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From: Ryan on 11 Sep 2009 10:19
On Sep 10, 8:19 am, DBK <boydkra...(a)gmail.com> wrote:
> On Sep 10, 7:09 am, Bruce Weaver <bwea...(a)lakeheadu.ca> wrote:
>
>
>
>
>
> > On Sep 9, 10:29 pm, DBK <boydkra...(a)gmail.com> wrote:
>
> > > Before running a standard logistic regression (and later a multi-level
> > > logistic model), I applied the arcsine, square root transformation to
> > > several variables expressed originally in proportions. I have two
> > > questions. First, is there any reason I shouldn't do this? Second, if
> > > not, what would be the back transformation procedure? First convert
> > > from log of odds ratio to odds ratio? Then a back transformation of
> > > the arcsine square root transformation?
>
> > > DBK
>
> > In my experience, the arcsine transformation is typically used for
> > dependent variables that are proportions (usually in the context of
> > ANOVA or linear regression models).  Why do you want to use it for
> > explanatory variables?  In logistic regression, there is no
> > requirement for continuous explanatory variables to be normally
> > distributed, if that is what you are concerned about.
>
> > --
> > Bruce Weaver
> > bwea...(a)lakeheadu.cahttp://sites.google.com/a/lakeheadu.ca/bweaver/Home
> > "When all else fails, RTFM."
>
> Yes, there is not a normality requirement for a standard logistic
> model (i.e. single level) but what about a multilevel logistic model?
>
> Thanks for the response.- Hide quoted text -
>
> - Show quoted text -

I'm cross-posting this question to the SAS group. The question is
around assumptions to running a multilevel logistic regression (a type
of generalized linear mixed model). Could someone explain and/or
provide a reference for the assumptions to running a multilevel
logistic regression model.

Let's stick with a simple example where we have a binary dependent
variable (0/1), one continuous explanatory variable, and a random
intercept. One might code up this type of model in the GLIMMIX
procedure as follows:

proc glimmix data=mydata method=quad;
class person;
model y = x / s link=logit dist=binary;
random intercept / subject = person;
run;

What are the specific assumptions to this test?

Thanks,

Ryan
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