View source: R/selectByFilter.R

sbfControl | R Documentation |

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, indexOut = NULL, timingSamps = 0, seeds = NA, allowParallel = TRUE, multivariate = FALSE )

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

`method` |
The external resampling method: |

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

`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 |

`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 |

`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 |

`allowParallel` |
if a parallel backend is loaded and available, should the function use it? |

`multivariate` |
a logical; should all the columns of |

More details on this function can be found at http://topepo.github.io/caret/feature-selection-using-univariate-filters.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 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.

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 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)

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