Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down on April 23, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
"Barth Riley" <BarthRiley@comcast.net> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Classification table from mlogit |

Date |
Mon, 22 Mar 2010 10:20:13 -0500 |

Thanks, that helps! Barth -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten buis Sent: Monday, March 22, 2010 9:31 AM To: statalist@hsphsun2.harvard.edu Subject: Re: st: Classification table from mlogit --- On Mon, 22/3/10, Barth Riley wrote: > Is there something analogous to lstat, which is used for > producing a classification table after calling the > logistic procedure, for mlogit? It is possible to something similar for -mlogit- by assigning someone to a category for which (s)he has the highest predicted probability, rather than assigning a person to a category for which her/his predicted probability exceeds 50%. *---------------------- begin example ------------------------ sysuse auto, clear recode rep78 1/2=3 mlogit rep78 foreign mpg price predict pr* tokenize `e(eqnames)' gen yhat = cond(pr1 == max(pr1,pr2,pr3), `1', /// cond(pr2 == max(pr1,pr2,pr3), `2', `3')) if e(sample) tab rep78 yhat *------------------------ end example ------------------------ However, I would be very careful when using such a method. These tables are notoriously sensitive to the marginal distribution of your dependent variable. If one category is very common, than you can obtain quite a good looking score by assigning everybody to that category. Any additional information from explanatory variables will only marginaly improve your "fit". Explanatory variables will have a much biger "impact" on getting the prediction righ when the marginal proportions of belonging to a category are approximately equal. It is however doubtful if such a difference is substantively meaningful, as this difference comes only from how much variation a baseline model has left unexplained. So, this sensitivity to the marginal distribution of your depedent variable is most of the time not a desirable characteristic for a fit-statistic. Hope this helps, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Classification table from mlogit***From:*"Barth Riley" <BarthRiley@comcast.net>

**Re: st: Classification table from mlogit***From:*Maarten buis <maartenbuis@yahoo.co.uk>

- Prev by Date:
**st: RE: re: question on XTOVERID** - Next by Date:
**st: RE: RE: Estimating a Log Periodic Power Law model with some constraints** - Previous by thread:
**Re: st: Classification table from mlogit** - Next by thread:
**st: re: question on XTOVERID** - Index(es):