Logistic Procedure


The LOGISTIC procedure fits linear logistic regression models for discrete response data by the method of maximum likelihood. The logistic regression usually used for binary responses. The LOGISTIC procedure enables to specify categorical variables (In the below example it is var1) or continuous variables as explanatory variables.


ods listing close;
proc logistic data=data ;

/*var1 is the one categorical variable*/

class var1 (ref = 'Group1') / param = ref;

/*Here var1, var2 and var3 are the three independent variables and we also fix the
Level of significance*/
model dependent = var1 var2 var3 / rsquare alpha = .10;
/*Outputting the required outputs into a SAS dataset*/
ods output OddsRatios=or (where = (Effect eq "var1 Group2 vs. Group1"));
ods output ParameterEstimates =param;
ods listing;
You may also like:

Author: Dinesh Motkar – Clinical SAS Programmer at Genpro   As part of SAS programming, we often come across situations where we need to remove unwanted data or to locate specific rows from data. Performing this processing using where clause or statement along with index...

Read More
Introduction to Linear Mixed Model

Introduction to Linear Mixed Model   Author: Anoop Jose – Clinical SAS Programmer at Genpro Research In clinical trials, usually, we take multiple measurements from a subject at different time points. In the case of repeated measures or longitudinal data, multiple observations are collected from...

Read More
Clinical Data and Wearable Device : Future of Data Capturing

Clinical Data and Wearable Device : Future of Data Capturing   Author: Mr. Vinu C Raju – Clinical Statistical Programmer at Genpro   Have you ever wondered how your social networking app is suggesting a friend request for a person you met yesterday in a...

Read More
Our Products