make.method: Creates a 'method' argument
In mice: Multivariate Imputation by Chained Equations

make.method

R Documentation

Creates a `method` argument

Description

This helper function creates a valid method vector. The method vector is an argument to the mice function that specifies the method for each block.

Usage

make.method(
  data,
  where = make.where(data),
  blocks = make.blocks(data),
  defaultMethod = c("pmm", "logreg", "polyreg", "polr")
)

Arguments

`data`	A data frame or a matrix containing the incomplete data. Missing values are coded as `NA`.
`where`	A data frame or matrix with logicals of the same dimensions as `data` indicating where in the data the imputations should be created. The default, `where = is.na(data)`, specifies that the missing data should be imputed. The `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 `mice`, like `mice.impute.panImpute()` may depend on a complete predictor space. In that case, a custom `where` matrix can not be specified.
`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 `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 `blocks` are imputed. The relevant columns in the `where` matrix are set to `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.
`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 `pmm`, predictive mean matching (numeric data) `logreg`, logistic regression imputation (binary data, factor with 2 levels) `polyreg`, polytomous regression imputation for unordered categorical data (factor > 2 levels) `polr`, proportional odds model for (ordered, > 2 levels).