bidiagpls.fit {mvdalab}

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