Question on glmmTMB

This should be fine. Unlike many mixed model packages, glmmTMB can 
handle models with no random effect.  When in doubt, you can just try 
out a comparison - this obviously isn't a 100% guarantee that something 
works reliably, but in this example all three approaches give very 
similar answers:

library(glmmTMB)
library(bbmle)
m1 <- glmmTMB(count~spp + mined, Salamanders, family=nbinom2)
m2 <- MASS::glm.nb(count~spp + mined, Salamanders)
m3 <- mle2(count ~ dnbinom(mu = exp(logmu), size = exp(logk)),
            parameters = list(logmu ~ spp + mined),
            start = list(logmu = 0, logk = 0),
            data = Salamanders)

library(broom)
library(broom.mixed)
tidy(m1)
tidy(m2)
tidy(m3)

Dear All,

The glmmTMB package is used to model data with mixed effects. For example:

glmmTMB(count~spp + mined + (1|site), Salamanders, family=nbinom2)

But I'm just curious to know what happens when the package is used to model
data without random effects (will this still be fine? How does this compare
with just using the glm function in the MASS package?). See example below:

glmmTMB(count~spp + mined, Salamanders, family=nbinom2)

With kind regards,
Faith

	[[alternative HTML version deleted]]

_______________________________________________
R-sig-mixed-models at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models