Missingness indicators, generated from the record

Returns one logical column per imputed variable, TRUE where that variable's value was filled. A caller reproducing a SAS analysis that carried ms_* indicators as model terms renames in one step: imputation_indicators(x, prefix = "ms_").

Usage

imputation_indicators(x, prefix = "imputed_")

Arguments

x

An object of class hvti_imputation or hvti_imputation_multi.

prefix

Prepended to each imputed variable's name. Defaults to "imputed_".

Value

A data frame of logical columns, one per imputed variable, with nrow() matching the imputed data.

Details

The indicators are generated rather than stored, and the SAS ms_ naming is not the default. A convention adopted for parity becomes permanent – it would be ours to maintain forever the moment a study depended on it – so the record stays the single source of truth and the name stays the caller's choice.

The principle underneath is not negotiable and predates the naming: a value that means "observed" and a value that means "filled in" must not be the same value. The indicators are how that survives into a model.

Examples

dat <- data.frame(age = c(50, 60, NA, 70), bmi = c(NA, 25, 27, 29))
out <- impute_mean(dat, vars = c("age", "bmi"))
imputation_indicators(out)
#>   imputed_age imputed_bmi
#> 1       FALSE        TRUE
#> 2       FALSE       FALSE
#> 3        TRUE       FALSE
#> 4       FALSE       FALSE
imputation_indicators(out, prefix = "ms_")
#>   ms_age ms_bmi
#> 1  FALSE   TRUE
#> 2  FALSE  FALSE
#> 3   TRUE  FALSE
#> 4  FALSE  FALSE