# Multi-class logistic Generalized Additive Models [closed]

I am looking for a package for three-class logit and probit GAM. I have searched and found out that ZeligGAM in R works for this but it seems that it is only applied to binary dependent variables. In my case, there are three groups for endogeneous variable. Do anyone happen to know?

Thank you

• Asking for a package is generally off topic here but I imagine your question could be edited to make it on topic. – Silverfish Aug 27 '15 at 7:46

## 1 Answer

Check out the mboost package, which implements generalized additive models of the multinomial family within a boosted framework that is resistant to the curse of dimensionality as well as problems of separation and partial separation (the logistic regression equivalent of multicollinearity). The package allows you to construct an ensemble of boosted base learner models where the terms can be linear, additive (penalized splines), random effects (exploiting the finding the a varying coefficients model, also allowed as a base learner, can be formulated as a random effect), and 2d effects (I believe they are Markov random fields). There is one hyper parameter that you will need to tune, which governs the number of boosting iterations. The function of interest is mboost::gamboost. In my experience, the package also performs quite well even on sizable datasets in the range of hundreds of thousands of datapoints. In my opinion, mboost and the packages that depend on it are among the most valuable packages for building models in R, and there is to my knowledge nothing quite like it available in Python, though I could be wrong.

• Many thanks for your helpful answer, Brash! really appreciate. – NL T Aug 28 '15 at 0:17