Stata 15 help for mi estimation

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


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 ------------------------------------------------------------------------- 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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