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From |
Richard Williams <richardwilliams.ndu@gmail.com> |

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

Subject |
Re: st: mlogit problem |

Date |
Sun, 17 Feb 2013 21:13:03 -0500 |

At 08:47 PM 2/17/2013, William Buchanan wrote:

Especially with so few observations, you should really consider amuch more parsimonious model. In terms of variable selection, whathas the literature in your area found previously? If you havelittle, or no, theoretical justification for including the variablein the model how would you judge whether a significant relationshipis an artifact in your data rather than a truly significantpredictor? You could also consider using data reduction techniquesto create composite scores of some of your RHS variables. Yourquestion, and the details that you've provided, are really far toobroad for any useful advice beyond going back to the drawing boardwhile keeping the principal of parsimony in mind.HTH, Billy

Sent from my iPhone On Feb 17, 2013, at 16:31, saqlain raza <bhatti_sb@yahoo.com> wrote:> Thanks JVerkuilen for your response. N=360. Yes I want to dovariables selection for my study. If this is not a good idea, what should I do?> Thanks again for your cooperation > > Saqlain RAZA > PhD Researcher > > > > ----- Original Message ----- >> From: JVerkuilen (Gmail) <jvverkuilen@gmail.com> >> To: statalist@hsphsun2.harvard.edu >> Cc: >> Sent: Sunday, February 17, 2013 4:07 PM >> Subject: Re: st: mlogit problem >> >> On Sun, Feb 17, 2013 at 6:46 AM, saqlain raza <bhatti_sb@yahoo.com> wrote: >>> Hello, >>> I am trying to fit -mlogit- with aroung 60 covariates (discrete and>> continous) initially to see the significnat variables. Mydependent variable has>> four alternate choices. Upto 44 covariates it converges. But, ifI add one more>> variable, iteration process starts and at the end, the result isnot converged.>> Any help will be highly acknowleged. >>> >>> Saqlain >>> PhD Researcher >> >> You need to indicate your N but you are running a probably unfittable >> model. With 60 covariates in an mlogit model you have 180 parameters >> and the chances of a non-concave likelihood, collinearity problems or >> perfect prediction become higher and higher. This is simply going to >> fail. I'd really go back to rethink your problem as looking at >> statistical significance is usually not a very good way to do variable >> selection anyway. >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/faqs/resources/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/faqs/resources/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/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

------------------------------------------- Richard Williams, Notre Dame Dept of Sociology OFFICE: (574)631-6668, (574)631-6463 HOME: (574)289-5227 EMAIL: Richard.A.Williams.5@ND.Edu WWW: http://www.nd.edu/~rwilliam * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: mlogit problem***From:*saqlain raza <bhatti_sb@yahoo.com>

**Re: st: mlogit problem***From:*"JVerkuilen (Gmail)" <jvverkuilen@gmail.com>

**Re: st: mlogit problem***From:*saqlain raza <bhatti_sb@yahoo.com>

**Re: st: mlogit problem***From:*William Buchanan <william@williambuchanan.net>

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