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Calculates score for a single imputation function

Source: R/IScore_cat.R
Iscore_cat.Rd

Calculates score for a single imputation function

Usage

Iscore_cat(
  X,
  X_imp,
  imputation_func,
  factor_vars = TRUE,
  multiple = TRUE,
  N = 50,
  max_length = NULL,
  skip_if_needed = TRUE
)

Arguments

X

data containing missing values denoted with NA's

X_imp

imputed dataset.

imputation_func

a function that imputes data

factor_vars

a logical value indicating whether imputation should be performed on factors. If FALSE, all the variables that are factors will be converted to numeric values.

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.

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.

Examples

set.seed(123)
X <- matrix(rnorm(500), nrow = 100)
X <- cbind(X, factor(sample(1:5, 100, replace = TRUE), levels = 1:5))
X[runif(600) < 0.2] <- NA
X <- cbind(X, factor(sample(1:2, 100, replace = TRUE), levels = 1:2))
X <- as.data.frame(X)
X[["V6"]] <- factor(X[["V6"]], levels = 1:5)
X[["V7"]] <- factor(X[["V7"]], levels = 1:2)
X[["V8"]] <- rnorm(100)
imputation_func <- miceDRF:::create_mice_imputation("cart")
X_imp <- imputation_func(X)

Iscore_cat(X, X_imp, imputation_func, factor_vars = FALSE)
#> [1] 0.6935389
#> attr(,"dat")
#>    column_id weight     score n_columns_used
#> V1         1 0.1971 0.6819397              2
#> V5         5 0.1924 0.6718325              2
#> V3         3 0.1600 0.7061233              2
#> V6         6 0.1539 0.6258925              2
#> V2         2 0.1476 0.7203252              2
#> V4         4 0.1275 0.7790775              2