s-news
[Top] [All Lists]

Re: [S] model selection in glm - logit link vs identity

To: s-news@wubios.wustl.edu
Subject: Re: [S] model selection in glm - logit link vs identity
From: John Maindonald <john.maindonald@anu.edu.au>
Date: Thu, 28 Jan 1999 08:54:21 +1100 (EST)
Sender: owner-s-news@wubios.wustl.edu
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
ranges of values of x1, x2, x3 and x4.  (One wants an idea of the
relative effects of changing b0, b1, b2, b3, b4 on the model fit.)
I take it that surv is either a 0/1 variable or a matrix; you have
not specified weights.

John Maindonald               email : john.maindonald@anu.edu.au        
Statistical Consulting Unit,  phone : (6249)3998        
c/o CMA, SMS,                 fax   : (6249)5549  
John Dedman Mathematical Sciences Building
Australian National University
Canberra ACT 0200
Australia
-----------------------------------------------------------------------
This message was distributed by s-news@wubios.wustl.edu.  To unsubscribe
send e-mail to s-news-request@wubios.wustl.edu with the BODY of the
message:  unsubscribe s-news

<Prev in Thread] Current Thread [Next in Thread>