I think there is some value in having the incomplete estimation results
available after some number of iterations because they can still be used
to form a linear classifier that completely separates the groups. Even
though the magnitude of the coefficients grows arbitrarily and standard
errors are meaningless in such cases, the relative values of the
coefficients appear stable if more than one is nonzero. I wonder if it's
possible to control the estimation when there is perfect separation by
imposing some sort of restriction such as b'b = 1?
Al Feiveson
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Richard
Williams
Sent: Monday, May 05, 2008 6:14 PM
To: [email protected]; [email protected]
Subject: Re: st: RE: identifying perfect outcome predictor
At 01:13 PM 5/5/2008, Newson, Roger B wrote:
>I personally use -glm- (with the options -link(logit) family(bin)-)
>instead of -logit-. That way, the offending parameters are allowed to
>"converge" to plus or minus infinity without an error message. And the
>guilty parameters are then displayed for all to read.
>
>I hope this helps.
>
>Roger
Interesting. Now, can that be considered a "bug" or a "feature" of glm?
My own oglm and gologit2 programs give these phenomenally high standard
errors when you have perfect outcome predictors, mostly because I've
never figured out how to trap such situations and issue a warning or
error message.
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME: (574)289-5227
EMAIL: [email protected]
WWW: http://www.nd.edu/~rwilliam
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