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. -- Bjørn-Helge Mevik and Ron Wehrens _______________________________________________ R-packages mailing list [EMAIL PROTECTED] https://stat.ethz.ch/mailman/listinfo/r-packages ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
