```
anovaScores(x, y)
gamScores(x, y)
caretSBF
lmSBF
rfSBF
treebagSBF
ldaSBF
nbSBF
```

x

a matrix or data frame of numeric predictors

y

a numeric or factor vector of outcomes

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.

`anovaScores`

and `gamScores`

are two examples of univariate filtering functions. `anovaScores`

fits a simple linear model between a single feature and the outcome, then the p-value for the whole model F-test is returned. `gamScores`

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 is returned.

If a particular model fails for `lm`

or `gam`

, a p-value of 1 is returned.

`sbfControl`

, `sbf`

, `summary.gam`