[R-sig-ME] [ADMB Users] getting standardized coefficients in admb
robertsonep at gmail.com
Tue Feb 9 15:49:20 CET 2016
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> 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.
> On Sun, Feb 7, 2016 at 2:47 PM, Ellen Robertson <robertsonep at gmail.com>
> > Ben,
> > Sorry for the delayed response. In my earlier email, I was referring
> > your post on
> > where you talk about using a function similar to the 'lm.beta' function
> > getting standardized coefficients from lmer models ('lm.beta.lmer') .
> > I'm trying to get standardized beta coefficients from different
> > 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
> > to be able to compare the strengths of different paths). I know that
> > continuous response/predictor variables I can just scale everything
> > running the model and that will output standardized beta coefficients.
> > 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"
> > 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
> >> 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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> >> >>
> >> >
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