`method`

argumentThis helper function creates a valid `method`

vector. The
`method`

vector is an argument to the `mice`

function that
specifies the method for each block.

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

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.

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).

Vector of `length(blocks)`

element with method names

```
# NOT RUN {
make.method(nhanes2)
# }
```

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