# caretSBF

##### Selection By Filtering (SBF) Helper Functions

Ancillary functions for univariate feature selection

- Keywords
- models

##### Usage

```
anovaFilter(x, y, cut = 0.05)
gamFilter(x, y, cut = 0.05)
```caretSBF
lmSBF
rfSBF
treebagSBF
ldaSBF
nbSBF

##### Arguments

- x
- a matrix or data frame of numeric predictors
- y
- a numeric or factor vector of outcomes
- cut
- a p-value cut-off

##### Details

This page documents the functions that are used in selection by filtering (SBF). The functions described here are passed to the algorithm via the
`functions`

argument of `sbfControl`

.

See `sbfControl`

for details on how these functions should be defined.

`anovaFilter`

and `gamFilter`

are two examples of univariate filtering functions. `anovaFilter`

fits a simple linear model between a single feature and the outcome, then the p-value for the whole model F-test is generated. If the p-values is greater than 0.05, the feature is retained for the model. `gamFilter`

fits a generalized additive model between a single predictor and the outcome using a smoothing spline basis function. A p-value is generated using the whole model test from `summary.gam`

and p-values greater than 0.05 indicate that a predictor will be excluded.

If a particular model fails for `lm`

or `gam`

, the predictor is not used in the model.

##### See Also

*Documentation reproduced from package caret, version 4.34, License: GPL-2*