Imputation of most likely value within the class

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.

Usage

mice.impute.2lonly.mean(y, ry, x, type, wy = NULL, ...)

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.

Value

Vector with imputed data, same type as y, and of length sum(wy)

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()

Author

Gerko Vink, Stef van Buuren, 2019