truncation: Trunction PLS

In plsVarSel: Variable Selection in Partial Least Squares

Trunction PLS

Description

Distribution based truncation for variable selection in subspace methods for multivariate regression.

Usage

truncation(..., Y.add, weights, method = "truncation")

Arguments

...	arguments passed on to mvrV).
Y.add	optional additional response vector/matrix found in the input data.
weights	optional object weighting vector.
method	choice (default = truncation).

Details

Loading weights are truncated around their median based on confidence intervals for modelling without replicates (Lenth et al.). The arguments passed to mvrV include all possible arguments to cppls and the following truncation parameters (with defaults) trunc.pow=FALSE, truncation=NULL, trunc.width=NULL, trunc.weight=0, reorth=FALSE, symmetric=FALSE.

The default way of performing truncation involves the following parameter values: truncation="Lenth", trunc.width=0.95, indicating Lenth's confidence intervals (assymmetric), with a confidence of 95 shrinkage instead of a hard threshold. An alternative truncation strategy can be used with: truncation="quantile", in which a quantile line is used for detecting outliers/inliers.

Value

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

Author(s)

Kristian Hovde Liland.

References

K.H. Liland, M. Høy, H. Martens, S. Sæbø: Distribution based truncation for variable selection in subspace methods for multivariate regression, Chemometrics and Intelligent Laboratory Systems 122 (2013) 103-111.

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")
tr <- truncation(density ~ NIR, ncomp=5, data=yarn, validation="CV",
 truncation="Lenth", trunc.width=0.95) # Default truncation
summary(tr)

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