wrtpls.fit: Weight Randomization Test PLS

In mvdalab: Multivariate Data Analysis Laboratory

Weight Randomization Test PLS

Description

Weight Randomization Test algorithm for PLS1

Usage

wrtpls.fit(X, Y, ncomp, perms, alpha, ...)

Arguments

X	a matrix of observations. NAs and Infs are not allowed.
Y	a vector. NAs and Infs are not allowed.
ncomp	the number of components to include in the model (see below).
alpha	the significance level for wrtpls
perms	the number of permutations to run for wrtpls
...	additional arguments. Currently ignored.

Details

This function should not be called directly, but through plsFit with the argument method="wrtpls". It implements the Bidiag2 scores algorithm with a permutation test for selecting the statistically significant components.

Value

An object of class mvdareg is returned. The object contains all components returned by the underlying fit function. In addition, it contains the following:

loadings	X loadings
weights	weights
D2	bidiag2 matrix
iD2	inverse of bidiag2 matrix
Ymean	mean of reponse variable
Xmeans	mean of predictor variables
coefficients	regression coefficients
y.loadings	y-loadings
scores	X scores
R	orthogonal weights
Y	scaled response values
Yactual	actual response values
fitted	fitted values
residuals	residuals
Xdata	X matrix
iPreds	predicted values
y.loadings2	scaled y-loadings
wrtpls	permutations effected
wrtpls.out.Sig	Significant LVs
wrtpls.crit	weight critical values
actual.normwobs	normed weights
fit.time	model fitting time
val.method	validation method
ncomp	number of latent variables
perms	number of permutations performed
alpha	permutation alpha value
method	PLS algorithm
scale	scaling used
scaled	was scaling performed
call	model call
terms	model terms
mm	model matrix
model	fitted model

Author(s)

Nelson Lee Afanador ([e-mail]), Thanh Tran ([e-mail])

References

Indahl, Ulf G., (2014) The geometry of PLS1 explained properly: 10 key notes on mathematical properties of and some alternative algorithmic approaches to PLS1 modeling. Journal of Chemometrics, 28, 168:180.

Manne R., Analysis of two partial-least-squares algorithms for multi-variate calibration. Chemom. Intell. Lab. Syst. 1987; 2: 187:197.

Thanh Tran, Ewa Szymanska, Jan Gerretzen, Lutgarde Buydens, Nelson Lee Afanador, Lionel Blanchet, Weight Randomization Test for the Selection of the Number of Components in PLS Models. Chemom. Intell. Lab. Syst., accepted for publication - Jan 2017.

See Also

plsFit

mvdalab documentation built on Oct. 6, 2022, 1:05 a.m.