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email@example.com (Brendan Halpin)
Tue, 07 Feb 2012 23:34:48 +0000
I'm having trouble with convergence, fitting a moderately complex
I have a substantively important interaction (categorical, 4*4) that is
sparse in the data. For some subsets of the data it is properly
estimated and is very significant by the LR test. For others it
complains "not concave" during the ML iterations and reports "Warning:
convergence not achieved" at the end. Typically, for one parameter
estimate, the SE is not reported and for some others the parameter
estimate is large and the SE enormous.
I'm fitting these models programmatically on a reasonably large number
of subsets of the data (for multiple imputation), and I'm primarily
interested in the predicted probability.
First, is there a way to use the factor-variable notation to suppress a
particular parameter in the interaction (or should I just use a recoded
copy of the problematic variable)?
Second, are there any circumstances under which the predicted
probabilities might be usable after all?
Brendan Halpin, Department of Sociology, University of Limerick, Ireland
Tel: w +353-61-213147 f +353-61-202569 h +353-61-338562; Room F1-009 x 3147
mailto:firstname.lastname@example.org ULSociology on Facebook: http://on.fb.me/fjIK9t
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