[R] glmer won't allow quasi- distribution mixed models

[Bert Gunter]

You should probably post this on the r-sig-mixed-models list instead, where
you are more likely to find the expertise to diagnose the problem and give
you a helpful response.

Cheers,
Bert



Bert Gunter

"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )

On Mon, Jul 9, 2018 at 6:34 AM, Luke Duncan <luke.mangaliso.duncan using gmail.com
> wrote:

> Dear R folk
>
> I am trying to run a series of models on distance data for three different
> species of animals. My data are not zero-inflated (distances were recorded
> for locomotion only and so if the animal didn't move, it wasn't recorded)
> and are Poisson distributed. However, all of the models that I run are
> horrifically over-dispersed and based on what I read online I thought that
> maybe I should consider using a quasi-Poisson distribution to attempt to
> account for the over-dispersion. All the online posts of others show that
> they do so successfully but for some reason, my lme4 package cannot use
> quasi-distributions. I have uninstalled and reinstalled R and the packages
> and I still get the same problem.
>
> I am
>
> a) at a loss as to how to deal with the over-dispersion I have and
> b) baffled by the fact that lme4 everywhere else can cope with
> quasi-distributions but mine can't.
>
> Any help would be appreciated!
>
> My code:
>
> library(lme4)
> woodlicedata<-read.csv("Woodlice.csv",header=T)
> attach(woodlicedata)
> names(woodlicedata)
> > ### This set of models examine whether there are differences in distances
> travelled.
> >
> distmodel<-glmer(Distance~Treatment*Sex+(1|ID)+(1|Path.
> set/ID),family=poisson(link='log'))
> > summary(distmodel)  ### AIC= 42972.6
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) [
> glmerMod]
>  Family: poisson  ( log )
> Formula: Distance ~ Treatment * Sex + (1 | ID) + (1 | Path.set/ID)
>
>      AIC      BIC   logLik deviance df.resid
>  42972.6  43007.3 -21479.3  42958.6     1038
>
> Scaled residuals:
>     Min      1Q  Median      3Q     Max
> -11.853  -4.074  -1.656   2.146  38.035
>
> Random effects:
>  Groups      Name        Variance  Std.Dev.
>  ID:Path.set (Intercept) 6.485e-02 0.2546560
>  ID          (Intercept) 6.906e-02 0.2627973
>  Path.set    (Intercept) 1.368e-10 0.0000117
> Number of obs: 1045, groups:  ID:Path.set, 104; ID, 52; Path.set, 2
>
> Fixed effects:
>                             Estimate Std. Error z value Pr(>|z|)
> (Intercept)                  4.20814    0.07757  54.248  < 2e-16 ***
> TreatmentRestricted          0.10843    0.14359   0.755  0.45015
> SexMale                     -0.08408    0.11545  -0.728  0.46644
> TreatmentRestricted:SexMale -0.49300    0.18781  -2.625  0.00866 **
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Correlation of Fixed Effects:
>             (Intr) TrtmnR SexMal
> TrtmntRstrc -0.540
> SexMale     -0.672  0.363
> TrtmntRs:SM  0.413 -0.765 -0.615
>
> >
> distmodel2<-glmer(Distance~Treatment*Sex+(1|ID)+(1|Path.
> set/ID),family=quasipoisson(link='log'))
> Error in lme4::glFormula(formula = Distance ~ Treatment * Sex + (1 | ID) +
> :
>   "quasi" families cannot be used in glmer
>
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>
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