Dear all,

 

I have some decimal data (a diversity index) with nested random effects
(sites within years).

There are a lot of zeros in the diversity index (19 out of 61 data points).

I would like to investigate the effects of two variables (one continuous,
one categorical) on the diversity index.

I am more familiar with modeling count data and would set up a mixed model
with Poisson error and a subject level random variable for overdispersion
(there is one data point per site, per year so in the model below the random
effects are at the subject level).

 

model1<-lmer(DiversityIndex~categorical_variable*continuous_variable+(1|Year
/Site),family=poisson)

 

However I get the error that the poisson distribution is for integers only -
although if I look at the summary of the model the output still looks
sensible. Can I use the poisson distribution on non-integer data?

The diversity index cannot be transformed to normal and I need to keep the
nested random effects in the model so I am not sure what error distribution
I should be using for non integer, non normal data with nested random
effects?

Any suggestions as to the type of model/family I should be using would be
much appreciated.

 

Thanks in advance

Claire


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