Message-ID: <4884d08b-9b13-8f8b-fc9c-a8abec88c97f@okologia.mta.hu>
Date: 2018-11-29T14:30:48Z
From: Botta-Dukát Zoltán
Subject: Fitting a GLMM to a percent cover data with glmer or glmmTMB
In-Reply-To: <f53d9582-73e4-0281-d32a-83d2c2264bae@okologia.mta.hu>
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 keltez?ssel, Botta-Duk?t Zolt?n ?rta:
> 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 keltez?ssel, Vasco Silva ?rta:
>> 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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