Impute.purse: Impute data sets in a purse
In bluefoxr/COINr: Composite Indicator Construction and Analysis

Impute.purse

R Documentation

Impute data sets in a purse

Description

This function imputes the target data set dset in each coin using the imputation function f_i, and optionally by specifying parameters via f_i_para. This is performed in the same way as the coin method Impute.coin(), i.e. each time point is imputed separately, unless f_i = "impute_panel". See details for more information.

Usage

## S3 method for class '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 `NA`s detected in the data frame after imputation has been applied. Set `FALSE` to suppress these warnings.
`...`	arguments passed to or from other methods.

Details

If f_i = "impute_panel" this is treated as a special case, and the data sets inside the purse are imputed using the impute_panel() function, which allows imputation of time series, using past/future values as information for imputation.

In this case, coins are not imputed individually, but treated as a single data set. To do this, set f_i = "impute_panel" and pass further parameters to impute_panel() using the f_i_para argument. Note that as this is a special case, the supported parameters of impute_panel() to specify through Impute() are "imp_type" and "max_time" (see Impute() for details on these). No further arguments need to be passed to impute_panel(). See vignette("imputation") for more details.