Version 2.1-0 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, wide 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
- Functions for plotting predictions, validation statistics,
coefficients, scores, loadings, and correlation loadings.
The main changes since 2.0-0 are
- Jackknife variance estimation of regression coefficients has been added.
- The `wide kernel' PLS algorithm has been implemented. It is faster than
the
other algorithms for very wide data.
- The definition of R^2 has been changed to 1 - SSE/SST for all estimators,
so R2() will give different results for test sets and
cross-validation compared to pls < 2.1-0. Also, the internal
calculations have been reorganised.
- The plot functions for coefficients, predictions and validation results
(R2, (R)MSEP) have gained an argument `main' to set the main title of the
plot.
- plots that go over several pages now only set `par(ask = TRUE)' if the
plot
device is interactive (suggested by Kevin Wright).
- mvr() and mvrCv() now check for near zero standard deviation when autoscaling
(`scale = TRUE')
See the file CHANGES in the sources for all changes.
--
Bj?rn-Helge Mevik and Ron Wehrens
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