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

[Cade, Brian]

Beta regression can be used for modeling proportion (or percentage) cover
data, but there are some issues with using it if you have many values of
0.0 or 1.0.  A much more flexible approach that I've used is to use
quantile regression with the proportion response (y) data logit
transformed.  Much easier to deal with 0.0 or 1.0 (boundary values) with
quantiles than with the means being estimated by beta regression.  The
quantreg package in R has what you need, but I think this logit transformed
quantile regression approach is also implemented in another package too
(perhaps Qtools package).  See Bottai et al. (2010.  Logistic quantile
regression for bounded outcomes.  Statistics in Medicine 29: 309-317.).

Brian

Brian S. Cade, PhD

U. S. Geological Survey
Fort Collins Science Center
2150 Centre Ave., Bldg. C
Fort Collins, CO  80526-8818

email:  cadeb using usgs.gov <brian_cade using usgs.gov>
tel:  970 226-9326



On Thu, Nov 29, 2018 at 7:24 AM Botta-Dukát Zoltán <
botta-dukat.zoltan using okologia.mta.hu> wrote:

> 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]]
> >
> > _______________________________________________
> > R-sig-ecology mailing list
> > R-sig-ecology using r-project.org
> > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>
> _______________________________________________
> R-sig-ecology mailing list
> R-sig-ecology using r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>

	[[alternative HTML version deleted]]