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| From | Maarten Buis <maartenlbuis@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: xtmelogit convergence issues and log transforming IVs |
| Date | Wed, 25 Jan 2012 10:29:39 +0100 |
On Tue, Jan 24, 2012 at 9:53 PM, William Hauser wrote: > The outcome is dichotomous so I'm > using the xtmelogit command. Stata is version 12, intercooled. > > The problem is that the model simply will not converge unless I > transform two of the predictor variables which are, in their > untransformed form, highly overdispersed. > > Problem is, I'm not sure how to interpret the resulting odds ratios > for the log transformed predictor variables I generally like to use linear splines for such cases, which you can create using -mkspline-. > gen log_priors=(ln(prior_record)/ln(1.10) > (a bunch of missing values result for all those cases where the > offender has no prior record) > replace log_priors=0 if prior_record==0 > gen no_priors=0 > replace no_priors=1 if prior_record==0 This is not too problematic. It says that there is a smooth (though hard to interpret) relation between prior record and whatever your outcome is and a sudden kink in that relationship at 0 (no prior record). I can see how something like that can make substantive sense. However, I would first try this transformation (the extra dummy for 0 prior convictions) before adding the complication of logging your prior record. It may very well be that that is enough to solve your problem, and than you have all interpretable coefficients. consider the example below, which uses both the "additional dummy for special values strategy" and the "linear spline strategy". Within the group of respondents with 10 years of experience (c_ttl_exp=0), who are working 40 hours per week (c_hours=0 and h40=1), and who have high school as their highest achieved level of education (c_grade=0) we expect to find .25 persons with a higher occupation for every person with a lower occupation. This odds increases by 18 % ((1.18-1)*100%) for every year extra experience if one has less than 10 years experience and only a non-significant 5 % per year if one has more than 10 years experience. An extra hour per week work is associated with a 3% increase in the odds and there is a non significant penalty for working the regular (40) amount of of hours per week of a 5% decrease in the odds of having a high occupation. *--------------------- begin example ------------------- sysuse nlsw88, clear // generate the dependent variable gen byte high_occ = ( occupation < 3 ) if occupation < . // center experience and create spline gen c_ttl_exp = ttl_exp - 10 mkspline sp1 0 sp2 = c_ttl_exp // center hours and add dummy for 40 hours/week gen c_hours = hours - 40 gen byte h40 = ( hours == 40 ) if hours < . // center grade gen c_grade = grade - 12 // estimate the model logit high_occ sp1 sp2 c_hours h40 c_grade, or lincom _cons + h40, or *---------------------- end example -------------------- (For more on examples I sent to the Statalist see: http://www.maartenbuis.nl/example_faq ) Hope this helps, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/