Odds ratio results

[Chapi]

Hello,

I am new to SAS, could you please help me how to generate exactly like in the below image results using proc logistic regression. I want to generate the odds ratio by categories in the variable and Confidence Intervals for the categories except the first category in the variables.  I am trying to predict a binary outcome and need the odds ratio table exactly like below. Please can someone help me with it. Below is the sample dataset.

Target	Education	Health_insurance	Living_arrangement	Age	Sex
0	-0.08264447	-0.677142541	0.979974336	-0.607323603	-1.04615533
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	-1.04615533
0	-0.08264447	1.055157965	-0.385145422	3.014553778	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	0.915907427	-1.04615533
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	-1.04615533
0	-0.08264447	1.055157965	-0.385145422	0.915907427	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	-1.04615533
0	-0.08264447	-0.677142541	-0.385145422	0.915907427	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	0.9539654
1	-0.08264447	1.055157965	-0.385145422	3.014553778	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	-1.04615533
1	-0.57348838	1.055157965	-0.385145422	-0.607323603	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	-1.04615533
1	-0.08264447	1.055157965	-0.385145422	0.915907427	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	-1.04615533
1	-0.08264447	-0.677142541	-0.385145422	-0.607323603	-1.04615533
0	-0.08264447	1.055157965	-0.385145422	-0.607323603	0.9539654
0	-0.57348838	-0.677142541	-0.385145422	-0.607323603	-1.04615533
0	-0.08264447	-0.677142541	-0.385145422	0.915907427	-1.04615533
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	0.9539654
1	-0.08264447	1.055157965	-0.385145422	0.915907427	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	3.014553778	-1.04615533
1	-0.08264447	-0.677142541	-0.385145422	0.915907427	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	0.9539654
0	-0.08264447	1.055157965	-0.385145422	3.014553778	0.9539654
1	-0.08264447	1.055157965	-0.385145422	3.014553778	-1.04615533
1	-0.08264447	1.055157965	-0.385145422	0.915907427	0.9539654
1	-0.08264447	1.055157965	0.979974336	-0.607323603	0.9539654
0	-0.08264447	1.055157965	-0.385145422	3.014553778	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	0.9539654
1	-0.08264447	1.055157965	0.979974336	0.915907427	-1.04615533
1	-0.08264447	-0.677142541	0.979974336	-0.607323603	-1.04615533
1	8.99941821	5.578245149	6.983458922	-0.607323603	0.9539654
1	-0.57348838	1.055157965	0.979974336	0.915907427	0.9539654
1	-0.08264447	-0.677142541	0.979974336	-0.607323603	0.9539654
0	-0.08264447	1.055157965	0.979974336	-0.607323603	0.9539654
1	-0.08264447	-0.677142541	-0.385145422	3.014553778	0.9539654
1	-0.08264447	1.055157965	0.979974336	-0.607323603	0.9539654
1	-0.08264447	-0.677142541	-0.385145422	-0.607323603	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	0.9539654
1	-0.08264447	1.055157965	-0.385145422	-0.607323603	-1.04615533
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	-1.04615533
0	-0.08264447	-0.677142541	0.979974336	-0.607323603	0.9539654
1	-0.08264447	1.055157965	0.979974336	-0.607323603	-1.04615533
0	-0.08264447	-0.677142541	0.979974336	0.915907427	0.9539654
1	-0.08264447	-0.677142541	-0.385145422	-0.607323603	0.9539654
1	-0.08264447	1.055157965	-0.385145422	3.014553778	-1.04615533
1	-0.08264447	1.055157965	0.979974336	-0.607323603	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	-1.04615533
1	-0.08264447	-0.677142541	0.979974336	-0.607323603	-1.04615533
0	-0.08264447	1.055157965	-0.385145422	0.915907427	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	0.9539654
1	-0.08264447	-0.677142541	-0.385145422	-0.607323603	0.9539654
1	-0.08264447	-0.677142541	-0.385145422	-0.607323603	0.9539654
0	-0.08264447	-0.677142541	-0.385145422	-0.607323603	-1.04615533
1	-0.57348838	-0.677142541	0.979974336	-0.607323603	-1.04615533
0	-0.08264447	-0.677142541	-0.385145422	0.915907427	0.9539654

[PeterClemmensen]

@Chapi can you provide the data you're working with or some of it?

[Chapi]

Hello,
Thank you. Attached the sample dataset.

[StatDave · Accepted solution]

If that is all the data you have, it is pretty sparse as evidenced by running this:

proc freq; 
table (Education	Health_insurance	Living_arrangement	Age	Sex)*Target; 
run;

Note the occurrences of only a single count in one of the predictor levels. So, you can filter those out cases and get results for the remaining data:

proc logistic ;
where education<8.99941821  and Health_insurance<5.578245149 and
Living_arrangement<6.983458922 and	Age<3.014553778;
class Target	Education	Health_insurance	Living_arrangement	Age	Sex;
model Target(event="1")=	Education	Health_insurance	Living_arrangement	Age	Sex;
run;

It's odd and unnecessary to have obviously categorical variables be numerically coded like this - I think you did some sort of thins with WOE, but that isn't needed. The above gives the odds ratios you want. You can always save them into a data set using an ODS OUTPUT statement if you want to do some other work with them.

[Chapi]

Thank you dave. It works perfect!!