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

[Botta-Dukát Zoltán]

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
>
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>
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