Compute the imputation KL-based scoring rules

Usage

DR_IScore(
  X,
  imputation_func = NULL,
  X_imp = NULL,
  m = 5,
  n_proj = 100,
  n_trees_per_proj = 5,
  min_node_size = 10,
  n_cores = 1,
  projection_function = NULL,
  ...
)

Arguments

X: data containing missing values denoted with NA's.
imputation_func: an imputing function. If NULL, please provide imputed datasets X_imp and m.
X_imp: a list of imputed datasets. If NULL it will be obtained using imputation_func.
m: the number of multiple imputations to consider, default to 5.
n_proj: an integer specifying the number of projections to consider for the score.
n_trees_per_proj: an integer, the number of trees per projection.
min_node_size: the minimum number of nodes in a tree.
n_cores: an integer, the number of cores to use.
projection_function: a function providing the user-specific projections.
...: used for compatibility

Value

a vector made of the scores for each imputation method.

References

This method is described in detail in:

Näf, Jeffrey, Meta-Lina Spohn, Loris Michel, and Nicolai Meinshausen. 2022. “Imputation Scores.” https://arxiv.org/abs/2106.03742.

Examples

set.seed(111)
X <- Iscores:::random_mcar_data(100, 3, 0.2)
imputation_func <- Iscores:::exp_imputation
DR_IScore(X, imputation_func, m = 2, n_proj = 10, n_trees_per_proj = 2 )
#> [1] 3.897228