[R-sig-ME] Fitting GLMM to percent cover data with glmmTMB
Mollie Brooks
mollieebrook@ @ending from gm@il@com
Fri Nov 30 12:21:04 CET 2018
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())
cheers,
Mollie
> On 29Nov 2018, at 22:24, Vasco Silva <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
>
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>
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