convert_to_imputation_list_df function

Convert list of imputation_list_single() objects to an imputation_list_df() object (i.e. a list of imputation_df() objects's)

convert_to_imputation_list_df(imputes, sample_ids)

Arguments

• imputes: a list of imputation_list_single() objects

• sample_ids: A list with 1 element per required imputation_df. Each element must contain a vector of "ID"'s which correspond to the imputation_single() ID's that are required for that dataset. The total number of ID's must by equal to the total number of rows within all of imputes$imputations

  To accommodate for method_bmlmi() the impute_data_individual() function returns a list of imputation_list_single() objects with 1 object per each subject.

  imputation_list_single() stores the subjects imputations as a matrix where the columns of the matrix correspond to the D of method_bmlmi(). Note that all other methods (i.e. methods_*()) are a special case of this with D = 1. The number of rows in the matrix varies for each subject and is equal to the number of times the patient was selected for imputation (for non-conditional mean methods this should be 1 per subject per imputed dataset).

  This function is best illustrated by an example:

  imputes = list(
      imputation_list_single(
     id = "Tom",
     imputations = matrix(
          imputation_single_t_1_1,  imputation_single_t_1_2,
          imputation_single_t_2_1,  imputation_single_t_2_2,
          imputation_single_t_3_1,  imputation_single_t_3_2
     )
      ),
      imputation_list_single(
     id = "Tom",
     imputations = matrix(
          imputation_single_h_1_1,  imputation_single_h_1_2,
     )
      )
  )
  
  sample_ids <- list(
      c("Tom", "Harry", "Tom"),
      c("Tom")
  )
  

  Then convert_to_imputation_df(imputes, sample_ids) would result in:

  imputation_list_df(
      imputation_df(
     imputation_single_t_1_1,
     imputation_single_h_1_1,
     imputation_single_t_2_1
      ),
      imputation_df(
     imputation_single_t_1_2,
     imputation_single_h_1_2,
     imputation_single_t_2_2
      ),
      imputation_df(
     imputation_single_t_3_1
      ),
      imputation_df(
     imputation_single_t_3_2
      )
  )
  

  Note that the different repetitions (i.e. the value set for D) are grouped together sequentially.