Various strategies to handle missing values — handle_missing

Given a dataframe, this function performs the following steps:

Removal of variables with a fraction of missing values greater than the chosen threshold, within each group.
Removal of variables with a fraction of missing values greater than the chosen threshold, for the entire dataframe.
Imputation of the remaining variables.

handle_missing_values(
  dat,
  covariates,
  use_additional_covariates,
  selected_covariates,
  id_var,
  by_var,
  threshold_within,
  threshold_overall,
  method_imputation,
  k,
  path_save_res
)

Arguments

dat: A dataframe containing the variables of interest. A dataframe.
covariates: A dataframe containing additional variables. A dataframe.
id_var: The variable name to be used to identify subjects. A string.
by_var: The variable name to group by. A string.
threshold_within: The missing value threshold within each group. An integer.
threshold_overall: The overall missing value threshold. An integer.
k: Number of nearest neighbors used for kNN.
path_save_res

Value

A named list containing the results of the steps described above. The imputed dataframe is named dat_imputed.