## Stata 15 help for svy_proportion

```
[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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