NEW! Discrete Choice Analysis Tools 2.0
Easier to Use: From input to results, and everything in between!
 Fast and efficient handling of large data sets
 Large scale data classification
 Publication quality formatted results tables with optional exportation
 Updated implementation for simple data input, parameter control, and estimation
 New logistic regression modelling for large scale classification including L2/L1 regularized classifiers and L2/ L1loss linear SVM with crossvalidation and prediction
Discrete Choice Analysis Tools 2.0 provides an adaptable, efficient, and userfriendly 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 setup and implementation intuitive.
Discrete Choice Analysis Tool v2.0 is a next generation GAUSS discrete choice analytics tool for:

Econometricians and Microeconomists
Political choice researchers
Survey data analysts
Sociologist
Epidemiologists
Insurance, safety and accident analysts
...And more!
Supported Models: Encompasses a large variety of linear classification models:
 NEW! Large Scale Data Classification: Performs largescale binary linear classification using support vector machines [SVM] or logistic regression [LR] methodology. Available options include crossvalidation of model parameters and prediction plotting. Easy to access output includes estimated prediction weights, predicted classifications and crossvalidation accuracy.
 Adjacent Categories Multinomial Logit Model: The logodds of one category versus the next higher category is linear in the cutpoints and explanatory variables.
 Logit and Probit Regression Models: Estimates dichotomous dependent variable with either Normal or extreme value distributions.
 Conditional Logit Models: Includes both variables that are attributes of the responses as well as, optionally, exogenous variables that are properties of cases.
 Mutltinomial Logit Model: Qualitative responses are each modeled with a separate set of regression coefficients.
 Negative Binomial Regression Model (left or right truncated, left or right censored, or zeroinflated): Estimates model with negative binomial distributed dependent variable. This includes censored models  the dependent variable is not observed but independent variables are available  and truncated models where not even the independent variables are observed. Also, a zeroinflated negative binomial model can be estimated where the probability of the zero category is a mixture of a negative binomial consistent probability and an excess probability. The mixture coefficient can be a function of independent variables.
 Nested Logit Regression Model: Derived from the assumption that residuals have a generalized extreme value distribution and allows for a general pattern of dependence among the responses thus avoiding the IIA problem, i.e., the "independence of irrelevant alternatives."
 Ordered Logit and Probit Regression Models: Estimates model with an ordered qualitative dependent variable with Normal or extreme value distributions.
 Possion Regression Model (left or right truncated, left or right censored, or zeroinflated): Estimates model with Poisson distributed dependent variable. This includes censored models  the dependent variable is not observed but independent variables are available  and truncated models where not even the independent variables are observed. Also, a zeroinflated Poisson model can be estimated where the probability of the zero category is a mixture of a Poisson consistent probability and an excess probability. The mixture coefficient can be a function of independent variables.
 Stereotype Multinomial Logit Model: The coefficients of the regression in each category are linear functions of a reference regression.
Outputs: GAUSS Easy to access, store, and export:
 NEW! Predicted counts and residuals
 Parameter estimates
 Variancecovariance matrix for coefficient estimates
 Percentages of dependent variables by category (where applicable)
 Complete data description of all independent variables
 Marginal effects of independent variables (by category of dependent variable, when applicable)
 Variancecovariance matrices of marginal effects
Reporting: Performs and reports a number of goodness of fit tests including for model performance analysis:
 Full model and restricted model loglikelihoods
 Chisquare statistic
 Agresti's Gsquared statistic
 Likelihood ratio statistics and accompanying probability values
 McFadden's Psuedo Rsquared
 McKelvey and Zovcina's Psuedo RSquared
 Cragg and Uhler's normed likelihood ratios
 Count RSquared
 Adjusted count RSquared
 Akaike and Bayesian information criterions
Platform: Windows, Mac, and Linux
Requirements: GAUSS/GAUSS Engine/GAUSS Light v14 or higher