Date: Mon, 22 Jun 2009 17:41:50 -0700
Reply-To: Bminer <b_miner@LIVE.COM>
Sender: "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From: Bminer <b_miner@LIVE.COM>
Subject: Re: Deviance versus log likelihood
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On Jun 22, 6:05 pm, shilin...@yahoo.com wrote:
> You should use -2(LL_reduced - LL_full) which is distributed as
> chi-square df=# of variables reduced.
> The error term in logistic or probit model is not useful. The
> distribution of Deviance_reduced - Deviance_full is not known. The
> definitions of both terms are different. You should not expect they
> would be equal to one another.
> On Jun 21, 3:17 pm, Bminer <b_mi...@live.com> wrote:
> > I am used to testing constraints on parameters (two nested models) in
> > logistic regression as either:
> > Deviance_reduced - Deviance_full
> > or
> > -2(LL_reduced - LL_full). Both give the same value to compare to
> > critical values of chi-square.
> > Using genmod and a normal error, identity link model the two values
> > above are not the same. Why?
> > Which should be used in a likelihood ratio test?
> > Thanks!
Hi Thanks for the reply. But, I see many authors saying to subtract
deviances or to compute deviance_full - deviance_reduced / scale. Here
is an example (look under Analysis of Deviance)
Its confusing because if you run a logistic regression with genmod,
deviance_full - deviance_reduced is the same exactly as -2(LL_reduced
- LL_full). If you run normal error with identity link, they are not.