```Date: Wed, 4 Jun 2008 14:43:39 -0500 Reply-To: Robin R High Sender: "SAS(r) Discussion" From: Robin R High Subject: Re: INTRACLASS CORR--PROC GLIMMIX Comments: To: Tom White In-Reply-To: <20080529205840.DC65F1CE803@ws1-6.us4.outblaze.com> Content-Type: text/plain; charset="US-ASCII" Tom, A quick follow-up to my note from last week. One way to compute an "ICC" (as suggested in Section 14.3.3 from Snijders and Bosker - "Multilevel Analysis") is to apply the ratio: tau^2 / (tau^2 + 3.29) for the "logit" link and tau^2 / (tau^2 + 1) for the "probit" link and where 3.29 is 3.1416^2/3 (i.e., pi^2/3) which is the variance of the "logistic" distribution and 1 is the variance of the "standard normal". Both of these formulas apply to the linear predictor (i.e, the logit) and not to the actual proportions (which are non-linear transformations of the logits), so at best, it would be a "pseudo" ICC. Most of us don't think in terms of logits when working with 0/1 data (either collected independently or in clusters), so this summary measure needs a qualifier or word of caution. There are also the v and vcorr options on the RANDOM statement in both MIXED and GLIMMIX that I have found helpful for examining the size of variance components and resulting correlations from random effects models. In either PROC, the ODS statements should always be applied to look at them, as the output and datasets tend to quickly fill up the output window otherwise, e.g. ods output v=v vcorr=vcr; ods exclude v vcorr; proc glimmix data=exmp; class e tr pos; model rst = e pos / distribution=binary link=logit; random tr(e) / solution v vcorr; run; proc print data=vcr(obs=8); var row col1-col8; run; .. again these apply to the logits, so need that same "qualification" before saying much, if anything, about clustering of the proportions. Robin High UNMC Tom White Sent by: "SAS(r) Discussion" 05/29/2008 04:00 PM Please respond to Tom White To SAS-L@LISTSERV.UGA.EDU cc Subject Re: INTRACLASS CORR--PROC GLIMMIX Thank you Robin for this response-- So, just to double chcek with my original question: Does GLIMMIX produce a single statistic (something like intraclass corr coeff) to tell me how much nesting is present in mydata set with binary response variable? Perhaps you say that the 'statistic' I'm looking for is the odds ratio? This tells me how much nesting is present in my data? Any papers that discuss this concept with binary response withing GLIMMIX? Thank you. Tom ----- Original Message ----- From: "Robin R High" To: SAS-L@LISTSERV.UGA.EDU Subject: Re: INTRACLASS CORR--PROC GLIMMIX Date: Thu, 29 May 2008 15:41:51 -0500 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 -- Mail.com Autos- Powered by Oncars.com: Drive By Today! http://www.oncars.com ```

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