Discrete Choice Analysis Tools v2.0

NEW! Discrete Choice Analysis Tools 2.0

Easier to Use: From input to results, and everything in between!


Discrete Choice Analysis Tools 2.0 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, and default or user specified starting values. Newly incorporated data and parameter input procedures make model set-up and implementation intuitive.

  • Fast and efficient handling of large data sets
  • Large scale data classification
  • Publication quality formatted results tables with optional exportation
  • Updated implementation simplifies data input, parameter control, and estimation
  • New logistic regression modelling for large scale classification including L2/L1 regularized classifiers and L2/ L1-loss linear SVM with cross-validation and prediction

Discrete Choice Analysis Tool v2.0 is a next generation GAUSS discrete choice analytics tool for:

    -Econometricians and Micro-economists
    -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 large-scale binary linear classification using support vector machines [SVM] or logistic regression [LR] methodology. Available options include cross-validation of model parameters and prediction plotting. Easy to access output includes estimated prediction weights, predicted classifications and cross-validation accuracy.
  • Adjacent Categories Multinomial Logit Model: The log-odds of one category versus the next higher category is linear in the cutpoints and explanatory variables.
  • Binary 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.
  • Multinomial 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 zero-inflated): 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 zero-inflated 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 zero-inflated): 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 zero-inflated 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: Easy to access, store, and export:

  • NEW! Predicted counts and residuals
  • Parameter estimates
  • Variance-covariance 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)
  • Variance-covariance 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 log-likelihoods
  • Chi-square statistic
  • Agresti's G-squared statistic
  • Likelihood ratio statistics and accompanying probability values
  • McFadden's Psuedo R-squared
  • McKelvey and Zovcina's Psuedo R-Squared
  • Cragg and Uhler's normed likelihood ratios
  • Count R-Squared
  • Adjusted count R-Squared
  • Akaike and Bayesian information criterions

Examples:

Platform: Windows, Mac, and Linux

Requirements: GAUSS/GAUSS Engine/GAUSS Light v14 or higher