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

[Scott@Foster m@ili@g off d@t@61@csiro@@u]

Hi,

I agree with Zoltan that bionimial is probably inappropriate, for the reasons he stated.

I'm not sure that Tweedie is your solution though -- it is defined for non-negative real numbers.
 Not just those between 0 and 100%.  Perhaps easiest to think of fish biomass caught in a net (can
be zero, or more.

Tweedie might work though, if your percentages are typically nowhere near the 100% boundary.  In
this case, the upper end of the support is kind of immaterial...  You hope...

Does glmmTMB supply a beta distribution?  Zero-inflated beta?  The quantile regression idea might be
useful too, as Brian suggested, but I'm not sure about random effects in that case.  Beta regression
will also have problems with exactly 0% (or 100%) observations.

It seems, to me, that you might be forced to make a decision about what is 'least wrong', rather
than what is 'best'.

Scott

PS Vasco and Zoltan: Sorry for the reply earlier, the message to the list bounced (CSIRO has
recently changed my email address).

On Thu, 2018-11-29 at 16:40 +0000, Vasco Silva wrote:
> 
> 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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-- 
Scott Foster
Research Scientist
Data61, CSIRO
E scott.foster using data61.csiro.au T +61 3 6232 5178
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