple filters for feature selection

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 = FALSE, 
           returnResamp = "final", 
           p = 0.75, 
           index = NULL, 
           timingSamps = 0,
           seeds = NA,
           allowParallel = TRUE)

`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 “final” 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.
`timingSamps`	the number of training set samples that will be used to measure the time for predicting samples (zero indicates that the prediction time should not be estimated).
`seeds`	an optional set of integers that will be used to set the seed at each resampling iteration. This is useful when the models are run in parallel. A value of `NA` will stop the seed from being set within the worker processes while a value of `NULL` will set the seeds using a random set of integers. Alternatively, a vector of integers can be used. The vector should have `B+1` elements where `B` is the number of resamples. See the Examples section below.
`allowParallel`	if a parallel backend is loaded and available, should the function use it?

More details on this function can be found at http://caret.r-forge.r-project.org/featureselection.html.

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

The function should return a model object that can be used to generate predictions.

The pred function returns a vector of predictions (numeric or factors) from the current model. The arguments are:

object the model generated by the fit function
x the current set of predictor set for the held-back samples

The score function is used to return a vector of scores with names for each predictor (such as a p-value). Inputs are:

x the predictors for the training samples
y the current training outcomes

The function should return a vector, as previously stated. Examples are give by anovaScores for classification and gamScores for regression.

The filter function is used to return a logical vector with names for each predictor (TRUE indicates that the prediction should be retained). Inputs are:

score the output of the score function
x the predictors for the training samples
y the current training outcomes

The function should return a named logical vector.

Examples of these functions are included in the package: caretSBF, lmSBF, rfSBF, treebagSBF, ldaSBF and nbSBF.

The web page http://caret.r-forge.r-project.org/ has more details and examples related to this function.

a list that echos the specified arguments

Max Kuhn

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

## Not run: 
data(BloodBrain)

## Use a GAM is the filter, then fit a random forest model
RFwithGAM <- sbf(bbbDescr, logBBB,
                 sbfControl = sbfControl(functions = rfSBF,
                                         verbose = FALSE, 
                                         seeds = sample.int(100000, 11),
                                         method = "cv"))
RFwithGAM

## End(Not run)