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Re: st: predict after glm


From   Thomas Gschwend <Thomas.Gschwend@mzes.uni-mannheim.de>
To   statalist@hsphsun2.harvard.edu, statalist@hsphsun2.harvard.edu
Subject   Re: st: predict after glm
Date   Mon, 27 Mar 2006 13:45:35 +0200

I work with up-to-date SE version on a windows platform

       c(stata_version) = 9.1
             c(version) = 9.1                        (version)
    ---------------------------------------------------------------------------------------------------------------------------
           c(born_date) = "20 Jan 2006"
              c(flavor) = "Intercooled"
                  c(SE) = 1
                c(mode) = ""
             c(console) = ""
    ---------------------------------------------------------------------------------------------------------------------------
                  c(os) = "Windows"
               c(osdtl) = ""
        c(machine_type) = "PC"
           c(byteorder) = "lohi"


At 13:29 27.03.2006, Ronnie Babigumira wrote:
What version of Stata are you using? I cannot reproduce your problem (Im using Stata 9.1,
Born 20 Jan 2006)

Ronnie

Thomas Gschwend wrote:

I tried to replicate the glm postestimation example using the beetle.dta closely following the code presented in the Stata9 Reference manual A-J on page 424. I have two puzzeling observations.
1) the glm model (without the -quitely- option) prompts a warning message: "convergence not achieved" which does not occur when in addition -, irls- is specified.
2)-predict- and -mfx- yield the following error message: "Unknown function /()"
Anybody any ideas?
Thomas
...and the log is:
. use http://www.stata-press.com/data/r9/beetle
. glm r ldose, f(binomial n) l(logit)
initial: log likelihood = -250.49593
rescale: log likelihood = -250.49593
Iteration 0: log likelihood = -250.49593
Iteration 1: log likelihood = -221.91214
Iteration 2: log likelihood = -221.87694
Iteration 3: log likelihood = -221.87694
Generalized linear models No. of obs = 24
Optimization : ML Residual df = 22
Scale parameter = 1
Deviance = 363.6276384 (1/df) Deviance = 16.52853
Pearson = 372.2527216 (1/df) Pearson = 16.92058
Variance function: V(u) = u*(1-u/n) [Binomial]
Link function : g(u) = ln(u/(n-u)) [Logit]
AIC = 18.65641
Log likelihood = -221.8769365 BIC = 203.7421
------------------------------------------------------------------------------
| OIM
r | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ldose | 22.0699 1.203182 18.34 0.000 19.71171 24.42809
_cons | -39.7564 2.167708 -18.34 0.000 -44.00503 -35.50777
------------------------------------------------------------------------------
Warning: convergence not achieved
. predict mu_logit
Unknown function /()
r(133);
end of do-file
r(133);



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********************************************************************
Thomas Gschwend, PhD
Mannheimer Zentrum fuer Europaeische Sozialforschung (MZES)
University of Mannheim
68131 Mannheim
Germany
0621.181.2809 (voice)
0621.181.2845 (fax)
Thomas.Gschwend@mzes.uni-mannheim.de
http://www.sowi.uni-mannheim.de/lehrstuehle/lspol1/gschwend.htm



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