Number of observations per variable pair.
Value
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.
Details
The four components in the output value is have the following interpretation:
- list('rr')
response-response, both variables are observed
- list('rm')
response-missing, row observed, column missing
- list('mr')
missing -response, row missing, column observed
- list('mm')
missing -missing, both variables are missing
References
Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice:
Multivariate Imputation by Chained Equations in R. Journal of
Statistical Software, 45(3), 1-67.
doi:10.18637/jss.v045.i03
Examples
pat <- md.pairs(nhanes)
pat
#> $rr
#> age bmi hyp chl
#> age 25 16 17 15
#> bmi 16 16 16 13
#> hyp 17 16 17 14
#> chl 15 13 14 15
#>
#> $rm
#> age bmi hyp chl
#> age 0 9 8 10
#> bmi 0 0 0 3
#> hyp 0 1 0 3
#> chl 0 2 1 0
#>
#> $mr
#> age bmi hyp chl
#> age 0 0 0 0
#> bmi 9 0 1 2
#> hyp 8 0 0 1
#> chl 10 3 3 0
#>
#> $mm
#> age bmi hyp chl
#> age 0 0 0 0
#> bmi 0 9 8 7
#> hyp 0 8 8 7
#> chl 0 7 7 10
#>
# show that these four matrices decompose the total sample size
# for each pair
pat$rr + pat$rm + pat$mr + pat$mm
#> age bmi hyp chl
#> age 25 25 25 25
#> bmi 25 25 25 25
#> hyp 25 25 25 25
#> chl 25 25 25 25
# percentage of usable cases to impute row variable from column variable
round(100 * pat$mr / (pat$mr + pat$mm))
#> age bmi hyp chl
#> age NaN NaN NaN NaN
#> bmi 100 0 11 22
#> hyp 100 0 0 12
#> chl 100 30 30 0