Description Details Author(s) See Also
This package extends and builds on the mice
package by
adding a functionality to perform multivariate predictive mean matching on
imputed data as well as new functionalities to perform predictive mean
matching on factor variables.
The mice
package, which was implemented and published by
Stef van Buuren and Karin Groothuis-Oudshoorn in 2001 and has been further
developed ever since, is one of most extensive and most commonly used
implementations of multiple imputation within R. Despite its many years of
refinement however, there are still some missing data problems that mice does
not handle very well, and two of these have now been addressed within the
implementation of this package.
First, mice does not provide any option to perform imputation on multiple
columns at once, which can, for instance, result in nonsensical output
imputations when there are causal relationships between the corresponding
attributes, e.g. a 15-year-old person that has a driver's license.
Further, mice still struggles with imputing categorical data, as many
internally used imputation methods either are not suited for this kind of data
at all or do not necessarily converge to the optimal solution.
Overall, miceExt
provides three functions, namely
mice.post.matching()
,
mice.binarize()
,
mice.factorize()
,
out of which the first function post-processes results of the
mice()
-algorithm by performing multivariate predictive mean matching on
a user-defined set of column tuples, and results in imputations that are
always equal to already-observed values, which annihilates the chance of
getting unrealistic output values.
The latter two functions tackle the
second issue by even extending the functionality of
mice.post.matching()
. The function mice.binarize()
transforms
categorical attributes of a given data frame into a binary dummy
representation, which results in an exclusively numerical data set that mice
can handle well. Inconsistencies within the imputed dummy columns can then be
handled by mice.post.matching()
, and mice.factorize()
finally
serves the purpose of retransforming the imputed binary data into the
corresponding original categories, resulting in a proper imputation of the
given categorical data.
Tobias Schumacher, Philipp Gaffert, Stef van Buuren, Karin Groothuis-Oudshoorn
mice.post.matching
, mice.binarize
,
mice.factorize
, mice
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