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Re: st: interpreting negative and positive AIC- OLS VS. GLM


From   Maarten buis <maartenbuis@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: interpreting negative and positive AIC- OLS VS. GLM
Date   Thu, 19 Aug 2010 17:12:00 +0000 (GMT)

--- On Thu, 19/8/10, Arina Viseth wrote:
> From your experience do you have a recommendation for
> assessing  model fit for this kind of model?

The thing that realy bites when it comes to modeling 
proportions with a linear model are the boundaries: 
You cannot have a linear line that will respect these 
boundaries forever (unless you have a horizontal line). 
So at some point a linear effect will have to become
nonlinear. The question is does that happen within
the range of your data, or can a linear line reasonably
represent your data. 

The first thing I would do is just plot the distribution
of your proportion and see if it gets close to one or
both of the boundaries. If that is the case I would not
use a linear model, and instead move towards one of the
alternatives like a fractional logit model or -betafit-
(which you can download by typing in Stata -ssc install
betafit-). A nice tool for that is Nick Cox's -stripplot-
(to install type in Stata -ssc install stripplot-), like
in the example below:

*------------------------- begin example -------------------
use http://fmwww.bc.edu/repec/bocode/c/citybudget.dta, clear
stripplot governing, stack width(.01)
*------------------------- end example ---------------------

If most of your observations are somewhere in the middle
and you thus think that linear regression is ok for your 
data, I would still check the residuals to see if the 
linear effects are appropriate and whether the boundaries 
haven't introduced more heteroskedasticity then you feel 
comfortable with.

Hope this helps,
Maarten

--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany

http://www.maartenbuis.nl
--------------------------


      

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