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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mice.impute.2lonly.mean.R
\name{mice.impute.2lonly.mean}
\alias{mice.impute.2lonly.mean}
\alias{2lonly.mean}
\title{Imputation of most likely value within the class}
\usage{
mice.impute.2lonly.mean(y, ry, x, type, wy = NULL, ...)
}
\arguments{
\item{y}{Vector to be imputed}
\item{ry}{Logical vector of length \code{length(y)} indicating the
the subset \code{y[ry]} of elements in \code{y} to which the imputation
model is fitted. The \code{ry} generally distinguishes the observed
(\code{TRUE}) and missing values (\code{FALSE}) in \code{y}.}
\item{x}{Numeric design matrix with \code{length(y)} rows with predictors for
\code{y}. Matrix \code{x} may have no missing values.}
\item{type}{Vector of length \code{ncol(x)} identifying random and class
variables. The class variable (only one is allowed) is coded as \code{-2}.}
\item{wy}{Logical vector of length \code{length(y)}. A \code{TRUE} value
indicates locations in \code{y} for which imputations are created.}
\item{...}{Other named arguments.}
}
\value{
Vector with imputed data, same type as \code{y}, and of length
\code{sum(wy)}
}
\description{
Method \code{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.
}
\details{
Observed values in \code{y} are averaged within the class, and
replicated to the missing \code{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, \code{mice.impute.2lonly.mean()} imputes the
class mean of \code{y}. If \code{y} is a second-level variable, then
conventionally all observed \code{y} will be identical within the
class, and the function just provides a quick fix for any
missing \code{y} by filling in the class mean.
For factor variables, \code{mice.impute.2lonly.mean()} imputes the
most frequently occuring category within the class.
If there are no observed \code{y} in the class, all entries of the
class are set to \code{NA}. Note that this may produce problems
later on in \code{mice} if imputation routines are called that
expects predictor data to be complete. Methods designed for
imputing this type of second-level variables include
\code{\link{mice.impute.2lonly.norm}} and
\code{\link{mice.impute.2lonly.pmm}}.
}
\references{
Van Buuren, S. (2018).
\href{https://stefvanbuuren.name/fimd/sec-level2pred.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}}
Boca Raton, FL.: Chapman & Hall/CRC Press.
}
\seealso{
Other univariate-2lonly:
\code{\link{mice.impute.2lonly.norm}()},
\code{\link{mice.impute.2lonly.pmm}()}
}
\author{
Gerko Vink, Stef van Buuren, 2019
}
\concept{univariate-2lonly}
\keyword{datagen}
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