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Multiple-Imputation Reference Manual

Publisher:  Stata Press
Copyright:  2023
ISBN-13:  978-1-59718-391-8
Pages:  389
 
Suggested citation

StataCorp. 2023. Stata 18 Multiple-Imputation Reference Manual. College Station, TX: Stata Press.

Supplemental material
Multiple-Imputation Reference Manual for Stata
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Table of contents

Intro substantive Introduction to multiple-imputation analysis
Intro Introduction to mi
Estimation Estimation commands for use with mi estimate


mi add Add imputations from another mi dataset
mi append Append mi data
mi convert Change style of mi data
mi copy Copy mi flongsep data
mi describe Describe mi data
mi erase Erase mi datasets
mi estimate Estimation using multiple imputations
mi estimate using Estimation using previously saved estimation results
mi estimate postestimation Postestimation tools for mi estimate
mi expand Expand mi data
mi export Export mi data
mi export ice Export mi data to ice format
mi export nhanes1 Export mi data to NHANES format
mi extract Extract original or imputed data from mi data
mi import Import data into mi
mi import flong Import flong-like data into mi
mi import flongsep Import flongsep-like data into mi
mi import ice Import ice-format data into mi
mi import nhanes1 Import NHANES-format data into mi
mi import wide Import wide-like data into mi
mi impute Impute missing values
mi impute chained Impute missing values using chained equations
mi impute intreg Impute using interval regression
mi impute logit Impute using logistic regression
mi impute mlogit Impute using multinomial logistic regression
mi impute monotone Impute missing values in monotone data
mi impute mvn Impute using multivariate normal regression
mi impute nbreg Impute using negative binomial regression
mi impute ologit Impute using ordered logistic regression
mi impute pmm Impute using predictive mean matching
mi impute poisson Impute using Poisson regression
mi impute regress Impute using linear regression
mi impute truncreg Impute using truncated regression
mi impute usermethod User-defined imputation methods
mi merge Merge mi data
mi misstable Tabulate pattern of missing values
mi passive Generate/replace and register passive variables
mi predict Obtain multiple-imputation predictions
mi ptrace Load parameter-trace file into Stata
mi rename Rename variable
mi replace0 Replace original data
mi reset Reset imputed or passive variables
mi reshape Reshape mi data
mi select Programmer's alternative to mi extract
mi set Declare multiple-imputation data
mi stsplit Split and join time-span records for mi data
mi test Test hypotheses after mi estimate
mi update Ensure that mi data are consistent
mi varying Identify variables that vary across imputations
mi xeq Execute command(s) on individual imputations
mi XXXset Declare mi data to be svy, st, ts, xt, etc.


noupdate option The noupdate option


Styles Dataset styles


Technical Details for programmers


Workflow Suggested workflow


Glossary


Combined author index
Combined subject index