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).