Re: AW: st: maximum number of outcomes in mlogit

[Steven Samuels <sjhsamuels@earthlink.net>]

Michael:

I am not sure that this is a task for -mlogit- or -mprobit- .  You  
have reduced the data to n=360 observations, each a vector of means.   
You grouped the 360 occupations, "by hand" into 54 groups and did a  
discriminant analysis, which confirms that the groups are separated.   
I agree that this is tautological.

So, I am not sure how to address your question (1) with this reduced  
set of data.  You can approach question (2), perhaps, with a cluster  
analysis.

To answer(1), you might have held out a sample of the original  
individuals from each occupation; created occupational groups as best  
you could; then tested how accurately you would classify the  
individuals in the hold-out sample into the groups.

-Steve

On May 21, 2008, at 4:08 AM, Tiemann, Michael wrote:


> Hello again,
>
> you are all too right, this is a lot of alternatives. Maybe I can  
> clarify why this is so.
> The alternatives are a set of groups of occupations, which are  
> coded according to a german classification of occupations. So I  
> definitely do not have a factorial structure. The information on  
> the occupations has been aggregated from individual data (a  
> microcensus file with more than 200.000 cases). What I now have is  
> a matrix with information on independent variables (as means over  
> those respondents included in each occupation) and the occupations  
> themselves, which amount to around 360. Technically, there are now  
> 360 cases (occupations) and 80 independent variables.
> With these data the abovementioned groups of occupations were  
> formed, though "by hand", following the idea that the independent  
> variables can be used to correctly classify the occupations. What I  
> want to do now is to find out whether this assumption really holds.  
> I have already done a discriminant analyses, but that mainly tells  
> me how well we have applied our theoretically derived aggregational  
> rules. Of course, it also tells me that the independent variables  
> can be used to discriminate the occupations in the way we did. But  
> this must be the case -- it is somewhat tautologic.
> With the mnlm I want to do two things: 1) find out whether the  
> independent variables can be used to discriminate the occupations  
> and 2) find out (which the -mlogtest, combine- would do nicely)  
> whether there are gruops that might have been combined. Does this  
> make anything clearer?
>
> Meanwhile, I will have a look at -glm-, -ipf- and -mprobit-.
>
> Thanks for your answers so far,
>
> Michael.
>
> _______________________________________
> Michael Tiemann
>
> Bundesinstitut f�r Berufsbildung (BIBB)
> Arbeitsbereich 2.2 "Qualifikation, berufliche Integration und  
> Erwerbst�tigkeit"
> Robert-Schuman-Platz 3
> 53175 Bonn
>
> Telefon: 0228 / 107-1235
> E-Mail: tiemann@bibb.de
> -----Urspr�ngliche Nachricht-----
> Von: owner-statalist@hsphsun2.harvard.edu [mailto:owner- 
> statalist@hsphsun2.harvard.edu] Im Auftrag von Richard Williams
> Gesendet: Mittwoch, 21. Mai 2008 06:08
> An: statalist@hsphsun2.harvard.edu; statalist@hsphsun2.harvard.edu
> Betreff: RE: st: maximum number of outcomes in mlogit
>
> At 08:15 PM 5/20/2008, jverkuilen wrote:
>
> > > I want to estimate a multinomial logistic model with mlogit. But
> > my dependent variable holds 54 categories. Now, when I try to run
> > mlogit I am informed there are too many categories.>
> >
> > Holy cow, that's a lot of alternatives. I am with Maarten on this,
> > unless you have a truly huge (and amazingly well-conditioned) dataset
> > you are going to be getting a lot of perfect prediction.
> >
> > JV
> I agree.  Having said that, I wonder if mprobit would work?  I  
> don't see a documented limit on the # of categories.  Of course,  
> you might have to wait a few months for it to finish running.
>
>
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
> OFFICE: (574)631-6668, (574)631-6463
> HOME:   (574)289-5227
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> WWW:    http://www.nd.edu/~rwilliam
>
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