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

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/SPA.R

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

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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 S<c3><a6>b<c3><b8>.

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

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

plsVarSel documentation built on May 30, 2017, 2:05 a.m.

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