SwPA-PLS provides the influence of each variable without considering the influence of the rest of the variables through sub-sampling of samples and variables.

1 2 | ```
spa_pls(y, X, ncomp = 10, N = 3, ratio = 0.8, Qv = 10,
SPA.threshold = 0.05)
``` |

`y` |
vector of response values ( |

`X` |
numeric predictor |

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

`N` |
number of Monte Carlo simulations (default = 3). |

`ratio` |
the proportion of the samples to use for calibration (default = 0.8). |

`Qv` |
integer number of variables to be sampled in each iteration (default = 10). |

`SPA.threshold` |
thresholding to remove non-important variables (default = 0.05). |

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

Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.

H. Li, M. Zeng, B. Tan, Y. Liang, Q. Xu, D. Cao, Recipe for revealing informative metabolites based on model population analysis, Metabolomics 6 (2010) 353-361. http://code.google.com/p/spa2010/downloads/list.

`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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