supplementaryObservations4PLSC: compute the latent variables for supplementary observations...

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

compute the latent variables for supplementary observations for a PLSC model computed with tepPLS.

Description

supplementaryObservations4PLSC: computes the latent variables for supplementary observations for a PLSC model computed with tepPLS.

Usage

supplementaryObservations4PLSC(
  resPLSC,
  Xsup = NULL,
  Ysup = NULL,
  dimNames = "Dimension "
)

Arguments

resPLSC	the results of a PLSC analysis from tepPLS.
Xsup	an Nsup by I matrix of supplementary observations matching the X matrix (see description for details). When NULL (Default) nothing is computed for Xsup.
Ysup	an Nsup by J matrix of supplementary observations matching the Y matrix (see description for details). When NULL (Default) nothing is computed for Ysup.
dimNames	Names for the dimensions (i.e., factors) for the supplementary loadings (Default: 'Dimension ').

Details

The original analysis is performed with tepPLS on the original data matrices X (N by I) and Y (N by J). The supplementary data matrices should have I columns for Xsup and J columns for Ysup.

Value

A list with lx.sup and ly.sup giving the latent variables values of the supplementary observations for (respectively) X and Y.

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