bidiagpls.fit: Bidiag2 PLS

In mvdalab: Multivariate Data Analysis Laboratory

Bidiag2 PLS

Description

Bidiagonalization algorithm for PLS1

Usage

bidiagpls.fit(X, Y, ncomp, ...)

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).
...	additional arguments. Currently ignored.

Details

This function should not be called directly, but through plsFit with the argument method="bidiagpls". It implements the Bidiag2 scores algorithm.

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
fit.time	model fitting time
val.method	validation method
ncomp	number of latent variables
contrasts	contrast matrix used
method	PLS algorithm used
scale	scaling used
validation	validation method
call	model call
terms	model terms
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.

See Also

plsFit

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