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.mcar(P, patterns, prop)
A vector containing the pattern numbers of the cases' candidates. 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.
A matrix of size #patterns by #variables where
A scalar specifying the proportion of missingness. Should be a value between 0 and 1. Default is a missingness proportion of 0.5.
A list containing vectors with
0 if a case should be made missing
1 if a case should remain complete. The first vector refers to the
first pattern, the second vector to the second pattern, etcetera.
Rianne Schouten, 2016
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