simpls.fit.RdFits a PLSR model with the SIMPLS algorithm.
simpls.fit(X, Y, ncomp, center = TRUE, stripped = FALSE, ...)
| X | a matrix of observations. |
|---|---|
| Y | a vector or matrix of responses. |
| ncomp | the number of components to be used in the modelling. |
| center | logical, determines if the \(X\) and \(Y\) matrices are mean centered or not. Default is to perform mean centering. |
| stripped | logical. If |
| ... | other arguments. Currently ignored. |
This function should not be called directly, but through
the generic functions plsr or mvr with the argument
method="simpls". SIMPLS is much faster than the NIPALS algorithm,
especially when the number of X variables increases, but gives
slightly different results in the case of multivariate Y. SIMPLS truly
maximises the covariance criterion. According to de Jong, the standard
PLS2 algorithms lie closer to ordinary least-squares regression where
a precise fit is sought; SIMPLS lies closer to PCR with stable
predictions.
A list containing the following components is returned:
an array of regression coefficients for 1, ...,
ncomp components. The dimensions of coefficients are
c(nvar, npred, ncomp) with nvar the number
of X variables and npred the number of variables to be
predicted in Y.
a matrix of scores.
a matrix of loadings.
a matrix of Y-scores.
a matrix of Y-loadings.
the projection matrix used to convert X to scores.
a vector of means of the X variables.
a vector of means of the Y variables.
an array of fitted values. The dimensions of
fitted.values are c(nobj, npred, ncomp) with
nobj the number samples and npred the number of
Y variables.
an array of regression residuals. It has the same
dimensions as fitted.values.
a vector with the amount of X-variance explained by each component.
Total variance in X.
de Jong, S. (1993) SIMPLS: an alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 18, 251--263.