Dear R-list,
 
I'm running a mixed effect logistic regression with both factors and covariates, an interaction and a random factor.
 
model <- glmer (intact_binomial ~ 
              species 
              + sex
              + retention_time
              + body_mass 
              + body_mass * retention_time
              + (1 | individual)
              , family = binomial (link = "logit")
              )
summary(model)
 
summary() returns effects sizes given as coefficients of the different factors. However, I would like to indicate the importance of the different terms in the model, to determine the relative importance of for instance sex versus body_mass: which one is more important in explaining my dependent variable? 
 
I thought comparing standardized coefficients of the different terms would be a good way, but I can't find how to compute standardized coefficients.
 
lm.beta() as a function does not work on S4 class objects.
 
Can anyone tell me if there is a function to compute standardized coefficients, or explain how I might calculate them manually? Or has alternative way of indicating relative importance of the different terms?
 
Thanks in advance,
Casper 
 
Deakin University, Geelong, Au
 
 

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