mlim (version 0.3.0)

mlim.error: imputation error

Description

calculates NRMSE, missclassification rate, and miss-ranking absolute mean distance, scaled between 0 to 1, where 1 means maximum distance between the actual rank of a level and the imputed level.

Usage

mlim.error(
  imputed,
  incomplete,
  complete,
  transform = NULL,
  varwise = FALSE,
  ignore.missclass = TRUE,
  ignore.rank = FALSE
)

Value

numeric vector

Arguments

imputed

the imputed dataframe

incomplete

the dataframe with missing values

complete

the original dataframe with no missing values

transform

character. it can be either "standardize", which standardizes the numeric variables before evaluating the imputation error, or "normalize", which change the scale of continuous variables to range from 0 to 1. the default is NULL.

varwise

logical, default is FALSE. if TRUE, in addition to mean accuracy for each variable type, the algorithm's performance for each variable (column) of the datast is also returned. if TRUE, instead of a numeric vector, a list is retuned.

ignore.missclass

logical. the default is TRUE. if FALSE, the overall missclassification rate for imputed unordered factors will be returned. in general, missclassification is not recommended, particularly for multinomial factors because it is not robust to imbalanced data. in other words, an imputation might show a very high accuracy, because it is biased towards the majority class, ignoring the minority levels. to avoid this error, Mean Per Class Error (MPCE) is returned, which is the average missclassification of each class and thus, it is a fairer criteria for evaluating multinomial classes.

ignore.rank

logical (default is FALSE, which is recommended). if TRUE, the accuracy of imputation of ordered factors (ordinal variables) will be evaluated based on 'missclassification rate' instead of normalized euclidean distance. this practice is not recommended because higher classification rate for ordinal variables does not guarantee lower distances between the imputed levels, despite the popularity of evaluating ordinal variables based on missclassification rate. in other words, assume an ordinal variable has 5 levels (1. strongly disagree, 2. disagree, 3. uncertain, 4. agree, 5.strongly agree). in this example, if "ignore.rank = TRUE", then an imputation that imputes level "5" as "4" is equally inaccurate as other algorithm that imputes level "5" as "1". therefore, if you have ordinal variables in your dataset, make sure you declare them as "ordered" factors to get the best imputation accuracy.

Author

E. F. Haghish

Examples

if (FALSE) {
data(iris)

# add 10% missing values, ensure missingness is stratified for factors
irisNA <- mlim.na(iris, p = 0.1, stratify = TRUE, seed = 2022)

# run the default imputation
MLIM <- mlim(irisNA)
mlim.error(MLIM, irisNA, iris)

# get error estimations for each variable
mlim.error(MLIM, irisNA, iris, varwise = TRUE)
}