I am working on my data including the insect abundance in dependence of landscape variables with a nested random effect. Since i collected the individuals in the field i have count data and thought a poisson distribution would fit best (using lme4). Currently my model looks like this example glmer(insects~landscape1*landscape2 +(1|region/location), family="poisson", data=..). (no overdisperion)
Following "A practical guide to mixed models in R" http://ase.tufts.edu/gsc/gradresources/guidetomixedmodelsinr/mixed%20model%20guide.html i "should use" a lognormal or gamma distribution since they fit best. I tried lme with log-transformed response and glmer with gamma even if I have no continous data and both show similiar results in contrast to the glmer with poisson distribution. I also tried using ord_plot() and distplot() (pos. slope, negative intercept) which showed that log.series would be the best choice again (according to Friendly http://www.datavis.ca/courses/grcat/grcat.pdf chapter 2.3.).
I don't want to use the log-transformed approach but was wondering if I could also use gamma for discrete count data or only for continuous? Or do you can suggest any alternatives not using poisson for count data? Hope to get some new insights. Thank you
Here are the two outputs of the methods i was following to decide for the right distribution. There is one count with 0. I am not that familiar with statistics yet but i read a lot that log-transformed data are difficult to interpret and that it is better to go for another method if the data are not normally distributed. So, i thought there should be another way.