This function creates a missing data indicator for each pattern. The continuous
probability distributions (Van Buuren, 2012, pp. 63, 64) will be induced on the
weighted sum scores, calculated earlier in the multivariate amputation function
ampute.continuous(P, scores, prop, type)
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 list containing vectors with the candidates's weighted sum scores,
the result of an underlying function in
A scalar specifying the proportion of missingness. Should be a value between 0 and 1. Default is a missingness proportion of 0.5.
A vector of strings containing the type of missingness for each
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 [aut, cre], Gerko Vink [aut], Peter Lugtig [ctb], 2016
#'Van Buuren, S. (2018). Flexible Imputation of Missing Data. Second Edition. Chapman & Hall/CRC. Boca Raton, FL.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.