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.dun...@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
>
> [[alternative HTML version deleted]]
>
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>
[[alternative HTML version deleted]]
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