[R-sig-ME] Fwd: glmer won't allow quasi- distribution mixed models

[Ben Bolker]

  I don't know what examples you're looking at that show successful use
of quasilikelihood with lme4; it's been years since that option was
removed from the package ... (can you point me to those links? I would
be curious to see how old they are ...)

  You have a variety of other choices for handling overdispersion (see
the GLMM FAQ.  See
http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#overdispersion ,
which (as of 5 minutes ago) includes a quasi-likelihood hack for use
with glmer models ...

  A few other points from your code below:

- most folks now deprecate the use of attach() (even the manual page
?attach says not to, under "Good practice !) - it generally leads to
more confusion than it's worth
- Your overdispersion seems to be extreme (min/max of scaled residuals
from -12 to 38, deviance/resid df = 42958.6/1038  ~ 40); before you
paper over the cracks using an overdispersion model, it would be a good
to plot your data and model diagnostics and look for outliers and/or
severe violations of the model fit ...
 - it seems weird for movement distances to be count variables (suitable
for modeling via Poisson/NB).  I would expect them to be continuous and
positive, e.g. log-Normal or Gamma.  Can you explain how they come to be
counts in this case?

On 2018-07-09 11:13 AM, Luke Duncan 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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