make.method.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/method.R
\name{make.method}
\alias{make.method}
\title{Creates a \code{method} argument}
\usage{
make.method(
  data,
  where = make.where(data),
  blocks = make.blocks(data),
  defaultMethod = c("pmm", "logreg", "polyreg", "polr")
)
}
\arguments{
\item{data}{A data frame or a matrix containing the incomplete data.  Missing
values are coded as \code{NA}.}

\item{where}{A data frame or matrix with logicals of the same dimensions
as \code{data} indicating where in the data the imputations should be
created. The default, \code{where = is.na(data)}, specifies that the
missing data should be imputed. The \code{where} argument may be used to
overimpute observed data, or to skip imputations for selected missing values.
Note: Imputation methods that generate imptutations outside of
\code{mice}, like \code{mice.impute.panImpute()} may depend on a complete
predictor space. In that case, a custom \code{where} matrix can not be
specified.}

\item{blocks}{List of vectors with variable names per block. List elements
may be named to identify blocks. Variables within a block are
imputed by a multivariate imputation method
(see \code{method} argument). By default each variable is placed
into its own block, which is effectively
fully conditional specification (FCS) by univariate models
(variable-by-variable imputation). Only variables whose names appear in
\code{blocks} are imputed. The relevant columns in the \code{where}
matrix are set to \code{FALSE} of variables that are not block members.
A variable may appear in multiple blocks. In that case, it is
effectively re-imputed each time that it is visited.}

\item{defaultMethod}{A vector of length 4 containing the default
imputation methods for 1) numeric data, 2) factor data with 2 levels, 3)
factor data with > 2 unordered levels, and 4) factor data with > 2
ordered levels. By default, the method uses
\code{pmm}, predictive mean matching (numeric data) \code{logreg}, logistic
regression imputation (binary data, factor with 2 levels) \code{polyreg},
polytomous regression imputation for unordered categorical data (factor > 2
levels) \code{polr}, proportional odds model for (ordered, > 2 levels).}
}
\value{
Vector of \code{length(blocks)} element with method names
}
\description{
This helper function creates a valid \code{method} vector. The
\code{method} vector is an argument to the \code{mice} function that
specifies the method for each block.
}
\examples{
make.method(nhanes2)
}
\seealso{
\code{\link{mice}}
}