combine_jack: combine imputed jackknife datasets

combine_jack function combines several imputed jackknifed dataframes into the final imputed dataframe and provide the variance for each imputed value.

combine_jack(
  ls_df,
  col_con,
  col_dis = c(),
  col_cat = c(),
  method = "onehot",
  dict_cat = NULL,
  var_cat = "unalike"
)

Arguments

ls_df

A list of imputed jackknifed dataframes.

col_con

Continous columns index.

col_dis

Discret columns index.

col_cat

Categorical columns index.

method

The encoded method of categorical columns in the imputed dataframes. This function is only coded for "onehot" situation.

dict_cat

The dictionary of categorical columns names if "onehot" method is applied. For example, it could be list("Y7"=c("Y7_1","Y7_2"), "Y8"=c("Y8_1","Y8_2","Y8_3")).

var_cat

The method of variance calculation for the categorical columns. "unalike" will lead to the calculation of unalikeability, while "wilcox_va" will lead to the calculation of Wilcox index: VarNC.

Value

df_result_disj The final imputed dataframe with the categorical columns in onehot form. df_result_var_disj The variance matrix for the final imputation dataframe with the categorical columns in onehot form. df_result The final imputed dataframe with the categorical columns in factor form. df_result_var The variance matrix for the final imputation dataframe with the categorical columns in factor form.

References

Statistical Analysis with Missing Data, by Little and Rubin, 2002