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SAS 9.2, can you run this program for Multivariate logistic regression with Firth option in 9.2 and let me know the output?
Hi All,
I am trying to run logistic regression of y1 on X1 -- X13 variables
(included the attached dataset =93all2=94), where quasi-complete
separation of data points is detected. SAS 9.2 has proc logistic with
an option of Firth method of estimation in case of quasi-complete
separation. However, this option is not available in earlier
versions.
I have come across a macro called =93fl=94 ,it can be used on SAS 9.1
version. I have included my output below.
I want someone to run the following proc logistic with Firth option
code on SAS version 9.2, so that I can check whether the output of
proc logistic on SAS 9.2 with Firth option matches to my output with
=93fl=94 macro on SAS 9.1.3 version. So please check whether your
parameter estimates (highlighted) are as shown below.
Any help will be greatly appreciated!
Thanks
Z
******PGM for the Iput Ds********************
data all2;
input y1 x1 - x13 ;
cards;
0 1 1 3 3 3 1 0 1 1 1 0 1 5
0 1 1 5 4 4 1 1 1 1 1 1 1 3.7
0 1 1 3 4 6 1 1 1 1 0 1 1 3.75
0 1 2 5 3 6 1 1 1 1 0 1 1 6.2
0 1 2 4 3 6 1 1 1 1 0 1 1 1.6
0 1 2 5 3 6 1 1 1 1 0 1 1 5
0 1 5 5 3 6 1 1 1 1 0 1 1 1.5
0 1 2 5 3 6 1 1 1 1 0 1 1 5.2
0 1 2 5 3 6 1 1 1 1 0 1 1 7.7
0 1 2 5 3 6 0 1 0 1 0 1 1 2
0 1 1 4 3 6 1 1 1 1 0 1 1 2.6
0 1 2 4 3 6 1 1 1 1 0 1 1 1.2
0 1 2 4 3 6 1 1 1 1 0 1 1 1.65
0 1 2 5 3 6 1 1 1 1 0 1 1 4.7
0 1 2 5 3 6 1 1 1 1 0 1 1 3.2
0 1 4 3 3 6 1 1 1 1 0 1 1 1.45
0 1 1 3 3 7 1 1 1 1 1 1 1 3.8
0 1 5 3 2 7 0 1 0 1 0 0 1 2
0 1 5 3 2 8 1 1 1 1 0 1 1 5.1
0 1 1 2 2 8 1 1 1 1 0 1 1 6
0 0 2 5 3 10 1 1 1 1 0 0 1 4.2
0 1 4 5 3 10 1 1 0 1 0 1 1 3.1
0 1 2 5 4 10 1 1 1 1 0 0 1 7.5
0 1 2 5 3 10 1 1 1 1 0 1 1 4.8
0 1 5 5 3 10 1 1 0 1 0 0 1 4.6
0 0 5 5 3 10 1 1 0 1 0 0 1 4.7
0 1 2 6 3 12 1 1 0 0 1 0 1 8
0 0 1 2 3 13 1 1 0 0 1 1 0 6
0 1 1 2 3 15 1 1 1 1 1 1 1 7.2
1 0 1 4 3 1 1 1 1 1 1 1 1 5
1 1 1 3 3 1 1 1 1 1 1 1 1 3.4
1 0 1 2 5 1 1 1 1 1 1 1 1 11
1 0 1 4 4 1 1 1 1 1 1 1 1 7.8
1 0 1 2 3 1 1 1 1 1 1 1 1 5.5
1 0 1 2 4 1 1 1 1 1 1 1 1 5.9
1 0 1 5 4 1 1 1 1 1 1 1 1 4.6
1 0 4 4 4 1 1 1 1 1 1 1 1 5.9
1 0 1 4 3 1 1 1 1 1 1 1 1 3.8
1 0 1 1 4 1 1 1 1 1 1 1 1 4.4
1 0 1 2 4 1 1 1 1 1 1 1 1 11.1
1 0 1 2 4 1 1 1 1 1 1 1 1 7.5
1 0 1 4 5 1 1 1 1 1 1 1 1 8
1 0 1 4 4 1 1 1 1 1 1 1 1 8.3
1 1 1 3 4 2 1 1 1 1 1 1 1 11.1
1 1 1 4 3 2 1 1 1 1 1 1 1 8.9
1 1 1 4 3 2 1 1 1 1 1 1 1 7.4
1 1 4 4 4 2 1 1 1 1 1 1 1 5.9
1 0 1 3 4 2 1 1 1 1 1 1 1 11.9
1 0 2 6 4 2 1 1 1 1 1 1 1 11.9
1 1 5 3 3 2 1 1 1 1 1 1 1 7.7
1 1 1 3 4 2 1 1 1 1 0 1 1 8.8
1 1 3 5 3 2 1 1 1 1 1 1 1 5.5
1 1 1 2 3 2 1 1 1 1 1 1 1 6.3
1 0 1 2 3 2 1 1 1 1 1 1 1 11.6
1 1 1 3 4 2 1 1 1 1 1 1 1 7.85
1 1 1 2 3 3 1 0 1 1 1 1 1 5
1 1 3 5 3 3 1 0 1 1 0 1 1 2
1 1 1 3 4 3 1 0 1 1 1 1 1 4
1 1 3 4 3 3 1 0 1 1 1 1 1 2
1 1 1 2 4 3 1 0 1 1 1 1 1 2
1 1 1 1 4 3 1 0 1 1 1 0 1 3
1 1 1 2 4 3 1 0 1 1 1 1 1 5
1 1 1 3 3 4 1 1 1 1 1 1 1 6.5
1 1 1 6 3 4 1 1 1 1 1 1 1 5.2
1 1 1 2 4 4 1 1 1 1 1 1 1 9
1 1 1 2 3 4 1 1 1 1 1 1 1 1.2
1 1 1 3 3 4 1 1 1 1 1 1 1 6.1
1 1 1 1 3 4 1 1 1 1 1 1 1 3
1 1 1 2 4 4 1 1 1 1 1 1 1 4.7
1 1 1 3 4 4 1 1 1 1 1 1 1 7.7
1 1 2 5 4 4 1 1 1 1 1 1 1 6.8
1 1 1 4 4 4 1 1 1 1 1 1 1 5.3
1 1 1 5 3 4 1 1 1 1 1 1 1 4.5
1 1 1 3 4 4 1 1 1 1 1 1 1 3.2
1 1 1 3 4 4 1 1 1 1 1 1 1 5.7
1 1 2 5 4 4 1 1 1 1 1 1 1 7
1 1 2 5 4 4 1 1 1 1 1 1 1 6.8
1 1 2 5 3 4 1 1 1 1 1 1 1 7
1 1 4 4 3 5 1 1 1 1 0 1 1 12
1 0 1 2 4 5 1 1 1 1 0 1 1 12
1 1 1 3 4 5 1 1 1 1 0 1 1 12
1 1 1 3 3 5 1 1 1 1 0 1 1 8
1 1 1 3 4 5 1 1 1 1 0 1 1 11
1 1 1 2 4 5 1 1 1 1 0 1 1 12
1 1 1 4 4 5 1 1 1 1 0 1 1 12
1 1 1 4 2 5 1 1 1 1 0 1 1 10
1 1 1 4 4 5 1 1 1 1 0 1 1 10
1 0 1 2 4 5 1 1 1 1 0 1 1 12
1 0 1 1 5 5 1 1 1 1 0 1 1 12
1 1 4 5 4 5 1 1 1 1 0 1 1 8
1 1 3 4 3 5 1 1 1 1 0 1 1 5
1 1 3 4 3 5 1 1 1 1 0 1 1 10
1 1 1 2 3 5 1 1 1 1 0 1 1 9
1 1 4 5 4 5 1 1 1 1 0 1 1 11
1 1 1 3 4 5 1 1 1 1 0 1 1 12
1 1 1 2 4 5 1 1 1 1 0 1 1 10
1 1 1 4 4 5 1 1 1 1 0 1 1 9
1 1 1 2 3 6 1 1 1 1 0 1 1 2.4
1 1 4 3 3 6 1 1 1 1 0 1 1 3
1 1 1 2 3 7 0 1 0 1 0 0 1 3.2
1 1 2 5 3 7 1 1 1 1 1 1 1 2
1 1 4 5 2 7 1 1 1 1 1 0 1 1.9
1 1 2 6 1 7 1 1 1 1 0 1 1 4.6
1 1 2 6 3 7 1 1 1 1 0 1 1 4
1 1 1 2 4 8 1 1 1 1 0 1 1 6.1
1 1 1 2 3 8 1 1 1 1 0 1 1 6.9
1 1 1 3 4 8 1 1 1 1 0 1 1 4
1 1 1 2 4 8 1 1 1 1 0 1 1 5.6
1 1 2 5 3 8 1 1 1 1 0 1 1 5
1 1 5 3 3 8 1 1 1 1 0 1 1 5.9
1 1 1 3 4 8 1 1 1 1 0 1 1 4.9
1 1 1 2 2 8 1 1 1 1 0 1 1 7
1 1 5 3 3 8 1 1 1 1 1 1 1 7
1 1 2 4 3 8 1 1 1 1 0 1 1 7.9
1 1 1 2 4 8 1 1 1 1 0 1 1 10.9
1 1 2 4 4 8 1 1 1 1 0 1 1 9
1 1 1 2 5 8 1 1 1 1 0 1 1 6
1 1 1 2 3 8 1 1 1 1 0 0 1 7
1 1 2 4 4 8 1 1 1 1 0 1 1 10
1 1 1 2 4 8 1 1 1 1 0 0 1 6
1 1 2 3 3 8 1 1 1 1 0 1 1 7
1 1 1 2 3 8 1 1 1 1 0 1 1 6
1 1 1 1 4 9 1 0 0 0 1 1 1 12
1 1 1 3 3 9 1 0 0 0 0 1 1 7
1 1 1 2 4 9 1 0 0 0 0 1 1 8
1 1 4 3 4 9 1 0 0 0 0 1 1 12
1 1 2 5 3 9 1 0 0 0 0 1 1 11.2
1 1 4 3 4 9 1 0 0 0 0 1 1 10
1 1 1 2 5 9 1 0 0 0 0 1 1 10
1 1 4 2 4 10 1 1 1 1 0 1 1 7.3
1 1 1 2 4 10 1 1 1 1 0 1 1 5.7
1 1 1 2 4 10 1 1 0 1 1 1 1 5.9
1 1 1 3 3 10 1 1 1 1 0 0 1 3.7
1 0 4 3 4 10 1 1 1 1 0 0 1 9
1 0 4 3 4 10 1 1 1 1 1 0 1 8.2
1 1 2 4 3 10 1 1 1 1 0 0 1 5.1
1 1 4 3 4 10 1 1 1 1 0 0 1 6.9
1 0 4 4 4 10 1 1 1 1 0 1 1 7.5
1 1 4 2 3 10 1 1 1 1 0 1 1 7.7
1 1 1 3 4 10 1 1 1 1 0 0 1 9.1
1 1 1 2 4 10 1 1 1 1 0 0 1 9.9
1 1 2 5 4 10 1 1 1 1 0 1 1 10.9
1 1 5 5 3 10 1 1 0 1 0 0 1 4.4
1 1 1 2 3 10 1 1 0 1 0 0 1 9.2
1 1 1 3 4 11 1 1 1 1 1 0 1 5.4
1 1 1 3 3 11 1 1 1 1 1 0 0 4.2
1 1 1 2 4 11 1 1 1 1 1 1 1 5.4
1 0 1 2 5 11 1 1 1 1 1 0 1 7.4
1 1 1 2 3 11 1 1 1 1 1 1 1 4.6
1 1 1 2 3 11 1 1 1 1 1 0 1 2.7
1 1 2 6 4 11 1 1 1 1 1 0 1 4.9
1 1 2 5 3 11 1 1 1 1 1 0 1 4.6
1 1 2 6 3 11 1 1 1 1 1 0 1 2.7
1 1 2 5 5 11 1 1 1 0 1 0 1 4.9
1 1 2 5 4 11 1 1 1 1 1 0 1 2.9
1 1 1 2 3 11 1 1 1 0 1 0 1 2.8
1 0 1 3 4 11 1 1 1 1 1 0 1 5.1
1 1 1 2 3 11 1 1 1 1 1 0 1 4.7
1 1 1 3 3 11 1 1 1 1 1 0 1 3.3
1 1 1 2 3 11 1 1 1 0 1 0 1 3
1 1 2 6 3 12 1 1 0 0 1 1 1 5.9
1 1 4 4 3 12 1 1 0 0 0 0 1 4.7
1 1 2 5 3 12 1 1 0 0 0 0 1 7
1 1 4 3 3 12 1 1 0 0 0 0 1 3.55
1 1 4 4 3 12 1 1 1 0 1 0 1 4.8
1 1 4 3 3 12 1 1 0 0 0 0 1 8.5
1 1 4 5 4 12 1 1 0 0 0 0 1 9.1
1 1 2 6 4 12 1 1 0 0 0 0 1 5
1 1 4 5 4 12 1 1 0 0 0 0 1 6.5
1 1 5 4 3 12 1 1 0 0 0 0 1 6.4
1 0 1 5 4 13 1 1 0 0 1 1 0 6.55
1 1 1 5 3 13 1 1 0 0 1 1 0 6.6
1 1 4 4 3 13 1 1 0 0 1 1 0 6.6
1 1 1 3 4 13 1 1 0 0 1 1 0 10.95
1 0 1 3 4 13 1 1 0 0 1 1 0 10
1 1 1 4 3 13 1 1 0 0 1 1 0 4.9
1 0 1 3 4 13 1 1 0 0 1 1 0 11.9
1 1 5 5 4 13 1 1 0 0 1 1 0 6.75
1 1 1 4 3 13 1 1 0 0 1 1 0 4.95
1 0 5 2 4 13 1 1 0 0 1 1 0 4.6
1 0 1 3 4 13 1 1 0 0 1 1 0 8.95
1 0 1 3 4 14 1 1 1 0 1 1 1 11.9
1 1 1 2 3 14 1 1 1 0 1 1 1 9.5
1 0 1 3 3 14 1 1 1 0 1 1 1 11.8
1 1 1 2 3 14 1 1 1 0 1 1 1 11.9
1 1 1 3 3 14 1 1 0 0 1 1 1 11.4
1 1 1 3 4 14 1 1 1 0 1 1 1 8.4
1 1 3 3 3 14 1 1 1 0 1 1 1 7.1
1 1 1 3 3 14 1 1 1 0 1 1 1 6.8
1 0 1 3 4 14 1 1 1 0 1 0 1 11.9
1 0 1 3 4 14 1 1 1 0 1 1 1 11.3
1 1 1 2 3 15 1 1 1 1 1 1 1 8
1 1 1 2 3 15 1 1 1 1 1 0 1 8
1 0 1 3 4 15 1 0 1 1 0 1 1 8
1 1 1 2 3 15 1 0 1 0 1 1 1 4
1 1 1 2 4 15 1 1 1 1 1 1 1 10.2
1 1 1 2 4 15 1 1 1 1 1 1 1 7.9
1 1 1 2 3 15 1 1 1 1 1 1 1 9.9
1 1 1 2 4 15 1 1 1 1 1 1 1 8.9
1 0 1 2 3 15 1 1 1 1 1 1 1 12
1 1 4 4 3 15 1 1 1 1 1 1 1 6.9
1 1 4 4 4 15 1 1 1 1 1 1 1 13.9
1 1 4 4 3 15 1 1 1 1 1 1 1 6.9
1 1 5 6 3 15 1 1 1 1 1 1 1 8.9
1 1 1 3 3 15 1 1 1 1 1 1 1 8.9
1 1 4 3 4 15 1 1 1 1 1 1 1 7.9
1 1 5 5 4 15 1 1 1 1 1 1 1 6.9
;
run;
*****************PGM TO RUN THE LOGISITICS**********************;
proc logistic data=3Dall2 descending;
model y1 =3D x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13/ Firth ;
run;
******************MY OUPUT In 9.1.2 ********************************
FL estimates and Wald confidence limits and tests
NOTE: Confidence interval for Intercept based on Wald method.
Parameter Standard Lower Upper Pr >
Variable estimate Error 95% c.l. 95% c.l. Chi-
Square
INTERCEP -1.04880 2.89888 -6.73059 4.63300 0.7175
X1 0.83428 0.77116 -0.67720 2.34575 0.2793
X2 0.18789 0.20125 -0.20656 0.58234 0.3505
X3 -0.39930 0.19563 -0.78272 -0.01587 0.0412
X4 0.73415 0.44361 -0.13532 1.60362 0.0979
X5 -0.10415 0.08940 -0.27938 0.07108 0.2440
X6 -0.39542 1.67737 -3.68305 2.89222 0.8136
X7 -0.26214 1.05326 -2.32652 1.80224 0.8034
X8 1.10669 1.07982 -1.00975 3.22314 0.3054
X9 -1.64879 1.20394 -4.00852 0.71094 0.1708
X10 1.49376 0.54460 0.42634 2.56117 0.0061
X11 -0.75918 0.66722 -2.06693 0.54856 0.2552
X12 0.39412 1.34056 -2.23338 3.02162 0.7688
X13 0.38033 0.11357 0.15773 0.60294 0.0008
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 Zintie
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3/25/2011 5:58:01 PM |
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You also need to add CLPARM=WALD to the MODEL statement options.
The SAS System 09:44 Saturday, March 26, 2011 2
The LOGISTIC Procedure
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
x1 2.303 0.508 10.441
x2 1.207 0.813 1.790
x3 0.671 0.457 0.984
x4 2.084 0.873 4.971
x5 0.901 0.756 1.074
x6 0.673 0.025 18.027
x7 0.769 0.098 6.063
x8 3.024 0.364 25.106
x9 0.192 0.018 2.036
x10 4.454 1.532 12.951
x11 0.468 0.127 1.731
x12 1.483 0.107 20.523
x13 1.463 1.171 1.827
Association of Predicted Probabilities and Observed Responses
Percent Concordant 88.8 Somers' D 0.780
Percent Discordant 10.9 Gamma 0.782
Percent Tied 0.3 Tau-a 0.189
Pairs 5162 c 0.890
Wald Confidence Interval for Parameters
Parameter Estimate 95% Confidence Limits
Intercept -1.0485 -6.7302 4.6332
x1 0.8343 -0.6772 2.3457
x2 0.1879 -0.2065 0.5823
x3 -0.3993 -0.7827 -0.0159
x4 0.7341 -0.1353 1.6036
x5 -0.1041 -0.2794 0.0711
x6 -0.3957 -3.6832 2.8919
x7 -0.2621 -2.3265 1.8022
x8 1.1067 -1.0097 3.2231
x9 -1.6488 -4.0085 0.7109
x10 1.4938 0.4264 2.5611
x11 -0.7592 -2.0669 0.5485
x12 0.3941 -2.2333 3.0216
x13 0.3803 0.1577 0.6029
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3/26/2011 2:58:01 PM
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