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 Bjørn-Helge Mevik _______________________________________________ R-packages mailing list [EMAIL PROTECTED] https://stat.ethz.ch/mailman/listinfo/r-packages ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html