# Imputation of most likely value within the class

Source:`R/mice.impute.2lonly.mean.R`

`mice.impute.2lonly.mean.Rd`

Method `2lonly.mean`

replicates the most likely value within
a class of a second-level variable. It works for numeric and
factor data. The function is primarily useful as a quick fixup for
data in which the second-level variable is inconsistent.

## Arguments

- y
Vector to be imputed

- ry
Logical vector of length

`length(y)`

indicating the the subset`y[ry]`

of elements in`y`

to which the imputation model is fitted. The`ry`

generally distinguishes the observed (`TRUE`

) and missing values (`FALSE`

) in`y`

.- x
Numeric design matrix with

`length(y)`

rows with predictors for`y`

. Matrix`x`

may have no missing values.- type
Vector of length

`ncol(x)`

identifying random and class variables. The class variable (only one is allowed) is coded as`-2`

.- wy
Logical vector of length

`length(y)`

. A`TRUE`

value indicates locations in`y`

for which imputations are created.- ...
Other named arguments.

## Details

Observed values in `y`

are averaged within the class, and
replicated to the missing `y`

within that class.
This function is primarily useful for repairing incomplete data
that are constant within the class, but vary over classes.

For numeric variables, `mice.impute.2lonly.mean()`

imputes the
class mean of `y`

. If `y`

is a second-level variable, then
conventionally all observed `y`

will be identical within the
class, and the function just provides a quick fix for any
missing `y`

by filling in the class mean.

For factor variables, `mice.impute.2lonly.mean()`

imputes the
most frequently occuring category within the class.

If there are no observed `y`

in the class, all entries of the
class are set to `NA`

. Note that this may produce problems
later on in `mice`

if imputation routines are called that
expects predictor data to be complete. Methods designed for
imputing this type of second-level variables include
`mice.impute.2lonly.norm`

and
`mice.impute.2lonly.pmm`

.

## References

Van Buuren, S. (2018).
*Flexible Imputation of Missing Data. Second Edition.*
Boca Raton, FL.: Chapman & Hall/CRC Press.

## See also

Other univariate-2lonly:
`mice.impute.2lonly.norm()`

,
`mice.impute.2lonly.pmm()`