|Date: ||Tue, 6 Sep 2011 13:04:10 -0400|
|Reply-To: ||R B <firstname.lastname@example.org>|
|Sender: ||"SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>|
|From: ||R B <email@example.com>|
|Subject: ||Re: Set static beta weight in GENLIN (a Monte Carlo study)|
It's uncommon to incorporate an offset into a logistic regression equation.
Typically, an offset is used for Poisson or Negative Binomial regression
equations to account for varying degrees of exposure. To do so for a
Poisson or Negative Binomial regression equation, typically the natural log
of the original variable is entered as an "offset" into the equation. This
makes sense given the log-link function which is used for Poisson and
Negative Binomial models. More can be said on this topic, but that is not
Anyway, back to your original question...If you enter a variable in its
original form as an "offset" in logistic regression via GENLIN, then you are
setting the beta coefficient to 1.0 as follows:
logit(y) = b0 + b1*x1 + b2*x2 + ... + 1.0*offset
Why you would want to set the regression coefficient to 1.0 for a variable
in a logit equation is beyond me. Before treating a variable as an offset,
at the very least I would start by entering the variable as a covariate to
see whether the estimate of the coefficient is in fact near 1.0.
On Tue, Sep 6, 2011 at 1:23 AM, J. R. Carroll <firstname.lastname@example.org> wrote:
> We're using GENLIN to estimate a logistic regression model. We want to test
> the fit of a model where we input the beta weight for a particular predictor
> rather than having it estimated. We ran a model where we set the value of
> the beta for that predictor equal to 1, by designating it as the "offset"
> variable. Is this achieving our goal? It seems to be, but we wanted to run
> it by the list.
> J. R. Carroll
> Researcher for Hurtz Labs
> Instructor at California State University, Sacramento
> Research Methods, Test Development, and Statistics
> Cell: (916) 628-4204
> Email: email@example.com