Hi Vasco,
An approach called fractional outcome regression sounds like it might be
suitable. It is advocated for variables in the range 0 to 1 (and including
these endpoints)
regards,
James
Message: 1
Date: Thu, 29 Nov 2018 15:23:32 +0100
From: =?UTF-8?Q?Botta-Duk=c3=a1t_Zolt=c3=a1n?=
<botta-dukat.zol...@okologia.mta.hu>
To: r-sig-ecology@r-project.org
Subject: Re: [R-sig-eco] Fitting a GLMM to a percent cover data with
glmer or glmmTMB
Message-ID: <f53d9582-73e4-0281-d32a-83d2c2264...@okologia.mta.hu>
Content-Type: text/plain; charset="iso-8859-2"; Format="flowed"
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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