# Sub-window permutation analysis coupled with PLS (SwPA-PLS)

### Description

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.

### Usage

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

### Arguments

`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). |

### Value

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

### Author(s)

Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.

### References

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.

### See Also

`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`

.

### Examples

1 2 |