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st: Problem with logit


From   Hervé CACI <hcaci@wanadoo.fr>
To   STATALIST <statalist@hsphsun2.harvard.edu>
Subject   st: Problem with logit
Date   Wed, 06 Aug 2003 23:55:37 +0200

Dear statalisters,

I ran a couple of logit/logistic regressions to predict a dichotomous
variable "y" (0 = Control subject, 1 = Suicide Attempter) using different
sets of personality traits. The sample size is very limited N=2*15 subjects.

Can someone explain the following output, and diagnose the problem ?

Thank you very much in advance.
Hervé.
-- 
Hervé CACI, MD, PhD
Child and Adolescent Psychiatry
Service de Pédiatrie
Hôpital de l'Archet 2
151, route de Saint Antoine de Ginestière
06202 Nice Cedex 3 -- FRANCE
Tel: 04 92 03 60 74
Fax: 04 92 03 60 81
email: caci.h@chu-nice.fr (at work)
       hcaci@wanadoo.fr   (at home)
Web: http://perso.wanadoo.fr/herve.caci


. logistic SAMPLE pa na

Logit estimates                                 Number of obs   =         30
                                                LR chi2(2)      =      41.59
                                                Prob > chi2     =     0.0000
Log likelihood = -1.689e-07                     Pseudo R2       =     1.0000

----------------------------------------------------------------------------
   SAMPLE_T0 | Odds Ratio   Std. Err.      z    P>|z|    [95% Conf.Interval]
-------------+--------------------------------------------------------------
          pa |   5.81e-26   5.03e-22    -0.01   0.995            0         .
          na |   3.90e+57   7.64e+61     0.01   0.995            0         .
----------------------------------------------------------------------------

note: 14 failures and 13 successes completely determined.

. logit SAMPLE pa na

Iteration 0:   log likelihood = -20.794415
Iteration 1:   log likelihood = -8.9274011
Iteration 2:   log likelihood = -6.3319885
Iteration 3:   log likelihood = -4.9662853
Iteration 4:   log likelihood = -4.1380091
Iteration 5:   log likelihood = -3.5844148
Iteration 6:   log likelihood = -2.9739135
Iteration 7:   log likelihood = -2.2594929
Iteration 8:   log likelihood = -1.6925544
Iteration 9:   log likelihood = -1.0983049
Iteration 10:  log likelihood =  -.5188107
Iteration 11:  log likelihood = -.21385608
Iteration 12:  log likelihood = -.07698551
Iteration 13:  log likelihood = -.02772706
Iteration 14:  log likelihood = -.01011442
Iteration 15:  log likelihood = -.00370927
Iteration 16:  log likelihood = -.00136299
Iteration 17:  log likelihood = -.00050121
Iteration 18:  log likelihood = -.00018435
Iteration 19:  log likelihood = -.00006782
Iteration 20:  log likelihood = -.00002495
Iteration 21:  log likelihood = -9.178e-06
Iteration 22:  log likelihood = -3.376e-06
Iteration 23:  log likelihood = -1.242e-06
Iteration 24:  log likelihood = -4.569e-07
Iteration 25:  log likelihood = -1.578e-07
Iteration 26:  log likelihood = -1.553e-07
Iteration 27:  log likelihood = -1.541e-07
Iteration 28:  log likelihood = -1.538e-07

Logit estimates                                 Number of obs   =         30
                                                LR chi2(2)      =      41.59
                                                Prob > chi2     =     0.0000
Log likelihood = -1.689e-07                     Pseudo R2       =     1.0000

----------------------------------------------------------------------------
   SAMPLE |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------+-----------------------------------------------------------------
       pa |  -58.10766   8658.502    -0.01   0.995    -17028.46    16912.24
       na |   132.6071   19606.43     0.01   0.995    -38295.29     38560.5
    _cons |  -510.9476    75810.8    -0.01   0.995    -149097.4    148075.5
----------------------------------------------------------------------------

note: 14 failures and 13 successes completely determined.



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