[R] re: patterns of missing data: determining monotonicity

[Michael Friendly]

[Sorry for the re-post; my examples got garbled in the original cut/paste.]

Here is a problem that perhaps someone out here has an idea about.  It 
vaguely reminds me of something
I've seen before, but can't place.  Can anyone help?

For multiple imputation, there are simpler methods available if  the 
patterns of missing data are 'monotone' ---
if Vj is missing then all variables Vk, k>j are also missing, vs. more 
complex methods required when the patterns are not monotone.  The 
problem is to determine if, for a collection of variables, there is an 
ordering of them with a monotone
missing data pattern, or, if not, what the longest monotone sequence is.

Here is an example, where in a dataset of 65 observations, there are 8 
different patterns of missingness,
with X and . representing observed and missing:

Group  V2  V3  V4  V5  V6  V7  V8  V9  V10  V11   nmiss

  1    x   x   x   x   x   x   x   x    x    x      0  
  2    x   x   x   x   x   x   .   x    x    x      1  
  3    x   x   x   x   x   .   x   x    x    x      1  
  4    x   x   x   x   x   .   .   x    x    x      2  
  5    x   x   .   x   .   x   x   x    x    x      2  
  6    x   x   .   .   x   x   x   x    x    x      2  
  7    x   x   .   .   x   .   x   x    x    x      3  
  8    x   x   .   .   .   x   x   x    x    x      3  

Treated as a binary matrix, one can sort the columns by the number
of non-missing for each variable, and monotone means that there
are at most 2 runs -- a string of 0s followed by all 1s for *all*
patterns. But how
to determine an ordering (or orderings) of variables of maximal length?

Group   V2  V3  V9 V10  V11  V6  V8  V5  V7  V4   nmiss

  1     0   0   0   0    0    0   0   0   0   0    0  
  2     0   0   0   0    0    0   1   0   0   0    1  
  3     0   0   0   0    0    0   0   0   1   0    1  
  4     0   0   0   0    0    0   1   0   1   0    2  
  5     0   0   0   0    0    1   0   0   0   1    2  
  6     0   0   0   0    0    0   0   1   0   1    2  
  7     0   0   0   0    0    0   0   1   1   1    3  
  8     0   0   0   0    0    1   0   1   0   1    3  
       ==  ==  ==  ===  ===  ==  ==  ==  ==  ==
        0   0   0   0    0    2   2   3   3   4        


-- 
Michael Friendly     Email: friendly at yorku.ca 
Professor, Psychology Dept.
York University      Voice: 416 736-5115 x66249 Fax: 416 736-5814
4700 Keele Street    http://www.math.yorku.ca/SCS/friendly.html
Toronto, ONT  M3J 1P3 CANADA