Date:         Thu, 29 May 2008 20:33:41 -0500
Reply-To:     sudip chatterjee <sudip.memphis@GMAIL.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         sudip chatterjee <sudip.memphis@GMAIL.COM>
Subject:      Re: INTRACLASS CORR--PROC GLIMMIX
Comments: To: Robin R High <rhigh@unmc.edu>
In-Reply-To:  <OFFF35F1B3.00073400-ON86257458.006FBB1F-86257458.0071B258@unmc.edu>
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 <rhigh@unmc.edu> 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 <tw2@MAIL.COM>
> Sent by: "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
> 05/29/2008 02:38 PM
> Please respond to
> Tom White <tw2@MAIL.COM>
>
>
> 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
>
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