The function calculates performance metrics, such as:
- \( R^2 = [1/N * [({\sum_{i=1}^N(P_i - (\bar{P})(O_i -
(\bar{O})]/\sigma_{P}*\sigma_{O}]^2}\),
- \(RMSE= (1/N * ({\sum_{i=1}^N(P_i - O_i)^2)^{1/2}}\)
and
- \(MAE = 1/N * {\sum_{i=1}^N|{P_i - O_i}|}\)
for each imputation method
Supported Imputation Methods:
1. Linear Regression Imputation (lm_imputation): it uses a linear regression
model to predict and impute missing values
2. Median Imputation (median_imputation): it replaces missing values with the
median of observed values
3. Mean Imputation (mean_imputation): it replaces missing values with the mean
of observed values
4. Hot Deck Imputation (hot_deck_imputation): it replaces missing values with
similar observed values
5. Expectation-Maximization Imputation (EM_imputation): it uses the
Expectation-Maximization algorithm to estimate and impute missing values
It evaluate different methods of imputing missing values and calculate
performance metrics for each method