Calculates IScores for multiple imputation functions

Calculates IScores for multiple imputation functions

Usage

Iscores_compare(
  X,
  imputation_list,
  methods = NULL,
  N = 50,
  max_length = NULL,
  skip_if_needed = TRUE
)

Arguments

X

data containing missing values denoted with NA's

imputation_list

a list of imputation functions

methods

a character vector of names of methods in imputation_list. It can be NULL, then the function will attempt to get names from imputation_list object.

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.

Value

a vector of IScores for provided methods

Examples

set.seed(111)
X <- matrix(rnorm(1000), nrow = 100)
X[runif(1000) < 0.4] <- NA

methods <- c("pmm", "cart", "sample", "norm.nob", "DRF")
imputation_list <- create_mice_imputations(methods)

Iscores_compare(X, imputation_list)
#> [1] "Calculating score for method: pmm"
#> No complete variables for training column 1. Skipping some observations.
#> No complete variables for training column 3. Skipping some observations.
#> No complete variables for training column 8. Skipping some observations.
#> No complete variables for training column 10. Skipping some observations.
#> No complete variables for training column 5. Skipping some observations.
#> No complete variables for training column 6. Skipping some observations.
#> No complete variables for training column 7. Skipping some observations.
#> No complete variables for training column 4. Skipping some observations.
#> No complete variables for training column 9. Skipping some observations.
#> No complete variables for training column 2. Skipping some observations.
#> [1] "Calculating score for method: cart"
#> No complete variables for training column 1. Skipping some observations.
#> No complete variables for training column 3. Skipping some observations.
#> No complete variables for training column 8. Skipping some observations.
#> No complete variables for training column 10. Skipping some observations.
#> No complete variables for training column 5. Skipping some observations.
#> No complete variables for training column 6. Skipping some observations.
#> No complete variables for training column 7. Skipping some observations.
#> No complete variables for training column 4. Skipping some observations.
#> No complete variables for training column 9. Skipping some observations.
#> No complete variables for training column 2. Skipping some observations.
#> [1] "Calculating score for method: sample"
#> No complete variables for training column 1. Skipping some observations.
#> No complete variables for training column 3. Skipping some observations.
#> No complete variables for training column 8. Skipping some observations.
#> No complete variables for training column 10. Skipping some observations.
#> No complete variables for training column 5. Skipping some observations.
#> No complete variables for training column 6. Skipping some observations.
#> No complete variables for training column 7. Skipping some observations.
#> No complete variables for training column 4. Skipping some observations.
#> No complete variables for training column 9. Skipping some observations.
#> No complete variables for training column 2. Skipping some observations.
#> [1] "Calculating score for method: norm.nob"
#> No complete variables for training column 1. Skipping some observations.
#> No complete variables for training column 3. Skipping some observations.
#> No complete variables for training column 8. Skipping some observations.
#> No complete variables for training column 10. Skipping some observations.
#> No complete variables for training column 5. Skipping some observations.
#> No complete variables for training column 6. Skipping some observations.
#> No complete variables for training column 7. Skipping some observations.
#> No complete variables for training column 4. Skipping some observations.
#> No complete variables for training column 9. Skipping some observations.
#> No complete variables for training column 2. Skipping some observations.
#> [1] "Calculating score for method: DRF"
#> No complete variables for training column 1. Skipping some observations.
#> No complete variables for training column 3. Skipping some observations.
#> No complete variables for training column 8. Skipping some observations.
#> No complete variables for training column 10. Skipping some observations.
#> No complete variables for training column 5. Skipping some observations.
#> No complete variables for training column 6. Skipping some observations.
#> No complete variables for training column 7. Skipping some observations.
#> No complete variables for training column 4. Skipping some observations.
#> No complete variables for training column 9. Skipping some observations.
#> No complete variables for training column 2. Skipping some observations.
#>    sample  norm.nob       pmm       DRF      cart 
#> 0.5485229 0.5488967 0.5583584 0.5587692 0.5797292