[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.