Stata 15 help for mi_intro

[MI] intro -- Introduction to mi

[Suggestion: Read [MI] intro substantive first.]

Description

+-------------------------------------------------------------+ | The mi suite of commands deals with multiple-imputation | | data, abbreviated as mi data. To become familiar with mi | | as quickly as possible, do the following | | | | 1. See A simple example under Remarks below. | | | | 2. If you have data that require imputing, see | | [MI] mi set | | [MI] mi impute | | | | 3. Alternatively, if you have already imputed data, see | | [MI] mi import | | | | 4. To fit your model, see | | [MI] mi estimate | +-------------------------------------------------------------+

To create mi data from original data

---------------------------------------------------------------------- mi set declare data to be mi data mi register register imputed, passive, or regular variables mi unregister unregister previously registered variables mi unset return data to unset status (rarely used) ---------------------------------------------------------------------- See Summary below for a summary of mi data and these commands. See [MI] Glossary for a definition of terms.

To import data that already have imputations for the missing values (do not mi set the data)

---------------------------------------------------------------------- mi import import mi data mi export export mi data to non-Stata application ----------------------------------------------------------------------

Once data are mi set or mi imported

---------------------------------------------------------------------- mi query query whether and how mi set mi describe describe mi data mi varying identify variables that vary over m mi misstable tabulate missing values mi passive create passive variable and register it ----------------------------------------------------------------------

To perform estimation on mi data

---------------------------------------------------------------------- mi impute impute missing values mi estimate perform and combine estimation on m>0 mi ptrace check stability of MCMC mi test perform tests on coefficients mi testtransform perform tests on transformed coefficients mi predict obtain linear predictions mi predictnl obtain nonlinear predictions ----------------------------------------------------------------------

To stset, svyset, tsset, or xtset any mi data that were not set at the time they were mi set

---------------------------------------------------------------------- mi fvset fvset for mi data mi svyset svyset for mi data mi xtset xtset for mi data mi tsset tsset for mi data mi stset stset for mi data mi streset streset for mi data mi st st for mi data ----------------------------------------------------------------------

To perform data management on mi data

---------------------------------------------------------------------- mi rename rename variable mi append append for mi data mi merge merge for mi data mi expand expand for mi data mi reshape reshape for mi data mi stsplit stsplit for mi data mi stjoin stjoin for mi data mi add add imputations from one mi dataset to another ----------------------------------------------------------------------

To perform data management for which no mi prefix command exists

---------------------------------------------------------------------- mi extract 0 extract m=0 data ... perform data management the usual way mi replace0 replace m=0 data in mi data ----------------------------------------------------------------------

To perform the same data-management or data-reporting command(s) on m=0, m=1, ...

---------------------------------------------------------------------- mi xeq: ... execute commands on m=0, m=1, m=2, ..., m=M mi xeq #: ... execute commands on m=# mi xeq # # ...: ... execute commands on specified values of m ----------------------------------------------------------------------

Useful utility commands

---------------------------------------------------------------------- mi convert convert mi data from one style to another

mi extract # extract m=# from mi data mi select # programmer's command similar to mi extract

mi copy copy mi data mi erase erase files containing mi data

mi update verify/make mi data consistent mi reset reset imputed or passive variable ----------------------------------------------------------------------

For programmers interested in extending mi

---------------------------------------------------------------------- [MI] technical Detail for programmers ----------------------------------------------------------------------

Summary of styles

There are four styles or formats in which mi data are stored: flongsep, flong, mlong, and wide.

1. Flongsep: m=0, m=1, ..., m=M are each separate .dta datasets. If m=0 data are stored in pat.dta, then m=1 data are stored in _1_pat.dta, m=2 in _2_pat.dta, and so on. Flongsep stands for full long and separate.

2. Flong: m=0, m=1, ..., m=M are stored in one dataset with _N = N + M*N observations, where N is the number of observations in m=0. Flong stands for full long.

3. Mlong: m=0, m=1, ..., m=M are stored in one dataset with _N = N + M*n observations, where n is the number of incomplete observations in m=0. Mlong stands for marginal long.

4. Wide: m=0, m=1, ..., m=M are stored in one dataset with _N = N observations. Each imputed and passive variable has M additional variables associated with it. If variable bp contains the values in m=0, then values for m=1 are contained in variable _1_bp, values for m=2 in _2_bp, and so on. Wide stands for wide.

See style in [MI] Glossary and see [MI] styles for examples. See [MI] technical for programmer's details.

Summary

1. mi data may be stored in one of four formats -- flongsep, flong, mlong, and wide -- known as styles. Descriptions are provided in Summary of styles directly above.

2. mi data contain M imputations numbered m = 1, 2, ..., M, and contain m=0, the original data with missing values.

3. Each variable in mi data is registered as imputed, passive, or regular, or it is unregistered. a. Unregistered variables are mostly treated like regular variables. b. Regular variables usually do not contain missing, or if they do, the missing values are not imputed in m>0. c. Imputed variables contain missing in m=0, and those values are imputed, or are to be imputed, in m>0. d. Passive variables are algebraic combinations of imputed, regular, or other passive variables.

4. If an imputed variable contains a value greater than . in m=0 -- it contains .a, .b, ..., .z -- then that value is considered a hard missing and the missing value persists in m>0.

See [MI] Glossary for a more thorough description of terms used throughout this manual.

Remarks

Remarks are presented under the following headings:

A simple example Suggested reading order

A simple example

We are about to type six commands:

. webuse mheart5 (1)

. mi set mlong (2)

. mi register imputed age bmi (3)

. set seed 29390 (4)

. mi impute mvn age bmi = attack smokes hsgrad female, add(10) (5)

. mi estimate: logistic attack smokes age bmi hsgrad female (6)

The story is that we want to fit

. logistic attack smokes age bmi hsgrad female

but the age and bmi variables contain missing values. Fitting the model by typing logistic ... would ignore some of the information in our data. Multiple imputation (MI) attempts to recover that information. The method imputes M values to fill in each of the missing values. After that, statistics are performed on the M imputed datasets separately and the results combined. The goal is to obtain better estimates of parameters and their standard errors.

In the solution shown above,

1. We load the data.

2. We set our data to be mi.

3. We inform mi which variables containing missing values for which we want to impute values.

4. We impute values in command 5; we prefer that our results be reproducible, so we set the random-number seed in command 4. This step is optional.

5. We create M=10 imputations for each missing value in the variables we registered in command 3.

6. We fit the desired model separately on each of the 10 imputed datasets and combine the results.

The results of running the six-command solution are

------------------------------------------------------------------------------- . webuse mheart5 (Fictional heart attack data, bmi and age missing)

. mi set mlong

. mi register imputed age bmi (28 m=0 obs. now marked as incomplete)

. set seed 29390

. mi impute mvn age bmi = attack smokes hsgrad female, add(10)

Performing EM optimization: note: 12 observations omitted from EM estimation because of all imputation variables missing observed log likelihood = -651.75868 at iteration 7

Performing MCMC data augmentation ...

Multivariate imputation Imputations = 10 Multivariate normal regression added = 10 Imputed: m=1 through m=10 updated = 0

Prior: uniform Iterations = 1000 burn-in = 100 between = 100

------------------------------------------------------------------ | Observations per m |---------------------------------------------- Variable | Complete Incomplete Imputed | Total -------------------+-----------------------------------+---------- age | 142 12 12 | 154 bmi | 126 28 28 | 154 ------------------------------------------------------------------ (complete + incomplete = total; imputed is the minimum across m of the number of filled-in observations.)

. mi estimate: logistic attack smokes age bmi hsgrad female

Multiple-imputation estimates Imputations = 10 Logistic regression Number of obs = 154 Average RVI = 0.1031 Largest FMI = 0.3256 DF adjustment: Large sample DF: min = 92.90 avg = 25990.98 max = 77778.66 Model F test: Equal FMI F( 5, 3279.8) = 3.27 Within VCE type: OIM Prob > F = 0.0060

------------------------------------------------------------------------------ attack | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- smokes | 1.18324 .3605462 3.28 0.001 .4765251 1.889954 age | .0321028 .016145 1.99 0.047 .0004071 .0637984 bmi | .1100667 .0546424 2.01 0.047 .0015561 .2185772 hsgrad | .1413171 .4043884 0.35 0.727 -.6512819 .933916 female | -.0759589 .416927 -0.18 0.855 -.8931367 .7412189 _cons | -5.38815 1.85184 -2.91 0.004 -9.047656 -1.728644 ------------------------------------------------------------------------------

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

Note that the output from the last command,

. mi estimate: logistic attack smokes age bmi hsgrad female

reported coefficients rather than odds ratios, which logistic would usually report. That is because the estimation command is not logistic, it is mi estimate, and mi estimate happened to use logistic to obtain results that mi estimate combined into its own estimation results.

mi estimate by default displays coefficients. If we now wanted to see odds ratios, we could type

. mi estimate, or (output showing odds ratios would appear)

Note carefully: We replay results by typing mi estimate, not by typing logistic. If we had wanted to see the odds ratios from the outset, we would have typed

. mi estimate, or: logistic attack smokes age bmi hsgrad female

Suggested reading order

The order of suggested reading of this manual is

[MI] intro substantive [MI] intro [MI] Glossary [MI] workflow

[MI] mi set [MI] mi import [MI] mi describe [MI] mi misstable

[MI] mi impute [MI] mi estimate

[MI] styles [MI] mi convert [MI] mi update

[MI] mi rename [MI] mi copy [MI] mi erase [MI] mi XXXset

[MI] mi extract [MI] mi replace0

[MI] mi append [MI] mi add [MI] mi merge [MI] mi reshape [MI] mi stsplit [MI] mi varying

Programmers will want to see [MI] technical.

Acknowledgments

We thank Jerry (Jerome) Reiter of Duke University, Patrick Royston of the MRC Clinical Trials Unit, and Ian White of the MRC Biostatistics Unit for their comments and assistance in the development of mi. We also thank James Carpenter of the London School of Hygiene and Tropical Medicine and Jonathan Sterne of the University of Bristol for their comments.

Previous and still ongoing work on multiple imputation in Stata influenced the design of mi. For their past and current contributions, we thank Patrick Royston and Ian White again for ice; John Carlin and John Galati, both of the Murdoch Children's Research Institute and University of Melbourne, and Patrick Royston and Ian White (yet again) for mim; John Galati for inorm; and Rodrigo Alfaro of the Banco Central de Chile for mira.


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