# impute

##### Generic Functions and Methods for Imputation

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.

##### Usage

`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)

##### Arguments

- x
a vector or an object created by

`transcan`

, or a vector needing basic unconditional imputation. If there are no`NA`

s and`x`

is a vector, it is returned unchanged.- fun
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.- object
an object of class

`"impute"`

- ...
ignored

##### Value

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`

).

##### See Also

##### Examples

```
# 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)
# }
```

*Documentation reproduced from package Hmisc, version 4.3-0, License: GPL (>= 2)*