plsca {plspm} | 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 predictands)
plsca(X, Y, nc = NULL, scaled = TRUE)
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). |
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).
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. |
Gaston Sanchez
Tenenhaus, M. (1998) La Regression PLS. Theorie et Pratique. Editions TECHNIP, Paris.
## 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)