[R] Heteroscedasticity in a percent-cover dataset

[Lai Wen Ya Samantha]

Hi,

I am currently trying to do a GLMM on a dataset with percent cover of
seagrass (dep. var) and a suite of explanatory variables including algal
(AC) and epiphyte cover (EC), rainfall, temperature and sunshine hours.

M2=glmer(SG~AC+EC+TP+SS+RF+(1|Location/fSi/fTr),
family=binomial,data=data,nAGQ=1)

As the dependent variable is percent cover, I used a binomial error
structure. I also have a random effect due to nested of the data collection
strategy. However, I keep getting heteroscedasticity issues as shown in the
image below. I have tried using an arcsine transformation (with a lme), but
the scatter of residuals are still very much similar.

What else can I do to try to resolve the heteroscedasticity in my data? Any
help will be very much appreciated!


<http://r.789695.n4.nabble.com/file/n4719735/Heteroscedasticity.png>




[http://r.789695.n4.nabble.com/file/n4719735/Heteroscedasticity.png]

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