Extracting posterior modes and credible intervals from glmmTMB output

I normally work with lme4 package, but the glmmTMB package is increasingly becoming better suited to work with highly complicated data (think overdispersion and/or zero-inflation).

Is there a way to extract posterior modes and credible intervals from glmmTMB models, similar to how it is done for lme4 models (example here).

Details:

I am working with count data (available here) that are zero-inflated and overdispersed and have random effects. The package best suited to work with this sort of data is the glmmTMB (details here). (Note two outliers: euc0==78 and np_other_grass==20).

The data looks like this:

euc0 ea_grass ep_grass np_grass np_other_grass month year precip season   prop_id quad
 3      5.7      0.0     16.7            4.0     7 2006    526 Winter    Barlow    1
 0      6.7      0.0     28.3            0.0     7 2006    525 Winter    Barlow    2
 0      2.3      0.0      3.3            0.0     7 2006    524 Winter    Barlow    3
 0      1.7      0.0     13.3            0.0     7 2006    845 Winter    Blaber    4
 0      5.7      0.0     45.0            0.0     7 2006    817 Winter    Blaber    5
 0     11.7      1.7     46.7            0.0     7 2006    607 Winter    DClark    3

The glmmTMB model:

model<-glmmTMB(euc0 ~ ea_grass + ep_grass + np_grass + np_other_grass + (1|prop_id), data = euc, family= nbinom2) #nbimom2 lets var increases quadratically
summary(model)
confint(model) #this gives the confidence intervals

How I would normally extract the posterior mode and credible intervals for a lmer/glmer model:

#extracting model estimates and credible intervals
sm.model <-arm::sim(model, n.sim=1000)
smfixef.model = sm.model@fixef
smfixef.model =coda::as.mcmc(smfixef.model)
MCMCglmm::posterior.mode(smfixef.model)  #mode of the distribution
coda::HPDinterval(smfixef.model)  #credible intervals

#among-brood variance
bid<-sm.model@ranef$prop_id[,,1]
bvar<-as.vector(apply(bid, 1, var)) #between brood variance posterior distribution
bvar<-coda::as.mcmc(bvar)
MCMCglmm::posterior.mode(bvar) #mode of the distribution
coda::HPDinterval(bvar) #credible intervals

Most of an answer:

1. Getting a multivariate Normal sample of the parameters of the conditional model is pretty easy (I think this is what arm::sim() is doing.

library(MASS)
pp <- fixef(model)$cond
vv <- vcov(model)$cond
samp <- MASS::mvrnorm(1000, mu=pp, Sigma=vv)

(then use the rest of your method above).

2. I'm a little skeptical that your second example is doing what you want it to do. The variance of the conditional modes is not necessarily a good estimate of the between-group variance (e.g. see here). Furthermore, I'm nervous about the half-assed-Bayesian approach (e.g., why no priors? Why look at the posterior mode, which is rarely a meaningful value in a Bayesian context?) although I do sometimes use similar approaches myself!) However, it's not too hard to use glmmTMB results to do a proper Markov chain Monte Carlo analysis:

library(tmbstan)
library(rstan)
library(coda)
library(emdbook) ## for lump.mcmc.list(), or use runjags::combine.mcmc()

t2 <- system.time(m2 <- tmbstan(model$obj))
m3 <- rstan::As.mcmc.list(m2)
lattice::xyplot(m3,layout=c(5,6))
m4 <- emdbook::lump.mcmc.list(m3)
coda::HPDinterval(m4)

It may be helpful to know that the theta column of m4 is the log of the among-group standard standard deviation ...

(See vignette("mcmc", package="glmmTMB") for a little bit more information ...)