These functions do simple and transcan
imputation and print, summarize, and subscript
variables that have NAs filled-in with imputed values. The simple
imputation method involves filling in NAs with constants,
with a specified single-valued function of the non-NAs, or from
a sample (with replacement) from the non-NA values (this is useful
in multiple imputation).
More complex imputations can be done
with the transcan function, which also works with the generic methods
shown here, i.e., impute can take a transcan object and use the
imputed values created by transcan (with imputed=TRUE) to fill-in NAs.
The print method places * after variable values that were imputed.
The summary method summarizes all imputed values and then uses
the next summary method available for the variable.
The subscript method preserves attributes of the variable and subsets
the list of imputed values corresponding with how the variable was
subsetted. The is.imputed function is for checking if observations
are imputed.
impute(x, ...)# S3 method for default
impute(x, fun=median, ...)
# S3 method for impute
print(x, ...)
# S3 method for impute
summary(object, ...)
is.imputed(x)
a vector or an object created by transcan, or a vector needing
basic unconditional imputation. If there are no NAs and x
is a vector, it is returned unchanged.
the name of a function to use in computing the (single)
imputed value from the non-NAs. The default is median.
If instead of specifying a function as fun, a single value or vector
(numeric, or character if object is a factor) is specified,
those values are used for insertion. fun can also be the character
string "random" to draw random values for imputation, with the random
values not forced to be the same if there are multiple NAs.
For a vector of constants, the vector must be of length one
(indicating the same value replaces all NAs) or must be as long as
the number of NAs, in which case the values correspond to consecutive NAs
to replace. For a factor object, constants for imputation may include
character values not in the current levels of object. In that
case new levels are added.
If object is of class "factor", fun is ignored and the
most frequent category is used for imputation.
an object of class "impute"
ignored
a vector with class "impute" placed in front of existing classes.
For is.imputed, a vector of logical values is returned (all
TRUE if object is not of class impute).
# NOT RUN {
age <- c(1,2,NA,4)
age.i <- impute(age)
# Could have used impute(age,2.5), impute(age,mean), impute(age,"random")
age.i
summary(age.i)
is.imputed(age.i)
# }
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