Missing data pattern by variable pairs
Number of observations per variable pair.
md.pairs(data)
data |
A data frame or a matrix containing the incomplete data. Missing
values are coded as |
The four components in the output value is have the following interpretation:
response-response, both variables are observed
response-missing, row observed, column missing
missing -response, row missing, column observed
missing -missing, both variables are missing
A list of four components named rr, rm, mr and
mm. Each component is square numerical matrix containing the number
observations within four missing data pattern.
Stef van Buuren, Karin Groothuis-Oudshoorn, 2009
Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice:
Multivariate Imputation by Chained Equations in R. Journal of
Statistical Software, 45(3), 1-67.
https://www.jstatsoft.org/v45/i03/
pat <- md.pairs(nhanes) pat # show that these four matrices decompose the total sample size # for each pair pat$rr + pat$rm + pat$mr + pat$mm # percentage of usable cases to impute row variable from column variable round(100 * pat$mr / (pat$mr + pat$mm))
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