Likelihood ratio for glmmTMB model?-CodePudding

I have a mixed model where I'm trying to find the significance of my random effect. The model is a mixed model with zero-inflated beta distribution which I built using the R package glmmTMB, with the following function:

model<-glmmTMB(Overlap~Diff.Long Diff.Bkp   DiffSeason   (1|Xnumber),ziformula=~1,data=data,family=beta_family())

I'm trying to find the significance of the variable "Xnumber". I've read that what I need to do is a likelihood ratio test, but don't know how to do this with a glmmTMB object. I've tried using the Anova() function but I don't think the output is giving me what I want:

Anova(model,type="II")
Analysis of Deviance Table (Type II Wald chisquare tests)

Response: Overlap
            Chisq Df Pr(>Chisq)  
Diff.Long  5.0217  1    0.02503 *
Diff.Bkp   1.4717  1    0.22507  
DiffSeason 7.5487  2    0.02295 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Any suggestions?

CodePudding user response：

Since Xnumber is the grouping variable for which random effects are generated, it won't show up in the Anova table, because it doesn't have a coefficient that is being tested. To test this term, you could just leave it out (i.e., estimate a pooled model) and then use lrtest() from the lmtest package to calculate the LR-test. The null hypothesis is that the pooled model is sufficient. Here's the example from the glmmTMB() function:

library(glmmTMB)
library(lmtest)
m1 <- glmmTMB(count ~ mined   (1|site),
               zi=~mined,
               family=poisson, data=Salamanders)
m2 <- glmmTMB(count ~ mined,
               zi=~mined,
               family=poisson, data=Salamanders)
lrtest(m1, m2)
#> Likelihood ratio test
#> 
#> Model 1: count ~ mined   (1 | site)
#> Model 2: count ~ mined
#>   #Df  LogLik Df  Chisq Pr(>Chisq)    
#> 1   5 -949.23                         
#> 2   4 -958.96 -1 19.456   1.03e-05 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

^{Created on 2022-11-25 by the reprex package (v2.0.1)}

Note, that in this case, since the test is significant, the pooled model is insufficient and the random effects model is preferred.