imi: Imputation Impact IMI

In sdap: Statistical Data Preparation Indicators

Description

The aggregated imputation impact for variable groups allows measuring the effect of imputations on the distribution of categoric variables (exclusive response groups).

Usage

imi(data, bij, gij, obsi = 1:nrow(gij), varj = 1:ncol(gij), weight)

Arguments

data	(mandatory): A dataframe containing the data to be processed.
bij	(optional): A matrix containing the structurally missingness indicators. bij can be calculated using the function smind. If the argument bij is missing, the indicator is calculated without considering a missingness indicators matrix.
gij	(mandatory): A matrix containing the imputation indicators for a given dataframe. gij can be calculated using the function impind.
obsi	(optional): A vector with the observations in rij to to be processed. If the argument is missing, all observations are processed.
varj	(optional): A vector with the variables (column numbers) to be considered for the calculation. If the argument varj is missing, all variables are considered for the indicator.
weight	(optional): A vector of weights to be considered when calculating the indicator. Default weight is 1.

Value

A list with the following elements: variables (variables), observations (observations), Number of imputations detected (imputations), Indicator IMI (imi).

Author(s)

Beat Hulliger - Juan Berdugo

sdap documentation built on May 2, 2019, 6:52 p.m.