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RE: st: RE: Ordinal logistic regression
From
"Lachenbruch, Peter" <[email protected]>
To
"'[email protected]'" <[email protected]>
Subject
RE: st: RE: Ordinal logistic regression
Date
Thu, 11 Nov 2010 09:36:56 -0800
It may be completely arbitrary, or there may be some standards in the field that say cutpoints are at 30 and 35. However, the original poster should realize that the category indicators are not considered as codes with equal spacing and that ologit takes that into account.
Another issue is that you can use a glm to select the distribution you want rather than forcing a linear regression. There may be some information that suggests a proper model in your field of research.
Tony
Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Neil Shephard
Sent: Thursday, November 11, 2010 8:29 AM
To: [email protected]
Subject: Re: st: RE: Ordinal logistic regression
On Thu, Nov 11, 2010 at 4:13 PM, Mary E. Mackesy-Amiti <[email protected]> wrote:
> I usually feel the same way about reducing information, but in some cases
> the clinically-relevant categories are of greater interest than the
> continuum.
Its completely arbitrary though. Besides which BMI isn't a robust
indicator of obesity as it doesn't work for people who are very fit
and have lots of well honed muscles (their BMI often puts them in the
"obese" category when they are anything but).
Plenty of information on why not to categorise continuous variables at
http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/CatContinuous
On Thu, Nov 11, 2010 at 4:20 PM, Nick Cox <[email protected]> wrote:
> How does obesity differ?
It doesn't, but clinicians seem to struggle with these concepts.
Neil
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