plsca {plspm}R Documentation

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 predictands)

Usage

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

Arguments

X A numeric matrix or data frame (X-block).
Y A numeric matrix or data frame (Y-block).
nc The number of extracted PLS components (NULL by default)
scaled A logical value indicating whether scaling data should be performed (TRUE by default).

Details

Arguments X and Y must contain more than one variable.

No missing data are allowed.

When nc=NULL the number of components is determined by taking the minimum between the number of columns from X and Y.

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 referred to 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 referred to 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

print.plsca,plot.plsca

Examples

  ## Not run: 
  ## example of PLSCA with the vehicles dataset
  data(vehicles)
  can <- plsca(vehicles[,1:12], vehicles[,13:16])
  can
  plot(can)
  
## End(Not run)

[Package plspm version 0.1-4 Index]