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help for ^ineqerr^                                                [STB-51: sg116]
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Impute missing values using the hotdeck method
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^hotdeck^ varlist [^if^ exp] [^in^ range] [^using^ filename] [^,^ ^by(^varlist^)^
       ^store^ ^imp^ute^(^#^)^ ^noise^ ^keep(^varlist^)^
       ^com^mand^(^command^)^ ^parms(^varlist^)^ ]
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Description
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^hotdeck^ will tabulate the missing data patterns within the
^varlist^.  A row of data with missing values in any of the variables
in the ^varlist^ is defined as a `missing line' of data, similarly a
`complete line' is one where all the variables in the ^varlist^
contain data. The ^hotdeck^ procedure replaces the ^varlist^ variables
in the `missing lines' with the corresponding values in the `complete
lines'.  ^hotdeck^ should be used several times within a multiple
imputation sequence since missing data are imputed stochastically
rather than deterministically. The ^nmiss^ missing lines in each
stratum of the data described by the `by' option are replaced by lines
sampled from the ^nobs^ complete lines in the same stratum. The
approximate Bayesian bootstrap method of Rubin and Schenker(1986) is
used; first a bootstrap sample of ^nobs^ lines are sampled with
replacement from the complete lines, and the ^nmiss^ missing lines are
sampled at random (again with replacement) from this bootstrap sample.
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A major assumption with the hotdeck procedure is that the missing data
are either missing completely at random (MCAR) or is missing at random
(MAR), the probability that a line is missing varying only with
respect to the categorical variables specified in the `by' option.
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If a dataset contains many variables with missing values then it is
possible that many of the rows of data will contain at least one
missing value. The ^hotdeck^ procedure will not work very well in such
circumstances.  There are more elaborate methods that ^only^ replace
missing values, rather than the whole row, for imputed values.  These
multivariate multiple imputation methods are discussed by
Schafer(1997).
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Options
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^by(^varlist^)^ specifies categorical variables defining strata within which 
    the imputation is to be carried out.
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^store^ specifies whether the imputed datasets are saved to disk.
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^using^ specifies the root of the imputed datasets filenames. The default is
    "imp" and hence the datasets will be saved as imp1.dta, imp2.dta, ....
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^keep(^varlist^)^ specifies the variables saved in the imputed
    datasets in addition to the imputed variables and the by list. By
    default the imputed variables and the by list are always saved.
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^impute(^#^)^ specifies the number of imputed datasets to
    generate. The number needed varies according to the percentage missing
    and the type of data, but generally 5 is sufficient.
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^command(^command^)^ specifies the analysis performed on every
    imputed dataset.
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^noise^ specifies whether the individual analyses, from the ^command^ option, 
    are displayed
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^parms(^varlist^)^ specifies the parameters of interest from the
    analysis. If the ^command^ is a regression command then the parameter
    list can include a subset of the variables specified in the regression
    command.The final output consists of the combined estimates of these
    parameters.
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Examples
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^hotdeck y, by(sex age) ^            - Impute values for y in sex/age groups 
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^hotdeck y using imp,store by(sex age) impute(2)^  - Store imputed datasets as 
imp1.dta and imp2.dta.
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^hotdeck y x, by(sex age) command(logit y x) parms(x _cons) impute(5)^ -
Do not save imputed datasets but carry out a logistic regression on the imputed
dataset and display the coefficients for x and the constant term of the model.
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Authors
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   Adrian Mander
   MRC Biostatistics Unit 
   Cambridge, UK
   adrian.mander@@mrc-bsu.cam.ac.uk

   David Clayton 
   MRC Biostatistics Unit
   Cambridge, UK
   david.clayton@@mrc-bsu.cam.ac.uk