Suppose your matrix is called A (`matrix' is not a good name). Then
crossprod(!is.na(A)) is pretty efficient. Test:
A <- matrix(1, 6, 3)
A[1,1] <- A[3, 1] <- A[2,2] <- NA
A
[,1] [,2] [,3]
[1,] NA 1 1
[2,] 1 NA 1
[3,] NA 1 1
[4,] 1 1 1
[5,] 1 1 1
[6,] 1 1 1
crossprod(!is.na(A))
[,1] [,2] [,3]
[1,] 4 3 4
[2,] 3 5 5
[3,] 4 5 6
On Tue, 23 Nov 2004, Andreas Wolf wrote:
is there a smart way of determining the number of pairwise present data
in a data matrix with missings (maybe as a by-product of some
statistical function?)
so far, i used several loops like:
for (column1 in 1:99) {
for (column2 in 2:100) {
for (row in 1:500) {
if (!is.na(matrix[row,column1]) & !is.na(matrix[row,column2])) {
pairs[col1,col2] <- pairs[col1,col2]+1
}
}
}
}
but this seems neither the most elegant nor an utterly fast solution.
thanks for suggestions.
andreas wolf
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Brian D. Ripley, [EMAIL PROTECTED]
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