[R] Multinomial logistic regression under R and Stata
ripley at stats.ox.ac.uk
Thu Mar 27 11:58:40 CET 2003
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
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
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.
Brian D. Ripley, ripley at stats.ox.ac.uk
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
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