A follow up on the estimate differences. I discovered that I was not
changing tolerance and iterations correctly for S-Plus glm(). The
default low value of epsilon=0.00001 and maxit=10 were not being
changed like I thought. When these were changed to epsilon=1e-12 and
maxit=30, I obtained same estimates and se as the SYSTAT nonlinear
application. SAS parameter estimates were same but se for b0 was
larger (8.295) than for S-Plus or SYSTAT. Thanks to all who reponded.
Brian Cade
______________________________ Reply Separator _________________________________
Subject: Re: [S] model selection in glm - logit link vs identity
Author: Prof Brian D Ripley <ripley@stats.ox.ac.uk> at NBS-Internet-Gateway
Date: 1/27/99 10:19 PM
On Thu, 28 Jan 1999, John Maindonald wrote:
>
> On Wed, 27 Jan 1999, Brian Cade wrote:
> > (2)
> > An interesting issue occurred with estimating the logit link model in
> > 3 different packages, S-Plus 4.5, SAS, SYSTAT 7; different parameter
> > estimates or standard errors. Results below. Note that deviances
> > (RSS = 16.5365) and estimates of dispersion (MSE = 0.1323) were the
> > same for all packages. Anyone have any comment on what S-Plus is
> > doing differently than SAS and SYSTAT?
> >
> > S-Plus SAS Insight SYSTAT 7
> > glm( family=quasi(link glm (quasi,link=logit, Nonlinear
> > =logit,variance=const) (variance=normal, model
> > deviance) (loss = RSS,
> > Gauss-Newton)
> >
> > b0(se) -0.098 (6.707) -0.383 (8.295) -0.383 (6.767)
> >
> > b1(se) 0.002 (0.003) 0.002 (0.003) 0.002 (0.003)
> >
> > b2(se) -0.003 (0.002) -0.003 (0.002) -0.003 (0.002)
> >
> > b3(se) 0.557 (0.208) 0.567 (0.262) 0.567 (0.211)
> >
> > b4(se) -0.033 (0.009) -0.034 (0.011) -0.034 (0.009)
> >
> > SYSTAT nonlinear least squares parameter estimates are similar to
> > those from SAS glm but SYSTAT standard errors are more similar to
> > S-plus glm standard errors. Great discrepancy between SAS/SYSTAT b0
> > estimate and that of S-Plus is disturbing.
>
> Brian Ripley responded
>
> >Have these converged? Try changing the control tolerances to be
> >sure. Often the likelihood surface is very flat if you have highly
> >related or ineffective carriers, and the convergence criterion in
> >glm() is rather lax. Actually, I don't see that a difference of less
> >than 5% of an s.e. should disturb you.
>
> One does have to worry about the difference in b0. How do the fitted
> values for the three fits compare? I doubt that this is a convergence
> issue, because it ought to affect other parameters also. What are the
Why, please? The likelihood is clearly a great deal flatter in that one
direction: just look at the size of the standard errors. I just cannot
understand how you can know that the other coefficients `ought' to be
affected, so could you please point us to the relative theory that ensures
this.
I _have_ seen precisely this in a logistic regression due to lack of
convergence, and changing the convergence criteria did give much
closer agreement.
Please explain how I should act differently if two fits with a very
similar RSS have fitted coefficients varying by a few percent of a
standard error. What is the practical significance?
--
Brian D. Ripley, ripley@stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272860 (secr)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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