[R-sig-ME] How to know if random intercepts and slopes are, necessary for glmer.nb model

[Highland Statistics Ltd]

> ----------------------------------------------------------------------
>
> Message: 1
> Date: Mon, 19 Oct 2015 08:59:40 -0400
> From: David Jones <david.tn.jones at gmail.com>
> To: r-sig-mixed-models at r-project.org
> Subject: [R-sig-ME] How to know if random intercepts and slopes are
> 	necessary for glmer.nb model
> Message-ID:
> 	<CAJgUswL0mkbgpv-Xt1MsPtVbm9qGUZ+uaJ+wugPZw8Dvh-XcLA at mail.gmail.com>
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>
> I am receiving a number of different warnings/errors when running glmer.nb
> on a fairly large dataset (N>500,000). For some of the models I have run,
> program-reported errors prevent the generation of estimates. I suspect that
> it is because the random effects are very small. I have tried models with
> random intercepts, as well as models with both random intercepts and slopes
> (all models include fixed effects). I am running models on a dataset which
> in theory would include random effects (patients nested within hospitals).
>
> My question is: how do you know if random intercepts and slopes are
> necessary, if you can't even estimate the random effects models (and thus
> use a model comparison test)? As I am aware you can look at design effects
> to evaluate if a random intercept is necessary (though please correct me if
> I am wrong here).
>
> Some example code I have used is below - many thanks.
>
> a2 <- as.factor(analysis$Location)
> NBIntercept<- glmer.nb(y ~ a2 + (1 | Hospital), data = analysis)
> NBInterceptSlope <- glmer.nb(y ~ a2 + (1 | Hospital) + (1 + a2 | Hospital),
> data = analysis)
>
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>

David....this is a little bit a 'Gandalf' question. Perhaps you should 
first figure out why the NB GLMM does not run. How many hospitals do you 
have. Perhaps you can set the theta parameter in glmer.nb to a fixed 
value (use an interval with nearly the same lower and upper limit).... 
and get the (log of ) theta from a nearby NB GLM model. That would 
certainly make the estimation process easier!

Why are you doing an NB GLMM? Do the Poisson GLMM equivalents run? I 
assume you had overdispersion. What was driving the overdispersion?

And if computing time is slow for the second NB GLMM model, fit the 
first model and see whether there are any a2 effects per hospital in the 
residuals of the first model.


Alain




-- 
Dr. Alain F. Zuur

First author of:
1. Beginner's Guide to GAMM with R (2014).
2. Beginner's Guide to GLM and GLMM with R (2013).
3. Beginner's Guide to GAM with R (2012).
4. Zero Inflated Models and GLMM with R (2012).
5. A Beginner's Guide to R (2009).
6. Mixed effects models and extensions in ecology with R (2009).
7. Analysing Ecological Data (2007).

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