Discrete Choice Analysis Tools

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Discrete Choice Analysis Tools

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Discrete Choice Analysis Tools 2.0 provides an adaptable, efficient, and user-friendly environment for linear data classification.
Overview

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, 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.

Platforms: Windows, Mac, and Linux

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

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

Examples

Product Inquiry

6 + 4 = enter the result ex:(3 + 2 = 5)



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