[R] Multinomial logistic regression under R and Stata
Tak Wing Chan
tw.chan at sociology.oxford.ac.uk
Mon Apr 21 17:58:59 CEST 2003
Dear Colleagues
I posted to the R-help and Stata lists a little while ago concerning
some disagreement in results I obtained from using the multinom function
in R and the mlogit command in Stata.
Many thanks to colleagues for your comments and ideas. I have checked
out some of your suggestions, and here is a report. The disagreements I
reported are of two types: (1) parameter estimates for the intercepts
and (2) standard errors of a quadratic term of a quantitative variable.
Regarding (1): yes, as some of you suggested, this is due to the coding
of another covariate in the model. Thanks!
As for (2), it turns out that the problem has to do with the scale of
the quadratic term.
In my original model, I have, out of habit, scaled down the quadratic
term by 100, so as to make its scale comparable to the linear term. I.e.
I did the following.
varsq <- var*var/100
This is in fact unnecessary in the present case, given the scale of the
linear term. But anyway, with the division, R and Stata disagree:
beta s.e.
R: 5.939880 2.920165
Stata: 5.939747 5.455495
R: 11.228705 2.191625
Stata: 11.22761 4.630293
However, if I don't divide the quadratic term by 100, then R and Stata agree.
R: 0.05939645 0.05455490
Stata: 0.0593975 0.0545549
R: 0.11227038 0.04630296
Stata: 0.1122761 0.0463029
So it apprears that R might have some precision problem in calculating
the Hessian when the scale of a variable is very small. I talked to a
colleague, David Firth, about this, and he suggests
> Possibly it would be worth implementing an algebraic vcov method for
multinomial logit models [in R]?
Once again, many thanks to colleagues for your time and help.
Cheers. Wing
--
Department of Sociology, University of Oxford,
Littlegate House, St Ebbes, Oxford OX1 1PT, UK
tel: +44 (1865) 286176, fax: +44 (1865) 286171
http://users.ox.ac.uk/~sfos0006
More information about the R-help
mailing list