[R-sig-ME] [ADMB Users] getting standardized coefficients in admb
bbolker at gmail.com
Sun Feb 7 22:46:36 CET 2016
(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.
On Sun, Feb 7, 2016 at 2:47 PM, Ellen Robertson <robertsonep at gmail.com> wrote:
> Sorry for the delayed response. In my earlier email, I was referring to
> your post on
> 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,
> 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
>> 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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