Example 17.2 Conditional Logit and Data Conversion

In this example, data are prepared for use by the MDCDATA statement. Sometimes, choice-specific information is stored in multiple variables. Since the MDC procedure requires multiple observations for each decision maker, you need to arrange the data so that there is an observation for each subject-alternative (individual-choice) combination. Simple binary choice data are obtained from Ben-Akiva and Lerman (1985). The following statements create the SAS data set:

data travel;
   length mode $ 8;
   input auto transit mode $;
datalines;
52.9   4.4 Transit
4.1   28.5 Transit
4.1   86.9 Auto
56.2  31.6 Transit
51.8  20.2 Transit
0.2   91.2 Auto
27.6  79.7 Auto
89.9  2.2  Transit
41.5  24.5 Transit
95.0  43.5 Transit

   ... more lines ...   

The travel time is stored in two variables, auto and transit. In addition, the chosen alternatives are stored in a character variable, mode. The choice variable, mode, is converted to a numeric variable, decision, since the MDC procedure supports only numeric variables. The following statements convert the original data set, travel, and estimate the binary logit model. The first 10 observations of a relevant subset of the new data set and the parameter estimates are displayed in Output 17.2.1 and Output 17.2.2, respectively.

data new;
   set travel;
   retain id 0;
   id+1;
   /*-- create auto variable --*/
   decision = (upcase(mode) = 'AUTO');
   ttime = auto;
   autodum = 1;
   trandum = 0;
   output;
   /*-- create transit variable --*/
   decision = (upcase(mode) = 'TRANSIT');
   ttime = transit;
   autodum = 0;
   trandum = 1;
   output;
run;

proc print data=new(obs=10);
   var decision autodum trandum ttime;
   id id;
run;

The following statements perform the binary logit estimation:

proc mdc data=new;
   model decision = autodum ttime /
            type=clogit
            nchoice=2;
   id id;
run;