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.
Herv�.
--
Herv� CACI, MD, PhD
Child and Adolescent Psychiatry
Service de P�diatrie
H�pital de l'Archet 2
151, route de Saint Antoine de Ginesti�re
06202 Nice Cedex 3 -- FRANCE
Tel: 04 92 03 60 74
Fax: 04 92 03 60 81
email: [email protected] (at work)
[email protected] (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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