[R-sig-ME] mixed lognormal hurdle model with multiple grouping factors

[Cesko Voeten]

Hi Guillaume,

If you're not afraid to go Bayesian, brms can do it. Alternatively, you may be able to use glmmTMB and treat the hurdle part as zero inflation, but this is conceptually not the same thing as a hurdle model so you would need to judge whether that would make sense at all for your application.

HTH,
Cesko

Op 18-12-2019 om 13:48 schreef Guillaume Adeux:
> Hi everyone,
> 
> I am looking for a package which can handle "hurdle.lognormal" distribution
> family and multiple grouping factors.
> 
> GLMMadaptive seemed as the way to go but unfortunately, to the best of my
> knowledge, it does not handle multiple grouping factors (random effects).
> 
> You may ask why? I am analyzing plant diversity and one of the treatments
> led to plots which were dominated by one species. Hence, certain diversity
> indices are estimated as zero in these plots, and produces a mass at zero.
> All other values are positive and continuous.
> 
> Anyone have an idea of a package/function which can handle this? Or any
> alternative approach?
> 
> In lmer syntax, the model is the following:
> 
> mod=lmer(diversity~block+syst+(1|plot)+(1|year)+(1|plot:year),data=density,REML=F)
> 
> Thank you for your time and help.
> 
> Sincerely,
> 
> Guillaume ADEUX
> 
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