[EXTERNAL] Re: Fitting a GLMM to a percent cover data with glmer or glmmTMB
Beta regression can be used for modeling proportion (or percentage) cover data, but there are some issues with using it if you have many values of 0.0 or 1.0. A much more flexible approach that I've used is to use quantile regression with the proportion response (y) data logit transformed. Much easier to deal with 0.0 or 1.0 (boundary values) with quantiles than with the means being estimated by beta regression. The quantreg package in R has what you need, but I think this logit transformed quantile regression approach is also implemented in another package too (perhaps Qtools package). See Bottai et al. (2010. Logistic quantile regression for bounded outcomes. Statistics in Medicine 29: 309-317.). Brian Brian S. Cade, PhD U. S. Geological Survey Fort Collins Science Center 2150 Centre Ave., Bldg. C Fort Collins, CO 80526-8818 email: cadeb at usgs.gov <brian_cade at usgs.gov> tel: 970 226-9326 On Thu, Nov 29, 2018 at 7:24 AM Botta-Duk?t Zolt?n <
botta-dukat.zoltan at okologia.mta.hu> wrote:
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
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