[R-sig-ME] Finding the best package for a mixed Poisson model

[Nik Tuzov]

Hello:

Couldyou help me get oriented among various packages to fit a mixedPoisson model. The goal is to find a relatively fast method that cantake at least two random factors, possibly with interaction, e.g.:

Y~ fixed part + A + B + A*B

Ihave looked at SAS GLIMMIX, lme4::glmer, glmmPQL, glmmTMB, andGLMMadaptive. I have stayed away from simulation/MCMC based methodsfor speed reasons. My questions are:

1)Are there any more packages that are consistent with my objective?

2)GLIMMIX (with default METHOD = RSPL option) and glmmPQL refer to thesame two papers: Wofinger, O’Connell, 1993 and Breslow, Clayton,1993. Does this mean that glmmPQL was meant to reproduce the defaultGLIMMIX? If yes, are they really consistent?

3)This reference:

https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#fitting-models-with-overdispersion

claimsthat previously lme4 had a functionality similar to glmmPQL but thenit was removed altogether because it was “deemed unreliable”.That implies that the default estimation method in GLIMMIX is veryunreliable (see 2)). Is it really that bad? If yes, why didn’t SASpick a different default (or maybe they did it in some newer PROC)?

4)Presently glmer uses Laplace or Gauss-Hermite quadrature, the latteronly if there is one random term in the model. How consistent is itwith the corresponding SAS options METHOD = LAPLACE and METHOD=QUAD ?

5)GLMMadaptive can use the quadrature with more than one random term:

https://stats.stackexchange.com/questions/403147/an-r-package-for-glmm-estimation-with-two-random-effects

Thedocumentation says that it’s based on the paper of Pinheiro &Bates, 1995, then why is it not available in glmer which is supportedby Bates? Is that because the GLMMadaptive results are unreliable?




Thanksin advance,

NikTuzov


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