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.
ampute.mcar(P, patterns, prop)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.
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.
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
and 1 if a case should remain complete. The first vector refers to the
first pattern, the second vector to the second pattern, etcetera.