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Calculates score for one imputation function

energy_Iscore_num.Rd

Calculates score for one imputation function

Usage

energy_Iscore_num(
  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.

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_data(n = 100, p = 4, ratio = 0.2)

imp_fun <- Iscores:::norm_imputation

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

sc
#> [1] 0.8951307
#> attr(,"dat")
#>    column_id weight     score n_columns_used
#> X1         1 0.1824 0.7767088              1
#> X2         2 0.1659 0.9537124              1
#> X3         3 0.1539 0.8848685              1
#> X4         4 0.1476 0.9863285              1