Number of imputations per blockSource:
Calculates the number of cells within a block for which imputation is requested.
nimp(where, blocks = make.blocks(where))
A data frame or matrix with logicals of the same dimensions as
dataindicating where in the data the imputations should be created. The default,
where = is.na(data), specifies that the missing data should be imputed. The
whereargument may be used to overimpute observed data, or to skip imputations for selected missing values. Note: Imputation methods that generate imptutations outside of
mice.impute.panImpute()may depend on a complete predictor space. In that case, a custom
wherematrix can not be specified.
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
methodargument). 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
blocksare imputed. The relevant columns in the
wherematrix are set to
FALSEof 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.
A numeric vector of length
the number of cells that need to be imputed within a block.