Version 1.2-0 of the pls package is now available on CRAN.
The pls package implements partial least squares regression (PLSR) and
principal component regression (PCR). Features of the package include
- Several plsr algorithms: orthogonal scores, kernel pls and simpls
- Flexible cross-validation
- A formula interface, with traditional methods like predict, coef,
plot and summary
- Functions for extraction of scores and loadings, and calculation of
(R)MSEP and R2
- A simple multiplicative scatter correction (msc) implementation
- Functions for plotting predictions, validation statistics,
coefficients, scores, loadings, biplots and correlation loadings.
The main changes since 1.1-0 are
- predict() now handles missing data like the `lm' method does (the
default is to predict `NA').
- fitted() and residuals() now return NA for observations with missing
values, if na.action is na.exclude.
- `ncomp' is now reduced when it is too large for the requested
cross-validation.
- Line plot parameter arguments have been added to predplotXy(), so
one can control the properties of the target line in predplot().
- MSEP(), RMSEP(), loadings(), loadingplot() and scoreplot() are now
generic.
See the file CHANGES in the sources for all changes.
--
Ron Wehrens and Bj??rn-Helge Mevik
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