```Date: Thu, 29 May 2008 20:33:41 -0500 Reply-To: sudip chatterjee Sender: "SAS(r) Discussion" From: sudip chatterjee Subject: Re: INTRACLASS CORR--PROC GLIMMIX Comments: To: Robin R High In-Reply-To: Content-Type: text/plain; charset=ISO-8859-1 Robin, This is the topic I am also very interested. What do you think about the Snijders and Boskers (book) method (1999) , where they kept individual residual variance fixed to 3.29 (pi-sq/3). Glimmix though produce level II residual std dev. So we can calculate ICC. I was looking at multilevel discussion group and nowadays many people suggest this method. I will like to have your opinion & others too about it. My new paper is related to this topic so any comment will be appreciated. Thank you. On Thu, May 29, 2008 at 3:41 PM, Robin R High wrote: > Tom, > > This is a topic about which I continually learn new things. When one > considers how the ICC is computed under the normal distribution model > (variance estimate is 'pooled' and assumed constant across grouping levels > which have different means);however, under the binary model, the variance > is a function of the mean, so constant variance is not part of the model; > computations for an ICC in this situation aren't the same. > > In the random effects model, when one considers how the clustering is > accounted for (i.e., applied within the link function) it is more likely, > perhaps, that the results will be distorted and even incorrect if the > random effect is not included; that is, a model computed with GLIMMIX > that converges with a positive estimate for the random effect is likely to > be better than assuming conventional, esp. when the sample size is > relatively "large". It would also be interesting to compare this random > effects logistic regression with NLMIXED if appropriate (since it is based > on quadrature). And GLIMMIX has the odds and oddsratio options (among > others) which simplify interpretation. > > Robin High > UNMC > > > > > > > Tom White > Sent by: "SAS(r) Discussion" > 05/29/2008 02:38 PM > Please respond to > Tom White > > > To > SAS-L@LISTSERV.UGA.EDU > cc > > Subject > INTRACLASS CORR--PROC GLIMMIX > > > > > > > Hello everyone, > > I have this sort question: > > From reading so far about PROC GLIMMIX, I undersand that it does not > produce an > intraclass corr coeff for binary dependent variable (i.e. logistic > regression). > > Therefore, what statistic can I use in GLIMMIX to tell me whether or not > nesting > of my data makes a diference. If it does, then I will use multilevel > logistic-- > if not, I will use conventional logistic. > > Thank you. > > T > > -- > Mail.com Autos- Powered by Oncars.com: Drive By Today! > http://www.oncars.com > ```

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