norm package prelim.norm

Others will certainly suggest more efficient ways to do this, but 
here is an inefficient approach:

 > X <- matrix(sample(c(0,1), 10000*3, replace=TRUE), ncol=3)
 > t(t(table(apply(X, 1, paste, collapse=""))))

       [,1]
   000 1197
   001 1238
   010 1245
   011 1230
   100 1331
   101 1259
   110 1247
   111 1253

   You are getting a warning and not an error from prelim.norm(), and 
the answer it gives seems to match the approach above.

 > library(norm)
 > X <- apply(X, 2, function(x){replace(x, x == 0, NA)})
 > prelim.norm(X)$r
      [,1] [,2] [,3]
1253    1    1    1
1230    0    1    1
1259    1    0    1
1238    0    0    1
1247    1    1    0
1245    0    1    0
1331    1    0    0
1197    0    0    0

hope this helps,

Chuck

Hey eveyone!  I hope someone can help wiht this question.  I have a matirux of all zeros and ones and I would like to indentify all unique patterns in the rows andthe number of times the pattern occurs.   I changed all zeros to NA tried to use prelim.norm to identify all patterns of missing data in the rows.  I got the message 
   
  Warning message:
NAs introduced by coercion 

  Any ideas of how to get this to work?  Or are there any way to indentify all the unique patterns in a huge matrix? ( 10000 x 71)
   
  Thanks for any suggestions!!
   
  Elizabeth Lawson

			
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