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Newsgroups: bit.listserv.stat-l
Date:         Tue, 26 Jan 1993 14:33:00 LCL
From:         CROSS <CROSS.CPC@MHS.UNC.EDU>
Subject:      summary of multinomial logit

On Tuesday, 1/19 I posted the following question to STAT-L:

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Users of multinomial logits:
  What software do you use and why?  Do any programs also calculate
probabilities for you?  Our center is looking for an alternative to PROC
MLOGIT which runs under version 5 of SAS.  Will post responses to the
list.  Thanks!

Catherine Cross
Carolina Population Center
CB#8120, University Square East
University of North Carolina
Chapel Hill, NC  27516-3997
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I received responses from 9 multinomial logit users over the past week.
SAS procedures were mentioned most frequently but I was surprised at
the wide variety of programs listed.  The following programs were
recommended:  SAS(PROC CATMOD-4 mentions; PROC LOGISTIC-2 mentions;
PROC PHREG), GLIM(2 mentions), LIMDEP(2 mentions), NOMINAL, SPIDA,
STATA, HOTZTRAN, LOGIT procedure under TSP, LOGIT procedure in QUANT
module in MARKOV (running under GAUSS), and LOGIT module of SYSTAT.
See responses below for details.  Thanks to everyone who replied!
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Response #1

Awhile back (pre SAS vers 6) I too was on a software hunt for the
multinomial logit (unordered categorical outcome) problem.  PROC CATMOD
worked if you knew the right tricks to use -- but the outcome was not
easily decipherable -- estimates were in a consistent but not readily
apparent order -- once you figured out how SAS ordered the coefficients
you could consistently decode the output.  I recently tried to run my
CATMOD tricks in the new version 6, but it seems they were specific to
version 5 -- I have tried to figure out how to translate the version 5
tricks into version 6, but have not yet succeeded.

I was on this software hunt about the time MLOGIT was released.  It
seemed to me that MLOGIT was great for ordered categorical outcomes,
which reflected the needs of the econometricians (as evidenced by the
references in the documentation).  For unordered MLOGIT wasnt all that
useful.

The easiest software to use, in my opinion, was the LOGIT procedure
in TSP (surprising, given that TSP is the domain of econometricians).
If you can get your data in a rectangular array, you simply use a few
TSP commands (use the @FIT options for fitted probabilities for the
observations in your data array -- if you want probabilities for
hypothetical values, it may require some work) and you have your model
estimately relatively quickly and with well labeled output.

At the time, the most sophisticated software for the problem was
called NOMINAL (obtained from Univ Laval in Quebec).  This was the
software used by Dubin and Pasternak in their very readable article in
AJE entitled "Risk Assessment for Case-Control Subgroups by
Polychotomous Logistic Regression", Am J Epi, 123(6): 1101-1117 (1986).
Since I was working in a biostatistical context this was a very useful
reference for me, and I tried to use the software.  However, I never
got it to run successfully (mostly due to the limitation of PCs at the
time -- should not be a problem now that we are in the 486 age).

Of course a "real" biostatistician would recommend GLIM -- it does it
all if you know what you want and how to program GLIM.

Hosmer and Lemeshow, in their book Applied Logistic Regression, give a
good explication of multinomial (aka polychotomous, aka polytomous),
but no software recommendations.  I have other references if you need
them, but that's about it for software.  Hope this is of at least some
help.

Ken Petronis
The Urban Institute
Washington, DC
"UI4300::PETRONIS"@ui.urban.org
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Response #2

You can use the PHREG procedure in version 6 of the SAS System to fit
multinomial logit models.  If you send me your postal address, I will
be glad to send you more information.  [And he did!]

Warren F. Kuhfeld
Statistical R & D
SAS Institute Inc.
Cary, NC 27513-2414
saswfk@unx.sas.com
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Response #3

SAS and SPIDA come to my mind!  In SAS, you can use PROC LOGISTIC and
CATMOD to fit multinomial logit models. You can specify different LINK
functions in PROC LOGISTIC to fit Proportional ODDS (cumulative logit
models) and Adjacent category (Continuation ratio models), for ORDINAL
(response) data. You can also use CATMOD to fit (among other forms of
logits) a POLYTOMOUS LOGIT models. I'm refering to SAS 6.07!

SPIDA is a relatively new software and can be used to fit (some of the
??) models mentioned above. I have not used SPIDA much, but have heard
from other users that its good.

Personally, I prefer to use SAS to fit multinomial models because I
find SAS to be excellent for data management.  (I don't want to switch
between softwares for data management and analyses).

Cande V Ananth
Department of Biostatistics, SPH
University of North Carolina at Chapel Hill
ananth@UNC.bitnet
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Response #4

PROC CATMOD in SAS does multinomial logit models quite nicely. It will
also output the predicted probabilities for each of C-1 logistic
equations for dependent variables having C categories. It can also
handle continuous independent variables via the DIRECT statement.

I believe I ran the same model in MLOGIT and CATMOD once some time ago
and compared the coefficients, log-liklihoods etc. If memory serves
correctly, the results were the same in both.

Hope this helps.

Kenneth A. Hardy
Assoc. Dir. for Information Systems
Inst. for Research in Social Science
Manning Hall CB#3355
University of North Carolina
Chapel Hill, N.C.  27599-3355
KENHARDY@UNCVM1.BITNET
KHARDY.IRSS@MHS.UNC.EDU
KENHARDY@UNCVM1.OIT.UNC.EDU
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Response #5

I've used LIMDEP fairly successfully, and I believe that version 6 of
SAS has this available as well -- I don't have my manuals handy, but I
remember seeing it in SAS/STAT

George Jakubson
CORNELL UNIVERSITY
AK5J@CORNELLA.CIT.CORNELL.EDU
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Response #6

I haven't done multinomial logit analysis myself, but I understand that
GLIM is an excellent program for carrying out such analyses.

Clive Payne at Oxford University (Nuffield College) is a GLIM
enthusiast, and uses it for loglinear and logit analyses - and is
usually willing to help with advice.  His e-mail address is:
nuff@oxford.ac.uk.

Stephen Gourlay
Senior Lecturer
Kingston University
bs_s467@neptune.king.ac.uk
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Response #7

I generally use Limdep 6.0 on the PC.  UNC has Limdep on the mainframe,
too.  I like the PC version better than the mainframe.  I also use
Stata, which I am getting to like more and more as I get familiar with
it.  It is written for both Unix and DOS platforms.  I have used both
and find them to be very easy to use.  The Intercooled DOS version
(written for 386 based machines with at least three megabytes of
memory) is unbelievably fast computation-wise.  The 386 version of
Limdep is very fast, too.

David Guilkey is quite familiar with both, you could probably talk to
him about it.  Also available at UNC is Hotztran, which is not such a
friendly program.  In any case, Guilkey knows about as much about
limited dependent variable computation as anyone on the planet, so you
could get the best info from him.

Cheers,

George McCarthy
MCCARTHY@LEVY.BARD.EDU
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Response #8

In addition to SAS PROC CATMOD and PROC LOGISTIC you can try the LOGIT
procedure in the QUANT module in MARKOV (running under GAUSS).  Use the
utility program ATOG.EXE to create a GAUSS datafile from an ASCII file
(see Chapter 17 in the GAUSS System and Graphics Manual).  MARKOV is
very easy to use.  GAUSS is not.  Get in touch with Ken Hardy at IRSS
to get access to the program.  It should be accessible on the social
sciences network server.  The program is not supported by the IRSS
staff, so you'll have to learn it yourself.  However, last year there
was a statistics group comprised of faculty, staff, and grad students
at IRSS which partly was intended as a GAUSS user group.  It was
organized by Walt Davis, a sociology graduate student.

Good luck,
Magnus

MAGNUS STENBECK
mastfoh%tellus.sos.se%sos@mail.swip.net
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Response #9

The LOGIT module of SYSTAT also fits multinomial logit models for a
categorical response variable that is not ordinal.  Agresti's book
Categorical Data Analysis (1990 edition) gives an overview of extant
computer programs for the different types of models.

Jacqui Cater
IEGH71U@tjuvm.tju.edu
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