# GLMMs, stable isotope distribution analysis

I am currently working with a set of samples of stable isotopic concentrations obtained from a group of individuals. I am trying to process this data through a glmm() from the package lme4 to highlight any correlation with environnemental values. My first idea was to carry a lmer() from the same package lme4.

M1d13C<-lmer(d13C~evt_var1+(1|individual_ID)+(1|year),REML=T,data=Table1, na.action = "na.fail")


d13C is my response variable evt_var1 is my environnemental variable

Unfortunately it isn't working as i expected: my d13C variable isn't normally distributed.(you can see my qqplot() below)

I tried to transform it with a logarithmic and square root transformation but the corrected distribution wasn't normal either. I would like to perform an equivalent model analysis to be able to work with a non normalized distribution.

I know that using the glmer() function could solve my problem but i don't know what i can assign the family parameter to (binomial; gaussian; poisson or gamma). My variable d13C is a negative variable (it isn't a binary so binomial is a no go...)

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Do you know which one of this family parameter do i need to use and do I need to transform d13C to change them into positive values ?

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Thank you for your help, and don't hesitate to tell me if you think i did something wrong

Say your evt_var1 was a categorical variable with 2 levels with a large effect on the response. In that case you might expect the response variable to have a bimodal distribution corresponding to the 2 levels of evt_var1. Then the response variable certainly wouldn't be distributed normally.
It's quite possible that a mixed model analyzed with lmer will then work quite well; it has no problems with negative values. There may be no need to go to generalized linear models (glmer).