PLS4jack: in PLS regression (PLSR) compute a supplementary projection...

In HerveAbdi/data4PCCAR: Some data sets and R-functions for PCA and CA to accompany Abdi & Beaton (to appear, 2023) Principal Component and Correspondence Analysis with R

in PLS regression (PLSR) compute a supplementary projection for a jackknifed estimation of one supplementary element. The prediction is performed for 1 to nfactor latent variables.

Description

in PLS regression (PLSR),PLS4jack: computes a supplementary projection for a jackknifed estimation of one supplementary element. The prediction is computed for 1 to nfactor latent variables. PLS4jack is mainly used by PLSR_SVD for computing the random effect prediction of jackknifed observations in PLSR, but it can also be used to project supplementary observation in PLSR.

Usage

PLS4jack(X, Y, xsup, nfactor)

Arguments

X	the X matrix of predictors in the PLSR model.
Y	the Y matrix to be predicted by tge PLSR model.
xsup	the supplementary elements whose Y values are to be predicted.
nfactor	number of factors of the model.

Details

see Abdi (2010) for details and examples.

Value

Yhatsup the matrix of the predicted values.

Author(s)

Hervé Abdi, Lei Xuan #' @references (see also https://personal.utdallas.edu/~herve/)

1. Abdi, H. (2010). Partial least square regression, projection on latent structure regression, PLS-Regression. Wiley Interdisciplinary Reviews: Computational Statistics, 2, 97-106.

2. Abdi, H. (2007). Partial least square regression (PLS regression). In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. pp. 740-744.

3. Abdi. H. (2003). Partial least squares regression (PLS-regression). In M. Lewis-Beck, A. Bryman, T. Futing (Eds): Encyclopedia for Research Methods for the Social Sciences. Thousand Oaks (CA): Sage. pp. 792-795.

See Also

PLSR_SVD

HerveAbdi/data4PCCAR documentation built on Sept. 11, 2022, 4:19 p.m.