stpls: Soft-Threshold PLS (ST-PLS)

In plsVarSel: Variable Selection in Partial Least Squares

Soft-Threshold PLS (ST-PLS)

Description

A soft-thresholding step in PLS algorithm (ST-PLS) based on ideas from the nearest shrunken centroid method.

Usage

stpls(..., method = c("stpls", "model.frame"))

Arguments

...	arguments for the underlying stpls.fit (see Details) and argumetns passed on to mvrV).
method	choice between the default stpls and alternative model.frame.

Details

The ST-PLS approach is more or less identical to the Sparse-PLS presented independently by Lè Cao et al. This implementation is an expansion of code from the pls package. Arguments for stpls.fit include ncomp and shrink, where the forme sets then number of components and the latter is the shrinkage parameter indicating how large proportion of the maximum absolute value of the loadings that should be subtracted from the loadings in the nearest shrunken centroid method.

Value

Returns an object of class mvrV, simliar to to mvr object of the pls package.

Author(s)

Solve Sæbø, Tahir Mehmood, Kristian Hovde Liland.

References

S. Sæbø, T. Almøy, J. Aarøe, A.H. Aastveit, ST-PLS: a multi-dimensional nearest shrunken centroid type classifier via pls, Journal of Chemometrics 20 (2007) 54-62.

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(yarn, package = "pls")
st <- stpls(density~NIR, ncomp=5, shrink=c(0.1,0.2), validation="CV", data=yarn)
summary(st)

plsVarSel documentation built on April 12, 2025, 1:19 a.m.