..-
help for ^missing^
..-

Examines and replaces missing values
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	^missing^ varlist ^, an^alysis ^me^thod^(^method-option^)^ [
time-series-option ]


Description
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^missing^ replaces missing values for the variables in ^varlist^


Options
- -------

^an^alysis displays a table of association measures. Specifically,
Simple and
Jaccard coefficients and their significance levels are calculated. High
coefficient values correspond to strong relationships between variables.

^me^thod^(^method-option^)^ specifies the method used for replacing
missing values
in ^varlist^. Available methods are:

    ^dr^op drops observations for which any variable takes on missing
value.

    ^im^pute makes use of the @impute@ ado file for performing best
subset
    regression. Since regression does not assume causality, each
variable is
    modelled as a combination of the rest.

    ^in^ter^[^varname^]^ replaces missing values with linear
interpolations of the
    existing values for each group defined by varname. When the missing
values
    are placed at the beginning (end) of the group, the first (last)
available
    value of that group is repeated. Interpolation only makes sense in
case of
    time-series. In order to sort by date the time-series option is
required:

	^da^te^(^date-variable^)^

    ^me^an^[^varname^]^ replaces missing values with the mean value for
each
    group defined by varname.

    ^pr^edict fits (@fit@ command) a linear model to all the variables
in varlist
    a replaces missing values with predicted (@predict@ command) values.
Note
    that this method does not assure that all the missing values are
filled in.


Bibliography
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A.K. Jain, R.C. Dubes (1988) "Algorithms for Clustering Data". Prentice
Hall.


Examples
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 . ^missing mpg cx, me(dr)^
 . ^missing mpg cx, me(in[maker]) da(year)^
 . ^missing mpg cx, me(me[maker])^


Also see
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On line help: @fit@, @impute@, @predict@


Author
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Jose Maria Sanchez Saez
