mice (version 3.3.0)

ampute.mcar: Multivariate Amputation In A MCAR Manner

Description

This function creates a missing data indicator for each pattern, based on a MCAR missingness mechanism. The function is used in the multivariate amputation function ampute.

Usage

ampute.mcar(P, patterns, prop)

Arguments

P

A vector containing the pattern numbers of the cases's candidacies. For each case, a value between 1 and #patterns is given. For example, a case with value 2 is candidate for missing data pattern 2.

patterns

A matrix of size #patterns by #variables where 0 indicates a variable should have missing values and 1 indicates a variable should remain complete. The user may specify as many patterns as desired. One pattern (a vector) is also possible. Could be the result of ampute.default.patterns, default will be a square matrix of size #variables where each pattern has missingness on one variable only.

prop

A scalar specifying the proportion of missingness. Should be a value between 0 and 1. Default is a missingness proportion of 0.5.

Value

A list containing vectors with 0 if a case should be made missing and 1 if a case should remain complete. The first vector refers to the first pattern, the second vector to the second pattern, etcetera.

See Also

ampute