A couple of examples using these procedures are also included. The training proc requires the Gauss Maximum Likelihood application module. The example programs can be executed simply by running this file in Gauss. The code is provided without guarantee for public, non-commercial use. The author thanks Gary King for helpful discussions, and NSF for supporting her visit at Harvard where the code was written.
First column is dependent var, and the next k columns are independent vars.
See the example program for more details. Setting it to 0 causes the default 1e-4 to be used. Same as that returned by the maxlik procedure. The estimated parameters themselves are relatively more meaningful, too when all input vars are of similar scales. DO NOT normalize y for binary data.