R: Number of imputations per block

nimp {mice} | R Documentation

## Number of imputations per block

### Description

Calculates the number of cells within a block for which imputation is requested.

### Usage

    nimp(where, blocks = make.blocks(where))

### Arguments

`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.

### Value

A numeric vector of length `length(blocks)` containing the number of cells that need to be imputed within a block.

### See Also

`mice`

### Examples

    where <- is.na(nhanes)

    # standard FCS
    nimp(where)

    # user-defined blocks
    nimp(where, blocks = name.blocks(list(c("bmi", "hyp"), "age", "chl")))

[Package mice version 3.13.0 Index]