sbfControl

From caret v6.0-70 by Max Kuhn

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

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 ``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.
indexOut: a list (the same length as index) that dictates which sample are held-out for each resample. If NULL, then the unique set of samples not contained in index is used.
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?
multivariate: a logical; should all the columns of x be exposed to the score function at once?

Details

More details on this function can be found at http://topepo.github.io/caret/featureselection.html#filter.

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 scores with names for each predictor (such as a p-value). Inputs are:

x the predictors for the training samples. If sbfControl()$multivariate is TRUE, this will be the full predictor matrix. Otherwise it is a vector for a specific predictor.
y the current training outcomes

When sbfControl()$multivariate is TRUE, the score function should return a named vector where length(scores) == ncol(x). Otherwise, the function's output should be a single value. Univariate examples are give by anovaScores for classification and gamScores for regression and the example below.

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://topepo.github.io/caret/ has more details and examples related to this function.

Value

a list that echos the specified arguments

Examples

## Not run: 
# data(BloodBrain)
# 
# ## Use a GAM is the filter, then fit a random forest model
# set.seed(1)
# RFwithGAM <- sbf(bbbDescr, logBBB,
#                  sbfControl = sbfControl(functions = rfSBF,
#                                          verbose = FALSE, 
#                                          seeds = sample.int(100000, 11),
#                                          method = "cv"))
# RFwithGAM
# 
# 
# ## A simple example for multivariate scoring
# rfSBF2 <- rfSBF
# rfSBF2$score <- function(x, y) apply(x, 2, rfSBF$score, y = y)
# 
# set.seed(1)
# RFwithGAM2 <- sbf(bbbDescr, logBBB,
#                   sbfControl = sbfControl(functions = rfSBF2,
#                                           verbose = FALSE, 
#                                           seeds = sample.int(100000, 11),
#                                           method = "cv",
#                                           multivariate = TRUE))
# RFwithGAM2
# 
# 
# ## End(Not run)

Documentation reproduced from package caret, version 6.0-70, License: GPL (>= 2)

Community examples

Looks like there are no examples yet.