help mi dialog: mi
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Title
[MI] intro -- Introduction to mi
[Suggestion: Read [MI] intro substantive first.]
Syntax
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| To become familiar with mi as quickly as possible |
| |
| 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 |
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| 4. To fit your model, see |
| [MI] mi estimate |
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To create mi data from original data
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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)
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See Description 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, but instead use
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mi import import mi data
mi export export mi data to non-Stata application
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Once data are mi set or mi imported
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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
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To perform estimation on mi data
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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
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To stset, svyset, tsset, or xtset any mi data that were not set at the
time they were mi set
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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
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To perform data management on mi data
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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
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To perform data management for which no mi prefix command exists
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mi extract 0 extract m=0 data
... perform data management the usual way
mi replace0 replace m=0 data in mi data
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To perform the same data-management or data-reporting command(s) on m=0,
m=1, ...
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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
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Useful utility commands include
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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
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Programmers interested in extending mi should see
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[MI] technical Detail for programmers
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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.
Description
The mi suite of commands deals with multiple-imputation data, abbreviated
as mi data.
In 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
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. 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
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(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 = 0.0682
DF adjustment: Large sample DF: min = 243.02
avg = 12704.39
max = 31046.43
Model F test: Equal FMI F( 5, 7017.5) = 3.50
Within VCE type: OIM Prob > F = 0.0037
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attack | Coef. Std. Err. t P>|t| [95% Conf. Interval]
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smokes | 1.233068 .3642825 3.38 0.001 .5190339 1.947102
age | .0336572 .0165166 2.04 0.042 .0012368 .0660777
bmi | .1136643 .0498647 2.28 0.024 .0154421 .2118866
hsgrad | .1795391 .4078177 0.44 0.660 -.6198001 .9788783
female | -.0936591 .4193291 -0.22 0.823 -.9155651 .7282469
_cons | -5.613589 1.768236 -3.17 0.002 -9.092009 -2.135169
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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.
Anyway, 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.
Also see
Manual: [MI] intro
Online: [MI] intro substantive, [MI] glossary, [MI] styles, [MI]
workflow
