[R-sig-eco] Fitting a GLMM to a percent cover data with glmer or glmmTMB

[Vasco Silva]

Thanks Zoltan. Using the glmmTMB with tweedie is the option that I can now
discern...

Vasco



Botta-Dukát Zoltán <botta-dukat.zoltan using okologia.mta.hu> escreveu no dia
quinta, 29/11/2018 à(s) 14:33:

> I have to correct myself :),  because an important point is missing from
> this sentence:
>
> Binomial distribution are defined as number of successes in independent
> trials.
>
> correctly:
>
> Binomial distribution are defined as number of successes in FIXED NUMBER
> OF independent trials.
>
> Zoltan
>
> 2018. 11. 29. 15:23 keltezéssel, Botta-Dukát Zoltán írta:
> > Hi,
> >
> > I'm sure that binomial is unsuitable for relative cover. Binomial
> > distribution are defined as number of successes in independent trials.
> > I think this scheme cannot be applied to relative cover or visually
> > estimated cover. It is important because both number of trials and
> > probability of success influence mean and variance, thus both should
> > have a meaning that correspond to terms in this scheme.
> >
> > Unfortunately, I have no experience with tweedie distribution. I am
> > also interested in experience of others! In theory an alternative
> > would be zero-inflated beta distribution (after rescaling percentage
> > between zero to one interval). Do some has an experience (including
> > its availability in R) with it?
> >
> > Cheers
> >
> > Zoltan
> >
> > 2018. 11. 28. 20:47 keltezéssel, Vasco Silva írta:
> >> Hi,
> >>
> >> I am trying to fit a GLMM on percent cover for each species using glmer:
> >>
> >>> str(cover)
> >> 'data.frame': 102 obs. of  114 variables:
> >> $ Plot : Factor w/ 10 levels "P1","P10","P2",..: 1 1 1 1 1 3 3 ...
> >> $ Sub.plot: Factor w/ 5 levels "S1","S2","S3",..: 1 2 3 4 5 1 2 ...
> >> $ Grazing : Factor w/ 2 levels "Fenced","Unfenced": 1 1 1 1 1 1 1  ...
> >> $ sp1 : int  0 0 0 1 0 0 1 ...
> >> $ sp2 : int  0 0 0 0 0 3 3 ...
> >> $ sp3 : int  0 1 0 0 1 3 3 ...
> >> $ sp4 : int  1 3 13 3 3 3 0 ...
> >> $ sp6 : int  0 0 0 0 0 0 0 ...
> >>   ...
> >> $ tot  : int  93 65 120 80 138 113 ...
> >>
> >> sp1.glmm <- glmer (cbind (sp1, tot- sp1) ~ Grazing + (1|Plot),
> >> data=cover,
> >> family=binomial (link ="logit"))
> >>
> >> However, I wonder if binomial distribution can be used (proportion of
> >> species cover from a total cover) or if I should  fitted the GLMM with
> >> glmmTMB (tweedie distribution)?
> >>
> >> I would greatly appreciate it if someone could help me.
> >>
> >> Cheers.
> >>
> >> Vasco Silva
> >>
> >>     [[alternative HTML version deleted]]
> >>
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