Impute data sets in a purse — Impute.purse • COINr

This function imputes the target data set dset in each coin using the imputation function f_i. This is performed in the same way as the coin method Impute.coin(), but with one "special case" for panel data. If f_i = "impute_panel, the data sets inside the purse are imputed using the impute_panel() function. In this case, coins are not imputed individually, but treated as a single data set. In this case, optionally set the imputation method as f_i_para = list(imp_type = .) and f_i_para = list(max_time = .) where . should be substituted with the maximum number of time points to search backwards for a non-NA value. See impute_panel() for more details. No further arguments need to be passed to impute_panel(). See vignette("imputation") for more details. See also Impute.coin() documentation.

Usage

# S3 method for purse
Impute(
  x,
  dset,
  f_i = NULL,
  f_i_para = NULL,
  impute_by = "column",
  group_level = NULL,
  use_group = NULL,
  normalise_first = NULL,
  write_to = NULL,
  warn_on_NAs = TRUE,
  ...
)

Arguments

x: A purse object
dset: The name of the data set to apply the function to, which should be accessible in .$Data.
f_i: An imputation function. For the "purse" class, if f_i = "impute_panel this is a special case: see details.
f_i_para: Further arguments to pass to f_i, other than x. See details.
impute_by: Specifies how to impute: if "column", passes each column (indicator) separately as a numerical vector to f_i; if "row", passes each row separately; and if "df" passes the entire data set (data frame) to f_i. The function called by f_i should be compatible with the type of data passed to it.
group_level: A level of the framework to use for grouping indicators. This is only relevant if impute_by = "row" or "df". In that case, indicators will be split into their groups at the level specified by group_level, and imputation will be performed across rows of the group, rather than the whole data set. This can make more sense because indicators within a group are likely to be more similar.
use_group: Optional grouping variable name to pass to imputation function if this supports group imputation.
normalise_first: Logical: if TRUE, each column is normalised using a min-max operation before imputation. By default this is FALSE unless impute_by = "row". See details.
write_to: Optional character string for naming the resulting data set in each coin. Data will be written to .$Data[[write_to]]. Default is write_to == "Imputed".
warn_on_NAs: Logical: if TRUE will issue a warning if there are any NAs detected in the data frame after imputation has been applied. Set FALSE to suppress these warnings.
...: arguments passed to or from other methods.

Value

An updated purse with imputed data sets added to each coin.

Examples

# see vignette("imputation")