exp(coef(model)$group)

exp(fixef(model))

Thanks.... yes this worked successfully :o)

Et

On Thu, Apr 8, 2010 at 11:48 AM, Andy Fugard (Work)
<andy.fugard@sbg.ac.at>wrote:

> Here's another example, borrowed from the help for "lmer":
>
> > gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
>              family = binomial, data = cbpp)
>
> As you say, coef works:
>
> > coef(gm1)
> $herd
>   (Intercept)    period2   period3   period4
> 1   -0.8085096 -0.9923347 -1.128675 -1.580374
> 2   -1.6974292 -0.9923347 -1.128675 -1.580374
> 3   -0.9922697 -0.9923347 -1.128675 -1.580374
> 4   -1.3592525 -0.9923347 -1.128675 -1.580374
> 5   -1.5885461 -0.9923347 -1.128675 -1.580374
> 6   -1.7987950 -0.9923347 -1.128675 -1.580374
> 7   -0.5091313 -0.9923347 -1.128675 -1.580374
> 8   -0.7991613 -0.9923347 -1.128675 -1.580374
> 9   -1.6361848 -0.9923347 -1.128675 -1.580374
> 10  -1.9394614 -0.9923347 -1.128675 -1.580374
> 11  -1.4831632 -0.9923347 -1.128675 -1.580374
> 12  -1.4633469 -0.9923347 -1.128675 -1.580374
> 13  -2.0884474 -0.9923347 -1.128675 -1.580374
> 14  -0.4278151 -0.9923347 -1.128675 -1.580374
> 15  -1.9290041 -0.9923347 -1.128675 -1.580374
>
> But note the "$herd" bit.  Since this model has a varying intercept by
> herd, you get a column in the resulting data frame called "herd".
>
> So you could try, for this example:
>
> > exp(coef(gm1)$herd)
>   (Intercept)   period2   period3   period4
> 1    0.4455216 0.3707102 0.3234614 0.2058981
> 2    0.1831538 0.3707102 0.3234614 0.2058981
> 3    0.3707343 0.3707102 0.3234614 0.2058981
> 4    0.2568527 0.3707102 0.3234614 0.2058981
> 5    0.2042223 0.3707102 0.3234614 0.2058981
> 6    0.1654982 0.3707102 0.3234614 0.2058981
> 7    0.6010174 0.3707102 0.3234614 0.2058981
> 8    0.4497060 0.3707102 0.3234614 0.2058981
> 9    0.1947215 0.3707102 0.3234614 0.2058981
> 10   0.1437814 0.3707102 0.3234614 0.2058981
> 11   0.2269188 0.3707102 0.3234614 0.2058981
> 12   0.2314603 0.3707102 0.3234614 0.2058981
> 13   0.1238793 0.3707102 0.3234614 0.2058981
> 14   0.6519320 0.3707102 0.3234614 0.2058981
> 15   0.1452928 0.3707102 0.3234614 0.2058981
>
> Since the slopes don't vary by herd, you might also want just the fixed
> effects:
>
> > exp(fixef(gm1))
> (Intercept)     period2     period3     period4
>  0.2469585   0.3707102   0.3234614   0.2058981
>
> HTH,
>
> Andy
>
>
> E T wrote:
> > If I use the command coef(model) this extracts the coefficients in the
> > model, however if I try exp(coef(model)) I receive an error:
> >
> > Error in exp(coef(model)) : Non-numeric argument to mathematical function
> >
> > I could manually get the exp of each factor in my model..... but as I
> have a
> > large model (and also have numerous other models to produce), I was
> > wondering if there was an automated method
> >
> > Regards
> >
> > Et
> >
> > On Thu, Apr 8, 2010 at 11:27 AM, E T <2nuzzbot@gmail.com> wrote:
> >
> >> odds.ratios = exp(coefs(model))
> >>
> >> Thanks, however unfortunately when I try the above command I receive the
> >> following error:
> >>
> >> Error: could not find function "coefs"
> >>
> >> Regards
> >>
> >> Et
> >>
> >>
> >>
> >>
> >> On Wed, Apr 7, 2010 at 5:47 PM, Daniel Ezra Johnson <
> >> danielezrajohnson@gmail.com> wrote:
> >>
> >>> something like odds.ratios = exp(coefs(model))
> >>>
> >>>
> >>> On Apr 7, 2010, at 12:28 PM, E T <2nuzzbot@gmail.com> wrote:
> >>>
> >>>  Hi all,
> >>>> Apologies for the simplicity of my question.... however any advice is
> >>>> greatly appreciated. Thanks
> >>>>
> >>>> Is there a specific command available to obtain the odds ratios
> produced
> >>>> from a multilevel logistic model?
> >>>>
> >>>> I have estimated a multi-level logistic model using the lme4 package.
> I
> >>>> can
> >>>> obtain results using the 'summary' command, however I would like to
> >>>> obtain
> >>>> the computed odds ratios.
> >>>> (Similar to the output that can be produced for logistic GLM using the
> >>>> logistic.display command from the epicalc package).
> >>>>
> >>>>   [[alternative HTML version deleted]]
> >>>>
> >>>> _______________________________________________
> >>>> R-sig-mixed-models@r-project.org mailing list
> >>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>>
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-mixed-models@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>
> --
> Andy Fugard, Postdoctoral researcher, ESF LogICCC project
> "Modeling human inference within the framework of probability logic"
> Department of Psychology, University of Salzburg, Austria
> http://www.andyfugard.info
>

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