filterPLSR | R Documentation |

Extract the index of influential variables based on threshold defiend for LW (loading weights), RC (regression coef), JT (jackknife testing) and VIP (variable importance on projection).

filterPLSR( y, X, ncomp = 10, ncomp.opt = c("minimum", "same"), validation = "LOO", LW.threshold = NULL, RC.threshold = NULL, URC.threshold = NULL, FRC.threshold = NULL, JT.threshold = NULL, VIP.threshold = NULL, SR.threshold = NULL, sMC.threshold = NULL, mRMR.threshold = NULL, WVC.threshold = NULL, ... )

`y` |
vector of response values ( |

`X` |
numeric predictor |

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

`ncomp.opt` |
use the number of components corresponding to minimum error (minimum)
or |

`validation` |
type of validation in the PLS modelling (default = "LOO"). |

`LW.threshold` |
threshold for Loading Weights if applied (default = NULL). |

`RC.threshold` |
threshold for Regression Coefficients if applied (default = NULL). |

`URC.threshold` |
threshold for Unit normalized Regression Coefficients if applied (default = NULL). |

`FRC.threshold` |
threshold for Fitness normalized Regression Coefficients if applied (default = NULL). |

`JT.threshold` |
threshold for Jackknife Testing if applied (default = NULL). |

`VIP.threshold` |
threshold for Variable Importance on Projections if applied (default = NULL). |

`SR.threshold` |
threshold for Selectivity Ration if applied (default = NULL). |

`sMC.threshold` |
threshold for Significance Multivariate Correlation if applied (default = NULL). |

`mRMR.threshold` |
threshold for minimum Redundancy Maximum Releveance if applied (default = NULL). |

`WVC.threshold` |
threshold for Weighted Variable Contribution if applied (default = NULL). |

`...` |
additional paramters for |

Filter methods are applied for variable selection with PLSR. This function can return selected variables and Root Mean Squared Error of Cross-Validation for various filter methods and determine optimum numbers of components.

Returns a list of lists containing filters (outer list), their selected variables, optimal numbers of components and prediction accuracies.

Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.

T. Mehmood, K.H. Liland, L. Snipen, S. Sæbø, A review of variable selection methods in Partial Least Squares Regression, Chemometrics and Intelligent Laboratory Systems 118 (2012) 62-69.

`VIP`

(SR/sMC/LW/RC/URC/FRC/mRMR), `filterPLSR`

, `spa_pls`

,
`stpls`

, `truncation`

, `bve_pls`

, `mcuve_pls`

,
`ipw_pls`

, `ga_pls`

, `rep_pls`

, `WVC_pls`

, `T2_pls`

.

data(gasoline, package = "pls") ## Not run: with( gasoline, filterPLSR(octane, NIR, ncomp = 10, "minimum", validation = "LOO", RC.threshold = c(0.1,0.5), SR.threshold = 0.5)) ## End(Not run)

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