Below is the output from a model of novel object test scores fit with the
nbinom1 (quasi-Poisson) option in
glmmADMB. I used this package/method because:
- the Poisson mean is < 5, so according to Bolker et al. 2009 I should not use
- there was overdipersion with the Poisson model run with
glmer. The NB1 model fit with
glmmADMBwas the best of all fits I tried.
There are 3 fixed factors as below, as well as a
Age interaction term, and individual (
ID) is the random factor.
Call: glmmadmb(formula = Score ~ Sex * Age + Object + (1 | ID), data = PGS, family = "nbinom1") AIC: 394.5 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.480 0.195 2.47 0.0137 SexM 0.797 0.262 3.05 0.0023 AgeS 1.224 0.254 4.82 1.4e-06 ObjectPLA -0.434 0.194 -2.24 0.0248 ObjectSCO -0.822 0.203 -4.05 5.1e-05 SexM:AgeS -0.825 0.353 -2.33 0.0196
Number of observations: total=105, ID=40 Random effect variance(s): Group=ID<br/> Variance StdDev<br/> (Intercept) 0.03738 0.1933 Negative binomial dispersion parameter: 1.5958 (std. err.: 0.34873) Log-likelihood: -189.228
Question: how can I test if the random effect is significant? I have looked around online, and where there are multiple random effects it seems straightforward, but I know I can't compare the
GLMM to a
GLM without my single random effect according to info on http://glmm.wikidot.com/faq.
I have tried Ben Bolker's
varprof function in the reef-fish example:
varprof(PGS.nbinom1) Error in varprof(PGS.nbinom1) : <br/> trying to get slot "deviance" from an object (class "glmmadmb") that is not an S4 object
As well as his
simulate.glm(PGS.nbinom1) Error in simulate.glm(M1) : family nbinom not implemented
And also Jeff Evans’ way, posted on archiveorange.com, where I ran the model with an uninformative dummy variable (a column with the word "dummy" in every row) and compared that with my model with ANOVA, but also got a warning message here:
anova(M1,M2) Analysis of Deviance Table Model 1: Score ~ Sex * Age + Object<br/> Model 2: Score ~ Sex * Age + Object<br/> NoPar LogLik Df Deviance Pr(>Chi)<br/> 1 8 -189.23 <br/> 2 8 -189.32 0 -0.19 1<br/> Warning message: In anova.glmmadmb(M1, M2) :<br/> something's wrong: models should be nested, increasing complexity should imply increasing log-likelihood
What else can I try? Any help would be much appreciated!