Logistic/logit regression

• Basic (dichotomous) ML logistic regression with influence statistics
• Fit diagnostics and ROC curve
• Skewed logistic regression
• Grouped-data logistic regression
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints

Conditional logistic regression

• McFadden’s choice model
• 1:1 and 1:k matching
• Conditional fixed-effects logit models (m:k matching) with exact likelihood (no limit on panel size)
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints
• Predictions for influence and lack-of-fit statistics and Pearson residuals

Multinomial logistic regression

• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints

Probit regression

• Dichotomous outcome with ML estimates
• Bivariate probit regression
• Endogenous regressors
• Grouped-data probit regression
• Heteroskedastic probit regression
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints

Ordinal regression models

• Ordered logistic (proportional-odds model)
• Ordered probit
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints

Tobit regression and truncated regression

• Lower and upper limits of censoring
• Differing limits for each observation
• Predictions available for expected value, conditional expected value, censored expected value, and probability of censoring
• Endogenous regressors
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints

Interval regression

• Open and closed intervals
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints

Poisson and negative binomial regression

• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints
• Predict expected counts, incidence rates, and probabilities of counts
• Poisson goodness-of-fit tests

Rank-ordered logistic regression

• Plackett–Luce model, exploded logit, choice-based conjoint analysis
• Alternative- and case-specific variables
• Complete rankings of ordered outcome
• Incomplete rankings of ordered outcome
• Ties (“indifference”)
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Prediction of probability that alternatives are ranked first

Stereotype logistic regression

• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints
• Predictions of probabilities of outcomes

Nested logit

• Random-utilities maximization model
• Full maximum-likelihood estimation
• Up to eight nested levels
• Facilities to set up the data and display the tree structure
• Linear constraints, including constraints on inclusive-value parameters
• Predictions available for utility functions, probabilities, conditional probabilities, and inclusive values
• Robust, cluster–robust, bootstrap, and jackknife standard errors

Multinomial probit regression

• Alternative- and case-specific variables
• Homo- or heteroskedastic variances
• Various correlation structures, including user-specified
• Probabilities based on GHK simulator
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints

Heckman selection models

• Two-step and maximum likelihood (ML)
• Robust, cluster–robust, bootstrap, and jackknife standard errors (ML only)
• Bootstrap and jackknife standard errors (two-step)
• Linear constraints (ML only)
• Predictions available for Mills’ ratio, expected value, conditional expected value, probability of selection, nonselection hazard, and more

Heckman selection with a binary outcome

• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints
• Predictions available for probability of binary outcome, all four combinations of outcome and selection, probability of selection, conditional probability of outcome, and more

Zero-inflated count models

• Zero-inflated Poisson
• Zero-inflated negative binomial
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints
• Predict expected counts, incidence rates, and probabilities of counts

Left-truncated count models

• Zero-truncated Poisson
• Zero-truncated negative binomial
• Left-truncated Poisson
• Left-truncated negative binomial
• Truncation varying by observation
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints
• Predict expected counts, incidence rates, and probabilities of counts

Treatment-effects model

• Two-step and maximum likelihood (ML)
• Fitted values and their standard errors (SEs)
• Expected value given treatment or nontreatment and their SEs
• Probability of treatment and its SE
• Robust, cluster–robust, bootstrap, and jackknife standard errors (ML only)
• Bootstrap and jackknife standard errors (two-step)
• Linear constraints (ML only)

Factor variables

• Automatically create indicators based on categorical variables
• Form interactions among discrete and continuous variables
• Include polynomial terms
• Perform contrasts of categories/levels

Marginal analysis

• Estimated marginal means
• Marginal and partial effects
• Average marginal and partial effects
• Least-squares means
• Predictive margins
• Adjusted predictions, means, and effects
• Contrasts of margins
• Pairwise comparisons of margins
• Profile plots
• Graphs of margins and marginal effects

Contrasts

• Analysis of main effects, simple effects, interaction effects, partial
interaction effects, and nested effects
• Comparisons against reference groups, of adjacent levels, or against
the grand mean
• Orthogonal polynomials
• Helmert contrasts
• Custom contrasts
• ANOVA-style tests
• Contrasts of nonlinear responses
• Multiple-comparison adjustments
• Balanced and unbalanced data
• Contrasts in odds-ratio metric
• Contrasts of means, intercepts, and slopes
• Graphs of contrasts
• Interaction plots

Pairwise comparisons

• Compare estimated means, intercepts, and slopes
• Compare marginal means, intercepts, and slopes
• Balanced and unbalanced data
• Nonlinear responses
• Multiple-comparison adjustments: Bonferroni, Šidák, Scheffé, Tukey HSD, Duncan, and Student-Newman-Keuls adjustments
• Group comparisons that are significant
• Graphs of pairwise comparisons