The imputation record

What every function in this package returns: the completed data, and a record of exactly what was changed to complete it.

Why a matrix and not a count

The record is a logical matrix parallel to the data – one row per row of the input, one column per imputed variable, TRUE where the value was filled. A summary saying "12 values were imputed in this variable" cannot answer was this patient's value imputed?, which is the question an audit asks. The matrix answers it directly and can be reduced to any count; the reverse is impossible.

The matrix form is chosen over a long data frame deliberately. It lines up with the data row for row without a join, and it stays small for a wide dataset. A long form reads more easily and is much larger, and the reading is what summary.hvti_imputation() is for.

What it holds

data

the completed data frame, via imputed_data()

matrix

the logical record, via imputed_matrix()

complete_case_pass

row-level: would this row have survived listwise deletion? Read from the input, before anything was filled

analysis_pass

row-level: is this row complete after imputing? Not derivable from the two above – a row can have a value filled and still be incomplete because a column outside vars is missing

provenance

method, m, the package version that produced it, and the seed where the method is stochastic, via imputation_provenance()

An imputed dataset that does not say how it was imputed cannot be reproduced. The SAS corpus this package ports is the proof: thirty years of imputed datasets whose method is now only partly recoverable, and only by scanning the code that produced them. So the provenance is not optional and travels with the artifact.