energy-I-Score for imputation of mixed data (categorical and numerical)

Usage

energy_Iscore_cat(
  X,
  imputation_func,
  X_imp = imputation_func(X),
  multiple = TRUE,
  N = 50,
  max_length = NULL,
  skip_if_needed = TRUE,
  scale = FALSE,
  n_cores = 1,
  silent = TRUE
)

Arguments

X: data containing missing values denoted with NA's.
imputation_func: a function that imputes data.
X_imp: imputed dataset of the same size as X. It's NULL by default meaning that it will be obtained by imputation of X using the imputation_func.
multiple: a logical indicating whether provided imputation method is a multiple imputation approach (i.e. it generates different values to impute for each call). Default to TRUE. Note that if multiple equals to FALSE, N is automatically set to 1.
N: a numeric value. Number of samples from imputation distribution H. Default to 50.
max_length: Maximum number of variables \(X_j\) to consider, can speed up the code. Default to NULL meaning that all the columns will be taken under consideration.
skip_if_needed: logical, indicating whether some observations should be skipped to obtain complete columns for scoring. If FALSE, NA will be returned for column with no observed variable for training.
scale: a logical value. If TRUE, each variable is scaled in the score.
n_cores: a number of cores for parallelization.
silent: logical indicating whether warnings and messages should be printed.

Value

a numerical value denoting weighted Imputation Score obtained for provided imputation function and a table with scores and weights calculated for particular columns.

Details

The categorical variables should be stored as factors. If you need additional conversion of the data (for example one-hot encoding) for imputation, please, implement everything within imputation_func parameter. You can use miceDRF:::onehot_to_factor and miceDRF:::factor_to_onehot functions.

References

This method is described in detail in:

Näf, J., Grzesiak, K., and Scornet, E. (2025). How to rank imputation methods? arXiv preprint. doi:10.48550/arXiv.2507.11297 .

Examples

set.seed(123)

X <- Iscores:::random_mcar_mixed_data(n = 100, p = 3, n_fac = 1, ratio = 0.2)

imp_fun <- Iscores:::median_mode_imputation

sc <- Iscores:::energy_Iscore_cat(X = X, imputation_func = imp_fun, N = 5)

sc
#> [1] 0.7699801
#> attr(,"dat")
#>      column_id weight     score n_columns_used
#> col1         1 0.1824 0.7096268              1
#> col2         2 0.1659 0.7301908              1
#> col4         4 0.1600 0.9355567              1
#> col3         3 0.1539 0.7122622              1