simplsca: SIMPLS-CA: SIMPLS Canonical Analysis

In plsdepot: Partial Least Squares (PLS) Data Analysis Methods

SIMPLS-CA: SIMPLS Canonical Analysis

Description

The function simplsca performs the SIMPLS Canonical Analysis algorithm as described in Michel Tenenhaus book La Regression PLS, chapter 5.

Usage

  simplsca(X, Y, comps = 2)

Arguments

X	Numeric matrix or data frame with two or more columns (X-block).
Y	Numeric matrix or data frame with two or more columns (Y-block).
comps	Number of components to be extracted. (TRUE by default).

Details

No missing data are allowed.

Value

An object of class "simplsca", basically a list with the following elements:

x.scores	scores of the X-block (also known as T components)
x.wgs	weights of the X-block
y.scores	scores of the Y-block (also known as U components)
y.wgs	weights of the Y-block
cor.xt	correlations between X and T
cor.yu	correlations between Y and U
cor.xu	correlations between X and U
cor.yt	correlations between Y and T
cor.tu	correlations between T and U
R2XT	explained variance of X by T
R2YT	explained variance of Y by T
R2YU	explained variance of Y by U
R2XU	explained variance of X by U

Author(s)

Gaston Sanchez

References

Tenenhaus, M. (1998) La Regression PLS. Theorie et Pratique. Paris: Editions TECHNIP.

See Also

plot.simplsca, simpls

Examples

## Not run: 
 # load data linnerud
 data(linnerud)

 # apply inter-battery method
 my_simca = simplsca(linnerud[,1:3], linnerud[,4:6])

 # plot variables
 plot(my_simca, what="variables")
 
## End(Not run)

plsdepot documentation built on April 1, 2023, 12:04 a.m.