Discrete Choice (DC)#

Provides an adaptable, efficient, and user-friendly environment for linear data classification.


Discrete Choice Analysis Tools 2.1 provides an adaptable, efficient, and user-friendly environment for linear data classification. It’s designed with a full suite of tools built to accommodate individual model specificity, including adjustable parameter bounds, linear or nonlinear constraints, default or user-specified starting values, and user specified Gradient and Hessian procedures. Newly incorporated data and parameter input procedures make model set-up and implementation intuitive.


If you’re interested in purchasing DC Please contact us to request pricing and installation information.

If you already own DC , you can use the GAUSS Package Manager for quick download and installation.

Requires GAUSS/GAUSS Engine/GAUSS Light v14 or higher.

Key Features#

Binary and Count Models#

  • Binary probit.

  • Binary logit.

  • Negative binomial regression.

  • Poisson regression.

Multinomial Logit Models#

  • Conditional logit.

  • Nested logit.

  • Ordered logit.

  • Adjacent category logit.

  • Stereotype logit.

Logistic Regression Modelling#

  • L2/L1 regularized classifiers.

  • L2/L1-loss linear SVM.

Accessible, Storable, and Exportable Output#

  • Parameter estimates.

  • Variance-covariance matrix for coefficient estimates and marginal effects.

  • Categorical dependent variables percentages.

  • Data descriptions of all independent variables.

  • Marginal effects of independent variables.

  • Predicted counts and residuals.

Model Selection and Assessment#

  • Full model and restricted model log-likelihoods.

  • Chi-square statistic.

  • Agresti’s G-squared statistic.

  • McFadden’s Pseudo R-squared.

  • Madalla’s Pseudo R-squared.

  • Akaike information criterion (AIC).

  • Bayesian information criterion (BIC).

  • Likelihood ratio statistics and accompanying probability values.

  • McKelvey and Zovcina’s psuedo R-Squared.

  • Cragg and Uhler’s normed likelihood ratios.

  • Count R-Squared.

  • Adjusted count R-Squared.