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.


closed as off-topic by gung, AdamO, Andy, Glen_b, Nick Cox May 15 '14 at 21:09

  • This question does not appear to be about statistics within the scope defined in the help center.
If this question can be reworded to fit the rules in the help center, please edit the question.

  • $\begingroup$ Velcome to the site! $\endgroup$ – kjetil b halvorsen May 15 '14 at 13:38
  • 4
    $\begingroup$ This question appears to be off-topic because it is about asking for statistical packages. $\endgroup$ – gung May 15 '14 at 18:23

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.


Not the answer you're looking for? Browse other questions tagged or ask your own question.