spa_pls: Sub-window permutation analysis coupled with PLS (SwPA-PLS)

In plsVarSel: Variable Selection in Partial Least Squares

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

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

Arguments

y	vector of response values (numeric or factor).
X	numeric predictor matrix.
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

data(gasoline, package = "pls")
with( gasoline, spa_pls(octane, NIR) )

plsVarSel documentation built on May 29, 2024, 7:34 a.m.