st: Multinomial logistic regression under R and Stata

[Tak Wing Chan <tw.chan@sociology.oxford.ac.uk>]

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

ps. I have posted this query to R-help already. But following the advice 
of a colleague, I am curious to know the views from colleagues in the 
Stata community ...

--
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