[R-sig-ME] [ADMB Users] getting standardized coefficients in admb

[Ben Bolker]

(I think this is more appropriate for r-sig-mixed-models, but I'm
leaving ADMB users cc'd for this last response.)

It's not obvious to me whether there's a simple analogue of
standardizing by response variance in the GLMM world.  I suppose you
*could* still standardize by predictor variance, or you could decide
that the link functions (log for NB/Poisson, logit for binomial)
effectively standardize the prediction side of the model.  It looks
like the last section of Schielzeth's 2010 MEE paper "Simple means
...", "Extensions", discusses this issue, but I haven't read it
carefully/absorbed it/tried to implement that in a function.

  cheers
    Ben


On Sun, Feb 7, 2016 at 2:47 PM, Ellen Robertson <robertsonep at gmail.com> wrote:
> Ben,
>    Sorry for the delayed response. In my earlier email, I was referring to
> your post on
> http://r-sig-mixed-models.r-project.narkive.com/1EtbqR8T/r-sig-me-standardized-coefficients-in-glmer-model
> where you talk about using a function similar to the 'lm.beta' function for
> getting standardized coefficients from lmer models ('lm.beta.lmer') .
>      I'm trying to get standardized beta coefficients from different types
> of glmer models (poisson, binomial, Gaussian) so that I can compare the
> effect sizes from each of these (I'm using all three of these different
> types of glmer models within a piecewise structural equation model and want
> to be able to compare the strengths of different paths).  I know that with
> continuous response/predictor variables I can just scale everything before
> running the model and that will output standardized beta coefficients.  But
> I am unsure of do this with non-continuous variables (such as a binomial
> response variable)?    You show (in the link above) how to scale binomial
> predictor variables (change them to numeric, 0/1, rather than
> male/female..and then scale)...but how would you do this with a binomial
> response variable which has to be 0/1?  I tried your "lm.beta.lmer" function
> and it worked when I had 2 predictors in my model but for some reason it
> didn't work with only one predictor variable.  I also wasn't sure if it
> would work with poisson/binomial models or if it only worked with lmer.
>     Thanks for any help you can give.  Cheers,
> Ellen
>
>
>
>
> On Wednesday, November 25, 2015 at 5:37:25 PM UTC-5, Ben Bolker wrote:
>>
>>  I meant to respond to this earlier (maybe I did, and maybe it fell
>> through the cracks).
>>
>>    Ellen, it's not clear whether you're asking about generic ADMB models
>> or about glmmADMB models: if the latter, then
>> r-sig-mix... at r-project.org is probably the more appropriate venue.
>>  If the former, then I'm not even sure what you would mean by
>> "standardized coefficients", as it would probably depend on the model.
>>
>>   Can you give a link/reference for "Bolker's code for beta.lmer for
>> glmer models"?
>>
>>   The very generic answer to your question is that you can either (1)
>> scale/center your continuous input variables *before* running the model
>> or (2) adjust the coefficients afterward, based on the means and
>> standard deviations of the parameters.  This
>>
>>
>> http://stackoverflow.com/questions/23642111/how-to-unscale-the-coefficients-from-an-lmer-model-fitted-with-a-scaled-respon/23643740#23643740
>>
>> gives a function that rescales parameters -- it should be ecumenical
>> (i.e., apply to any set of coefficients from a linear or generalized
>> linear model, no matter what software it was fitted with).
>>
>>
>> On 15-11-25 05:30 PM, Johnoel Ancheta wrote:
>> > Is this possible?
>> >
>> > On Mon, Nov 23, 2015 at 7:31 AM, Ellen Robertson <rober... at gmail.com>
>> > wrote:
>> >
>> >> Hi everyone,
>> >>    Is it possible to get standardized coefficients from admb models?  I
>> >> know about lm.beta for linear models and saw Bolkner's code for
>> >> beta.lmer
>> >> for glmer models....but I have been unable to get standardized
>> >> coefficients
>> >> from my admb models.  Thanks for your help,
>> >> Ellen
>> >>
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>> >>
>> >
>>
>