[ADMB Users] getting standardized coefficients in admb

(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

--
You received this message because you are subscribed to the Google
Groups
"ADMB Users" group.
To unsubscribe from this group and stop receiving emails from it,

send

an
email to users+un... at admb-project.org.
For more options, visit
https://groups.google.com/a/admb-project.org/d/optout.