Thanks Zoltan. Using the glmmTMB with tweedie is the option that I can now discern...
Vasco Botta-Dukát Zoltán <botta-dukat.zol...@okologia.mta.hu> escreveu no dia quinta, 29/11/2018 à(s) 14:33: > 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 keltezéssel, Botta-Dukát Zoltán írta: > > 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 > >> > >> [[alternative HTML version deleted]] > >> > >> _______________________________________________ > >> R-sig-ecology mailing list > >> R-sig-ecology@r-project.org > >> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > > > > _______________________________________________ > > R-sig-ecology mailing list > > R-sig-ecology@r-project.org > > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > > _______________________________________________ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
