[R-sig-ME] standardized coefficients in glmer model

[Ben Bolker]

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On 10-12-11 03:29 PM, David Duffy wrote:
> On Sat, 11 Dec 2010, Leeuwen, Casper van wrote:
> 
>> 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?
> 
> Given this is a logistic regression, there are various more or less
> unsatisfactory equivalents of an R2.  You might be better off just
> comparing effect sizes eg odds ratio (exp(beta)) for sex versus that for
> the difference between the first and third quartiles of BMI or
> from say BMI=20 to BMI=25 and BMI=30, presuming this is human data.
> 
  I think the OP is not asking for a pseudo-R^2 or summary of overall
goodness of fit, but standardized regression coefficients ...
  given that "species" is one of the predictors, I bet it's not human data.

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