Survey regression models
Linear regression
Logistic regression
Cox regression
Parametric survival regression
Multinomial logistic regression
Conditional logit regression
Negative binomial regression
Ordered logistic regression
Probit regression
Ordered probit regression
Poisson regression
Structural equation modeling
Censored and interval regression
Instrumental-variables regression
Heckman selection model
Probit estimation with selection
Nonlinear least squares
more
Multilevel models with survey data*
Variance and standard-error estimates
Taylor-series linearization (Huber/White/sandwich)
Balanced and repeated replications (BRR)
Survey jackknife
Bootstrap (with bootstrap replicate weights)
Successive difference replication (SDR)
Sampling designs
Sampling (probability) weights
Stratification
Clustering
Multistage designs
Weights at each sampling stage*
Finite population correction in all stages
Support for strata with one sampling unit
Features
Poststratification
Design effects
Misspecification effects
Effects for linear combinations
Coefficient of variation
Estimate linear/nonlinear combinations of parameters
Hypotheses tests for survey data
Estimation with linear constraints
Goodness of fit for logistic and probit estimators
Multiple imputation
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Maximum pseudolikelihood estimation
User-defined likelihoods
Survey characteristics automatically handled
Summary statistics
Population and subpopulation means
Population and subpopulation standard deviations
Population and subpopulation proportions
Population and subpopulation ratios
Population and subpopulation totals
Provide full covariance estimates across subpopulations
Summary tables
Two-way contingency tables with tests of independence
One-way tables
Table describing the sampling design of survey data
Postestimation Selector*
View and run all postestimation features for your command
Automatically updated as estimation commands are run
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
Works with multiple outcomes simultaneously*
Contrasts of margins
Pairwise comparisons of margins
Profile plots
Interaction plots
Graphs of margins and marginal effects
Additional resources