**[MI] estimation** -- Estimation commands for use with mi estimate

__Description__

Multiple-imputation data analysis in Stata is similar to standard data
analysis. The standard syntax applies, but you need to remember the
following for MI data analysis:

1. The data must be declared as **mi** data.

If you already have multiply imputed data (saved in Stata
format), use **mi import** to import it into **mi**.

If you do not have multiply imputed data, use **mi set** to declare
your original data to be **mi** data and use **mi impute** to fill in
missing values.

2. After you have declared **mi** data, commands such as **svyset**, **stset**,
and **xtset** cannot be used. Instead use **mi svyset** to declare
survey data, use **mi stset** to declare survival data, and use **mi**
**xtset** to declare panel data. See **[MI] mi XXXset**.

3. Prefix the estimation commands with **mi estimate:**.

The following estimation commands support the **mi** **estimate** prefix.

Command Description
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Linear regression models
**regress** Linear regression
**cnsreg** Constrained linear regression
**mvreg** Multivariate regression

Binary-response regression models
**logistic** Logistic regression, reporting odds ratios
**logit** Logistic regression, reporting coefficients
**probit** Probit regression
**cloglog** Complementary log-log regression
**binreg** GLM for the binomial family

Count-response regression models
**poisson** Poisson regression
**nbreg** Negative binomial regression
**gnbreg** Generalized negative binomial regression

Ordinal-response regression models
**ologit** Ordered logistic regression
**oprobit** Ordered probit regression

Categorical-response regression models
**mlogit** Multinomial (polytomous) logistic regression
**mprobit** Multinomial probit regression
**clogit** Conditional (fixed-effects) logistic regression

Fractional-response regression models
**fracreg** Fractional response regression

Quantile regression models
**qreg** Quantile regression
**iqreg** Interquantile range regression
**sqreg** Simultaneous-quantile regression
**bsqreg** Bootstrapped quantile regression

Survival regression models
**stcox** Cox proportional hazards model
**streg** Parametric survival models
**stcrreg** Competing-risks regression

Other regression models
**glm** Generalized linear models
**areg** Linear regression with a large dummy-variable set
**rreg** Robust regression
**truncreg** Truncated regression

Descriptive statistics
**mean** Estimate means
**proportion** Estimate proportions
**ratio** Estimate ratios
**total** Estimate totals

Panel-data models
**xtreg** Fixed-, between- and random-effects, and
population-averaged linear models
**xtrc** Random-coefficients model
**xtlogit** Fixed-effects, random-effects, and
population-averaged logit models
**xtprobit** Random-effects and population-averaged probit
models
**xtcloglog** Random-effects and population-averaged cloglog
models
**xtpoisson** Fixed-effects, random-effects, and
population-averaged Poisson models
**xtnbreg** Fixed-effects, random-effects, and
population-averaged negative binomial models
**xtgee** Fit population-averaged panel-data models by using
GEE

Multilevel mixed-effects models
**meqrlogit** Multilevel mixed-effects logistic regression (QR
decomposition)
**meqrpoisson** Multilevel mixed-effects Poisson regression (QR
decomposition)
**mixed** Multilevel mixed-effects linear regression

Survey regression models
**svy:** Estimation commands for survey data (excluding
commands that are not listed above)

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