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)
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
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).
Author
Gaston Sanchez
References
Tenenhaus, M. (1998) La Regression PLS. Theorie et
Pratique. Editions TECHNIP, Paris.
if (FALSE) {
## 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)
}