Number of imputations per block

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. 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.

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)
#> age bmi hyp chl 
#>   0   9   8  10 

# user-defined blocks
nimp(where, blocks = name.blocks(list(c("bmi", "hyp"), "age", "chl")))
#>  B1 age chl 
#>  17   0  10