Perhaps you should ask Stata how it finds its estimates, and why it disagrees with R?
R uses the observed information matrix for the standard errors. It is also possible to use the expected (Fisher) information matrix. Where they differ, the observed one is generally regarded as a better choice, especially when as here the curvature is measured over a reasonably-sized neighbourhood. Intercepts often depend on coding, and you should cross-check the coding. More generally, such differences can be caused by the Hauck-Donner effect and lack of convergence, so it is almost always worth playing with the convergence criteria. On Thu, 27 Mar 2003, Tak Wing Chan wrote: > Dear Colleagues > > I have been fitting some multinomial logistic regression models using R > (version 1.6.1 on a linux box) and Stata 7. Although the vast majority > of the parameter estimates and standard errors I get from R are the same > as those from Stata (given rounding errors and so on), there are a few > estimates for the same model which are quite different. I would be most > grateful if colleagues could advise me as to what might be causing this, > and should I worry ... > > Anyway, with R, I have been using the function multinom under the > package nnet. Below are two examples where the estimates for standard > error differ substantially between R and Stata: > > beta s.e. > R: 5.939880 2.920165 > Stata: 5.939747 5.455495 > > R: 11.228705 2.191625 > Stata: 11.22761 4.630293 > > The parameters concerned are the quadratic term of a quantitative > variable (measuring social status). I notice that the s.e. for this > quadratic term are large anyway compared to other s.e. in the model. > > There are other differences between R and Stata, and these concerned the > intercept terms. Here is an example: > > beta s.e. > R: 0.2870793 0.4512347 > Stata: -0.2109653 0.5053566 > > Since both estimates are not significantly different from zero, I trust > I can ignore the difference between the estimates. Or could I? > > Many thanks in advance for any help. Please let me know if I should > provide further info. > > With best wishes. > > Wing > > > -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
