0

I'm working with Bayesian hierarchichal regressions fitted with R-INLA. I would like to simplify my model by reducing the number of covariates.

According to my understanding, Bayesian variable selection (spike & slab priors) cannot be done with R-INLA . I don't like the idea of forward/backward selection based on some information criteria (WAIC, DIC).

What approaches would you recommend for variable selection in this context? I'd appreciate it if you could cite your sources.

antarctica
  • 43
  • 5
  • I solved this problem (not for R-INLA, but it was Gaussian Process as well) by chosing different tool for variable selection. I used R package Boruta for this purpose, along with simple GLM with backward selection. – Tomas Jul 11 '21 at 16:57
  • Thanks! I'll take a look at the Boruta method. But I'm trying to avoid stepwise selection... – antarctica Jul 11 '21 at 20:35
  • Projpred is an r library designed to help with this. Have you looked at that library? – Demetri Pananos Jul 11 '21 at 21:21
  • Thanks for the tip, projpred looks interesting. However, in this case I'm looking for methods that could be compatible with INLA. – antarctica Jul 12 '21 at 01:59

0 Answers0