[R-sig-ME] LRT between GLMM and GLM to test a Single Random Intercept

Thierry Onkelinx thierry@onkelinx @ending from inbo@be
Wed Dec 12 11:07:40 CET 2018

Dear Juho,

I'd take a step back and think on why you add the random intercept. Is it
clearly a part of the design? E.g. it takes repeated measures into account.
Then you need the term in the model, what ever the p-value. The variance of
the random effect indicates its importance.

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey


Op wo 12 dec. 2018 om 06:37 schreef Juho Kristian Ruohonen <
juho.kristian.ruohonen using gmail.com>:

> My readers are likely to want to see a p-value on the only random effect
> (an intercept) in my logistic GLMM.
> Supposedly, if I fit the model using Laplace approximation, then the
> likelihood is comparable with that of the fixed-effects model, so the
> p-value from a LRT (divided by two) can be used. But I don't trust the
> Laplace approximation much. I'd rather use at least 10 quadrature points
> for improved accuracy. This also results in a more flattering (smaller)
> random-effect variance and hence a lower reported intraclass correlation.
> But if I use any more than 1 quadrature point, I can no longer report a
> p-value on the random effect because *anova()* refuses to compare the
> models, citing incomparable likelihood functions. I thought of calculating
> the log-likelihood of the GLMM manually using *dbinom()*, the data and the
> fitted values, but this thread
> <
> https://stats.stackexchange.com/questions/381085/calculating-log-likelihood-of-logistic-adaptive-quadrature-glmm-for-comparison-w
> >
> says I can't use the binomial PMF for that.
> Is there a way I can have my cake (many quadrature points) and eat it too
> (get a p-value for the random effect)? That parametric bootstrap procedure
> sounds neat, but I'd still be running into the same problem: the LRT
> calculated at each iteration compares a fixed and a mixed model, hence the
> likelihoods cannot be compared.
>         [[alternative HTML version deleted]]
> _______________________________________________
> R-sig-mixed-models using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

	[[alternative HTML version deleted]]

More information about the R-sig-mixed-models mailing list