plsca {plsdepot}

PLS-CA: Partial Least Squares Canonical Analysis

Description

Performs partial least squares canonical analysis for two blocks of data. Compared to PLSR2, the blocks of variables in PLS-CA play a symmetric role (i.e. there is neither predictors nor responses)

Usage

  plsca(X, Y, comps = NULL, scaled = TRUE)

Arguments

X

A numeric matrix or data frame (X-block) with more than one variable. No missing data are allowed

Y

A numeric matrix or data frame (Y-block) with more than one variable. No missing data are allowed

comps

The number of extracted PLS components (NULL by default) When comps=NULL the number of components is determined by taking the minimum between the number of columns from X and Y.

scaled

A logical value indicating whether scaling data should be performed (TRUE by default). #'When scaled=TRUE the data is scaled to standardized values (mean=0, variance=1). Otherwise the data will only be centered (mean=0).

Value

An object of class "plsca", 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

x.loads

loadings of the X-block

y.scores

scores of the Y-block (also known as U components)

y.wgs

weights of the Y-block

y.loads

loadings of the Y-block

cor.xt

correlations between X and T

cor.yu

correlations between Y and U

cor.tu

correlations between T and U

cor.xu

correlations between X and U

cor.yt

correlations between Y and T

R2X

explained variance of X by T

R2Y

explained variance of Y by U

com.xu

communality of X with U

com.yt

communality of Y with T

Author(s)

Gaston Sanchez

References

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

See Also

plot.plsca

Examples

## Not run: 
 ## example of PLSCA with the vehicles dataset
 data(vehicles)

 # apply plsca
 my_plsca = plsca(vehicles[,1:12], vehicles[,13:16])
 my_plsca

 # plot variables
 plot(my_plsca)
 
## End(Not run)