Impute a data frame imprecisely
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a data.frame acting as recipient; see details.
a data.frame acting as donor; see details.
1-character string of the desired imputation method.
The following values are possible, see details for an explanantion:
a character vector containing the variable names
to be used as matching variables. If
a named list containing the possible values of
all variable in
object of class 'impimp'
further arguments passed down to
object to test for class
As in the context of statistical matching the data.frames
donor are assumed to contain an
overlapping set of variables.
The missing values in
recipient are subsituted with
observed values in
donor for approaches based on donation
classes and otherwise with the set of all possible values for
the variable in question.
method = "domain" a missing value of a variable in
recipient is imputed by the set of all possible values
of that variable.
The other methods are based on donation classes which are formed
based on the matching variables whose names are provided by
matchvars. They need to be present in both
method = "variable_wise" a missing value of a variable
recipient is imputed by the set of all observed values
of that variable in
method = "case_wise" the variables only present in
donor are represented as tuples. A missing tuple in
recipient is then imputed by the set of all observed
The data.frame resulting in an imprecise imputation
It is also of class
"impimp" and stores the imputation
method in its attribute
"impmethod", the names of the
variables of the resulting object containing imputed values
in the attribute
"imputedvarnames", as well as the
list of (guessed) levels of each underlying variable in
The variable names and observations in
donor must not contain characters that are reserved for
The actual characters that are internally used are stored in the
options("impimp.varssep"). The former is used to separate
the values of a set-valued observation, while the other is used
for a concise tupel representation.
This method does not require that all variables in
factor variables, however,
the imputation methods apply coercion to factor, so purely
numerical variables will be treated as factors eventually.
It does assume (and test for it) that there are no missing
values present in the matching variables.
Endres, E., Fink, P. and Augustin, T. (2018), Imprecise Imputation: A Nonparametric Micro Approach Reflecting the Natural Uncertainty of Statistical Matching with Categorical Data, Department of Statistics (LMU Munich): Technical Reports, No. 214. URL https://epub.ub.uni-muenchen.de/42423/.
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