Statalist


[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

st: RE: R: RE: testing for overdispersione after Poisson Regression


From   "Carter Rees" <carterrees@gmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: R: RE: testing for overdispersione after Poisson Regression
Date   Fri, 1 Feb 2008 14:28:23 -0500

Carlos,

Check out the annotated negative binomial output on the UCLA stats portal:
http://www.ats.ucla.edu/stat/stata/output/stata_nbreg_output.htm

The definition of alpha in the Parameter Estimates section may be what you
are after.

Carter

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Carlo Lazzaro
Sent: Friday, February 01, 2008 2:02 PM
To: statalist@hsphsun2.harvard.edu
Subject: st: R: RE: testing for overdispersione after Poisson Regression


Dear Nick,
Thanks a lot for your kindness and for your time.

For sake of completeness, I report below all the routine I have performed.
My purpose was to test for the propriety of Poisson regression in dealing
with the issue of which indvars should be considered relevant in explaining
the increased or reduced undergoing to hospital visit. 
After the result of post estimation, I am not clear whether I should better
switch to a negative binomial regression.

Thanks a lot again and Kind Regards,

Carlo
----------------------------------------------------------------------------
poisson ___hospvisit ___drvisit ofnp opp opnp emr exchlth poorhlth numchron
adldiff noreast midwe
> st west age black male married school faminc employed privins medicaid

Iteration 0:   log likelihood = -15443.546  
Iteration 1:   log likelihood = -9957.3521  (backed up)
Iteration 2:   log likelihood = -7079.7823  
Iteration 3:   log likelihood = -3293.7693  
Iteration 4:   log likelihood = -2791.9417  
Iteration 5:   log likelihood = -2773.1699  
Iteration 6:   log likelihood =  -2772.855  
Iteration 7:   log likelihood = -2772.8545  
Iteration 8:   log likelihood = -2772.8545  

Poisson regression                                Number of obs   =
4406
                                                  LR chi2(21)     =
1063.31
                                                  Prob > chi2     =
0.0000
Log likelihood = -2772.8545                       Pseudo R2       =
0.1609

----------------------------------------------------------------------------
--
___hospvisit |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
  ___drvisit |    .031564   .0025853    12.21   0.000     .0264969
.0366311
        ofnp |  -.0035584   .0036833    -0.97   0.334    -.0107775
.0036608
         opp |    .009755   .0050281     1.94   0.052    -.0000998
.0196098
        opnp |   .0085071   .0049302     1.73   0.084    -.0011558
.0181701
         emr |   .3080027   .0136605    22.55   0.000     .2812287
.3347768
     exchlth |    -.61551   .1764821    -3.49   0.000    -.9614086
-.2696114
    poorhlth |   .2268273   .0761728     2.98   0.003     .0775312
.3761233
    numchron |   .1681921   .0190159     8.84   0.000     .1309217
.2054626
     adldiff |   .1914079   .0698929     2.74   0.006     .0544204
.3283954
     noreast |  -.0116473    .082506    -0.14   0.888    -.1733561
.1500614
     midwest |   .1485223   .0726288     2.04   0.041     .0061724
.2908723
        west |   .0275293   .0823022     0.33   0.738      -.13378
.1888387
         age |   .1960624    .045575     4.30   0.000     .1067371
.2853877
       black |   .0360653   .0935412     0.39   0.700    -.1472722
.2194028
        male |   .1517583   .0644441     2.35   0.019     .0254502
.2780663
     married |    .000077   .0673596     0.00   0.999    -.1319454
.1320993
      school |   .0047826   .0083723     0.57   0.568    -.0116268
.0211919
      faminc |   .0051941   .0100271     0.52   0.604    -.0144586
.0248468
    employed |   -.076601     .10922    -0.70   0.483    -.2906683
.1374663
     privins |   .1028314   .0828429     1.24   0.215    -.0595376
.2652005
    medicaid |   .1541872   .1045082     1.48   0.140     -.050645
.3590195
       _cons |  -3.734123   .3769326    -9.91   0.000    -4.472897
-2.995348
----------------------------------------------------------------------------
--

. estat gof

         Goodness-of-fit chi2  =  3599.159
         Prob > chi2(4384)     =    1.0000

. estat gof, pearson

         Goodness-of-fit chi2  =  5438.241
         Prob > chi2(4384)     =    0.0000
----------------------------------------------------------------------------
-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Nick Cox
Inviato: venerd́ 1 febbraio 2008 19.41
A: statalist@hsphsun2.harvard.edu
Oggetto: st: RE: testing for overdispersione after Poisson Regression

Seems as if neither is! The key is surely what other output you have,
including any indications of major or minor problems. 

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Carlo Lazzaro

testing for overdispersion after a Poisson regression,

I have obtained two opposite results:


estat gof

         Goodness-of-fit chi2  =  3599.159
         Prob > chi2(4384)     =    1.0000

. estat gof, pearson

         Goodness-of-fit chi2  =  5438.241
         Prob > chi2(4384)     =    0.0000

Which one is trustworthy??


*
*   For searches and help try:
*   http://www.stata.com/support/faqs/res/findit.html
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/



*
*   For searches and help try:
*   http://www.stata.com/support/faqs/res/findit.html
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/


*
*   For searches and help try:
*   http://www.stata.com/support/faqs/res/findit.html
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/



© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index