| plsca | R Documentation | 
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)
plsca(X, Y, comps = NULL, scaled = TRUE)
| 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
( | 
| scaled | A logical value indicating whether scaling
data should be performed ( | 
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 | 
Gaston Sanchez
Tenenhaus, M. (1998) La Regression PLS. Theorie et Pratique. Editions TECHNIP, Paris.
plot.plsca
## 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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