R: Missing data pattern by variable pairs

md.pairs {mice} | R Documentation

## Missing data pattern by variable pairs

### Description

Number of observations per variable pair.

### Usage

    md.pairs(data)

### Arguments

`data` | A data frame or a matrix containing the incomplete data. Missing values are coded as `NA`.

### 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

### 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.

### Author(s)

Stef van Buuren, Karin Groothuis-Oudshoorn, 2009

### 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

    # 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))

[Package mice version 3.14.0 Index]