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 Sæbø.

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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