Stata 15 help for svy_estimation

[SVY] svy estimation -- Estimation commands for survey data

Description

Survey data analysis in Stata is essentially the same as standard data analysis. The standard syntax applies; you just need to also remember the following:

o Use svyset to identify the survey design characteristics.

o Prefix the estimation commands with svy:.

For example,

. webuse nhanes2f . svyset psuid [pweight=finalwgt], strata(stratid) . svy: regress zinc age c.age#c.age weight female black orace rural

See [SVY] svyset and [SVY] svy.

The following estimation commands support the svy prefix:

Command Description ------------------------------------------------------------------------- Descriptive statistics mean Estimate means proportion Estimate proportions ratio Estimate ratios total Estimate totals

Linear regression models churdle Cragg hurdle regression cnsreg Constrained linear regression eintreg Extended interval regression eregress Extended linear regression etregress Linear regression with endogenous treatment effects glm Generalized linear models hetregress Heteroskedastic linear regression intreg Interval regression nl Nonlinear least-squares estimation regress Linear regression tobit Tobit regression truncreg Truncated regression

Structural equation models sem Structural equation model estimation command gsem Generalized structural equation model estimation command

Survival-data regression models stcox Cox proportional hazards model stintreg Parametric models for interval-censored survival-time data streg Parametric survival models

Binary-response regression models biprobit Bivariate probit regression cloglog Complementary log-log regression eprobit Extended probit regression hetprobit Heteroskedastic probit regression logistic Logistic regression, reporting odds ratios logit Logistic regression, reporting coefficients probit Probit regression scobit Skewed logistic regression

Discrete-response regression models asmixlogit Alternative-specific mixed logit regression clogit Conditional (fixed-effects) logistic regression eoprobit Extended ordered probit regression mlogit Multinomial (polytomous) logistic regression mprobit Multinomial probit regression ologit Ordered logistic regression oprobit Ordered probit regression slogit Stereotype logistic regression zioprobit Zero-inflated ordered probit regression

Fractional-response regression models betareg Beta regression fracreg Fractional response regression

Poisson regression models cpoisson Censored Poisson regression etpoisson Poisson regression with endogenous treatment effects gnbreg Generalized negative binomial regression nbreg Negative binomial regression poisson Poisson regression tnbreg Truncated negative binomial regression tpoisson Truncated Poisson regression zinb Zero-inflated negative binomial regression zip Zero-inflated Poisson regression

Instrumental-variables regression models ivprobit Probit model with continuous endogenous covariates ivregress Single-equation instrumental-variables regression ivtobit Tobit model with continuous endogenous covariates

Regression models with selection heckman Heckman selection model heckoprobit Ordered probit model with sample selection heckpoisson Poisson regression with sample selection heckprobit Probit model with sample selection

Multilevel mixed-effects models mecloglog Multilevel mixed-effects complementary log-log regression meglm Multilevel mixed-effects generalized linear model meintreg Multilevel mixed-effects interval regression melogit Multilevel mixed-effects logistic regression menbreg Multilevel mixed-effects negative binomial regression meologit Multilevel mixed-effects ordered logistic regression meoprobit Multilevel mixed-effects ordered probit regression mepoisson Multilevel mixed-effects Poisson regression meprobit Multilevel mixed-effects probit regression mestreg Multilevel mixed-effects parametric survival models metobit Multilevel mixed-effects tobit regression

Finite mixture models fmm: betareg Finite mixtures of beta regression models fmm: cloglog Finite mixtures of complementary log-log regression models fmm: glm Finite mixtures of generalized linear regression models fmm: intreg Finite mixtures of interval regression models fmm: ivregress Finite mixtures of linear regression models with endogenous covariates fmm: logit Finite mixtures of logistic regression models fmm: mlogit Finite mixtures of multinomial (polytomous) logistic regression models fmm: nbreg Finite mixtures of negative binomial regression models fmm: ologit Finite mixtures of ordered logistic regression models fmm: oprobit Finite mixtures of ordered probit regression models fmm: pointmass Finite mixtures models with a density mass at a single point fmm: poisson Finite mixtures of Poisson regression models fmm: probit Finite mixtures of probit regression models fmm: regress Finite mixtures of linear regression models fmm: streg Finite mixtures of parametric survival models fmm: tobit Finite mixtures of tobit regression models fmm: tpoisson Finite mixtures of truncated Poisson regression models fmm: truncreg Finite mixtures of truncated linear regression models

Item response theory irt 1pl One-parameter logistic model irt 2pl Two-parameter logistic model irt 3pl Three-parameter logistic model irt grm Graded response model irt nrm Nominal response model irt pcm Partial credit model irt rsm Rating scale model irt hybrid Hybrid IRT models -------------------------------------------------------------------------

Menu

Statistics > Survey data analysis > ...

Dialog boxes for all statistical estimators that support svy can be found on the above menu path. In addition, you can access survey data estimation from standard dialog boxes on the SE/Robust or SE/Cluster tab.

Examples

Descriptive statistics . webuse nmihs . svyset [pweight=finwgt], strata(stratan) . svy: mean birthwgt

Regression models . webuse nhanes2d . svyset . svy: logistic highbp height weight age age2 female . svy, subpop(female): logistic highbp height weight age age2

Cox proportional hazards model . webuse nhefs . svyset psu2 [pw=swgt2], strata(strata2) . stset age_lung_cancer [pw=swgt2], fail(lung_cancer) . svy: stcox former_smoker smoker male urban1 rural

Multiple baseline hazards . stphplot, strata(revised_race) adjust(former_smoker smoker male urban1 rural) zero legend(col(1)) . svy: stcox former_smoker smoker male urban1 rural, strata(revised_race)


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