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

[Ben Bolker]

   Actually, now that I look at the article more carefully it turns out 
that those paragraphs are mostly focused on *mixed* models, and don't 
say too much about how the argument generalizes (so to speak) to the 
generalized-linear (mixed) model case.  Scaling the *estimated 
parameter* by 1/standard deviation of the response is not insane (you 
can't scale the response variable *before* you fit the model in a GLM, 
that doesn't make sense), but doesn't have the same nice interpretation 
as in a linear model.  In general the link functions do put the 
parameters on a simple, dimensionless scale, but I'm not sure about a 
sensible, general way to compare among parameters of models fitted with 
*different* link functions.

On 16-02-09 09:49 AM, Ellen Robertson wrote:
> Thanks very much for your response and for pointing me in the direction
> of that article. Cheers, Ellen
>
> On Sun, Feb 7, 2016 at 4:46 PM, Ben Bolker <bbolker at gmail.com
> <mailto:bbolker at gmail.com>> wrote:
>
>     (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 <mailto: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 <mailto: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 <mailto: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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