[R-sig-ME] Fitting GLMM to percent cover data with glmmTMB
mollieebrook@ @ending from gm@il@com
Sun Dec 2 15:19:19 CET 2018
Sorry, I had been thinking that the ziformula in glmmTMB would handle the zeros, but I just realized that, for now, the zeros have to be fit with a separate model because of a call to family$initialize which checks the response variable. https://github.com/glmmTMB/glmmTMB/issues/355 <https://github.com/glmmTMB/glmmTMB/issues/355>
So for now, you could either fit the hurdle in two separate models in glmmTMB or fit a single model in gamlss.
> On 30Nov 2018, at 21:21, Vasco Silva <silvadavasco using gmail.com> wrote:
> Thanks Mollie.
> I convert % cover to relative abundance using "decostand" function and fit the GLMMM but it seems to lack something:
> > sp1.glm1<-glmmTMB(sp1~Grazing+(1|Plot),zi=~0, data=cover,
> + family=beta_family(link ="logit"))
> Error in eval(expr, envir, enclos) : y values must be 0 < y < 1
> Mollie Brooks <mollieebrooks using gmail.com <mailto:mollieebrooks using gmail.com>> escreveu no dia sexta, 30/11/2018 à(s) 11:21:
> Hi Vasco,
> I mostly agree with what Scott Foster said over on R-sig-eco to your earlier question:
> > 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.
> I would convert percentages to the 0-1 scale and then try a zero-inflated beta distribution. The Tweedie makes more sense if your response variable is the sum of a bunch of positive values like body weights.
> You can do a GLMM with a zero-inflated beta distribution in glmmTMB with something like
> m1 <- glmmTMB(sp1 ~ Grazing + (1|Plot), zi=~1, data=cover, family=beta_family())
> m2 <- glmmTMB(sp1 ~ Grazing + (1|Plot), zi=~ Grazing, data=cover, family=beta_family())
> > On 29Nov 2018, at 22:24, Vasco Silva <silvadavasco using gmail.com <mailto:silvadavasco using gmail.com>> wrote:
> > Hi,
> > I am trying to fit a GLMM on percent cover for each plant species:
> >> str(cover)
> > 'data.frame': 100 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 ...
> > I was wondering whether the GLMM can be fitted with glmmTMB (tweedie
> > distribution) and if so, should I use percent cover or percent cover
> > converted to relative abundance?
> > sp1.glmm <- glmmTMB (sp1 ~ Grazing + (1|Plot), data=cover, family=tweedie
> > (link ="logit"))
> > Any advice would be very much appreciated.
> > Cheers.
> > Vasco Silva
> > [[alternative HTML version deleted]]
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