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

A backward variable elimination procedure for elimination of non informative variables.

1 | ```
bve_pls(y, X, ncomp = 10, ratio = 0.75, VIP.threshold = 1)
``` |

`y` |
vector of response values ( |

`X` |
numeric predictor |

`ncomp` |
integer number of components (default = 10). |

`ratio` |
the proportion of the samples to use for calibration (default = 0.75). |

`VIP.threshold` |
thresholding to remove non-important variables (default = 1). |

Variables are first sorted with respect to some importancemeasure, and usually one of the filter measures described above are used. Secondly, a threshold is used to eliminate a subset of the least informative variables. Then a model is fitted again to the remaining variables and performance is measured. The procedure is repeated until maximum model performance is achieved.

Returns a vector of variable numbers corresponding to the model having lowest prediction error.

Tahir Mehmood, Kristian Hovde Liland, Solve S<c3><a6>b<c3><b8>.

I. Frank, Intermediate least squares regression method, Chemometrics and Intelligent Laboratory Systems 1 (3) (1987) 233-242.

`VIP`

(SR/sMC/LW/RC), `filterPLSR`

, `shaving`

,
`stpls`

, `truncation`

,
`bve_pls`

, `ga_pls`

, `ipw_pls`

, `mcuve_pls`

,
`rep_pls`

, `spa_pls`

,
`lda_from_pls`

, `lda_from_pls_cv`

, `setDA`

.

1 2 |

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