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Re: st: iccvar

From   Nick Cox <>
Subject   Re: st: iccvar
Date   Wed, 12 Dec 2012 16:53:53 +0000

-iccvar- and -quickicc- are from SSC.


On Wed, Dec 12, 2012 at 4:03 PM, Robert Fornango
<> wrote:
> Megan,
> -iccvar- produces the intraclass correlation coefficient (ICC) for a random
> intercept model after -xtmixed-. The ICC is the proportion of variation in
> the outcome that is attributable to differences between clusters. In this
> case, the ICC refers to the variation due to between patient differences in
> your data.
> However, the ICC can also be thought of as a measure of within-cluster
> correlation. For example, when there is no between-cluster heterogeneity,
> then there are no differences across patients, and observations do not
> cluster within patients. The ICC in this case would be 0. But, as the
> between-patient differences increase, so does the correlation of values
> within patients, and the ICC increases as well.
> The ICC is not the same as Pearson's r, as it calculates the covariance
> between pairs of observations using the model estimates for the mean (B),
> and standard deviations sd_(cons) and sd_(Residual). Pearson's r would be
> calculated by obtaining the sample means and standard deviations for
> different time points (in your case) to compute the covariance. Obviously,
> if you wanted a strict Pearson's correlation, you could -reshape- your data
> into wide format and obtain the correlation of observations across time
> points.
> I recommend pp.58-61 in:
> Rabe-Hesketh, Sophia, and Anders Skrondal. 2008. Multilevel and Longitudinal
> Modeling Using Stata. 2nd Edition. College Station: Stata Press.
> The third edition of this text was released in 2012. While I'm sure the
> discussion is likely to be the same on this topic, I am unsure of the page
> numbers.
> As a final note on -iccvar-: please be aware that the command only estimates
> the ICC for random intercept models, up to 4 levels. If you have a random
> intercept and random slope model, you should use -quickicc-, also by Eric C
> Hedberg.
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