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:
'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]]