plsdepot (version 0.1.17)

plsca: 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

References

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

See Also

plot.plsca

Examples

Run this code
## 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)

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