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| From | "JVerkuilen (Gmail)" <jvverkuilen@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: OLS assumptions not met: transformation, gls, or glm as solutions? |
| Date | Thu, 20 Dec 2012 22:29:16 -0500 |
On Thu, Dec 20, 2012 at 7:33 PM, Alan Acock <acock@me.com> wrote: > If I run > > > is there a clear interpretation of the coefficient or some transformation of the coefficients? > > I'm think the answer should be obvious to me, but it is not. Having spent time developing a similar model (using the beta distribution as an error with mixing terms, e.g., in Verkuilen & Smithson, 2012) what I would say is that it's a logit scaled coefficient. In general you could say it's a log-odds of some sort. The transformed linear prediction usually fits much better than would be an identity link formulation when there's reasonable skew and the resulting predicted values are always admissible, but I agree that the coefficients are not directly interpretable. That's not really all that different than many other transformed coefficients in other GLMs such as log link with gamma errors. That's one reason that I'd recommend generating predicted values for meaningful scenarios, or even entire predictive distributions. Verkuilen, J. & Smithson, M. J. (2012). Mixed and Mixture Regression Models for Continuous Bounded Responses Using the Beta Distribution. Journal of Educational and Behavioral Statistics, 37(1), 82-113. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/