I'm very new to R and PLS-regression. I would like to know, based on your experience, which R packages for PLS-regression are most highly recommended. My area of application is chemistry.
Contents
hide
Best Answer
Each package has been developed for some specific purposes although they may all have some "Partial least squares regression" term as the their general explanation. For example
- Package plsRglm is designed for Partial least squares Regression for (weighted) generalized linear models and kfold crossvalidation of such models using various criteria.
- Package plsRcox is good for Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings.
- Package pls is developed for Partial Least Squares Regression (PLSR), Principal Component Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS).
Maybe for the chemistry the package pls would be more suitable since it has been mentioned (as an example) on page 2 of the Journal of Statistical Software in here.
Depending on what you are modeling, what is your model and the approach you may decide to go with a different package. One last thing, if a package is not reliable in R, then it will be removed from CRAN.