>> Home >> Products >> Capabilities >> Generalized linear models

## Generalized linear models

 Link functions Identity Log Logit Probit Complementary log-log Power Odds power Negative binomial Log-log Log-complement Families Gaussian (normal) Inverse Gaussian Bernoulli/binomial Poisson Negative binomial Gamma Choice of estimation method Maximum likelihood Iteratively reweighted least squares (IRLS) Customizable functions User-defined link functions User-defined variance functions User-defined HAC kernels Choice of variance estimates and standard errors Inverse Hessian Outer product of the gradients (OPG) Observed information matrix Expected information matrix Robust Huber/White/sandwich estimator Robust variance with clustered/correlated data Heteroskedasticity- and autocorrelation-consistent (HAC) with Newey–West, Gallant, Anderson, or user-written kernel Jackknife Bootstrap GEE estimation for panel data Predicts Expected value of dependent variable Anscombe residual Cook’s distance Deviance residual Diagonal of hat matrix Likelihood residual Pearson residual Response residual Score residual Working residual Factor variables Automatically create indicators based on categorical variables Form interactions among discrete and continuous variables Include polynomial terms Perform contrasts of categories/levels 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 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 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

See New in Stata 12 for more about what was added in Stata Release 12.

Stata 12
Overview: Why use Stata?
Stata/MP
Capabilities
New in Stata 12
Supported platforms
Which Stata?
Technical support