Modeling and Multivariate Methods - SAS

Chapter 16 Performing Choice Modeling 409
Launch the Choice Platform and Select Data Sets
If you are scripting the Choice platform, you can also set the acceptable criterion for convergence when
estimating the parameters by adding this command to the Choice() specification:
Choice( ..., Convergence Criterion( fraction ), ... )
See the Object Scripting Index for an example.
Choice Model Output
Click on Run Model to obtain the results. These results are shown in Figure 16.6.
• The resulting parameter estimates are sometimes referred to as part-worths. Each part-worth is the
coefficient of utility associated with that attribute. By default, these estimates are based upon the Firth
bias-corrected maximum likelihood estimators, and are, therefore, considered to be more accurate than
MLEs without bias correction.
• Comparison criteria are used to help determine the better-fitting model(s) when more than one model is
investigated for your data. The model with the lower or lowest criterion value is believed to be the better
or best model. Three criteria are shown in the Choice Model output and include AICc (corrected
Akaike’s Information Criterion), -2*LogLikelihood, and -2*Firth Loglikelihood. The AICc formula is:
AICc
2k( k+
1)
= – 2loglikelihood + 2k + ---------------------- n – k – 1
where k is the number of estimated parameters in the model and n is the number of observations in the
dataset. Note that the -2*Firth Loglikelihood result is only included in the report when the Firth
Bias-adjusted Estimates checkbox is checked in the launch window. (See Figure 16.5.) This option is
checked by default. The decision to use or not use the Firth Bias-adjusted Estimates does not affect the
AICc score or the -2*LogLikelihood results.
• Likelihood ratio tests appear for each effect in the model. These results are obtained by default if the
model is fit quickly (less than five seconds); otherwise, you can click on the Choice Model drop down
menu and select Likelihood Ratio Tests.
Figure 16.6 Choice Model Results with No Subject Data for Pizza Example
Part-worths for crust, cheese,
and topping