Deterministic Imputation

• Execution: banff.deterministic()

• SDE function type: Treatment

• Input status flags: FTI (required)

• Output status flags: IDE

Description

Performs imputation when only one combination of values permits the record to pass the set of edits.

The deterministic imputation procedure analyzes each field previously identified as requiring imputation to determine if there is only one possible value which would satisfy the original edits. If such a value is found, it is imputed during execution of this procedure. This method can also be referred to as deductive imputation, since a missing or inconsistent value can be deduced with certainty based upon other fields of the same record.

For a full mathematical description of the procedure methods, with examples, please see the Functional Description.

Input and output tables

Descriptions of input and output tables are given below. Banff supports a number of input and output formats; please see the Banff User Guide for more information.

Input Table	Description
indata	Input statistical data. Mandatory.
instatus	Input status file containing FTI status flags. Mandatory. This procedure uses FTI flags to identify fields requiring deterministic imputation.

Output Table	Description
outdata	Output statistical table containing imputed data. Note that outdata will only contain successfully imputed records and affected fields. Users should update indata with the values from outdata before continuing the data editing process.
outstatus	Output status file identifying imputed fields with IDE status flags, and their values after imputation.

For details on the content of output tables, please see the Output Tables document.

Parameters

Parameter	Python type	Description
unit_id	str	Identify key variable (unit identifier) on indata. Mandatory. Must be unique for each record. Records with a missing value are dropped before processing.
edits	str	List of consistency edits. Mandatory. Example: "Revenue - Expenses = Profit; Revenue >= 0; Expenses >= 0;"
accept_negative	bool	Treat negative values as valid. Default=False. By default, a positivity edit is added for every variable in the list of edits; this parameter permits users to remove this restriction. If required, users may directly add positivity edits for individual variables.
by	str	Variable(s) used to partition indata into by-groups for independent processing. Deterministic imputation will be the same with and without by-group use. Example: by = "province industry"
prefill_by_vars	bool	Adds by-group variable(s) to input status file to improve performance. Default=True.
presort	bool	Sorts input tables before processing according to procedure requirements. Default=True.
no_by_stats	bool	Reduces log output by suppressing by-group specific messages. Default=False.