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Is there a simple approach to converting a data frame with dummies (binary coded) on whether an aspect is present, to a co-occurrence matrix containing the counts of two aspects co-occuring?
E.g. going from this
X <- data.frame(rbind(c(1,0,1,0), c(0,1,1,0), c(0,1,1,1), c(0,0,1,0)))
X
X1 X2 X3 X4
1 1 0 1 0
2 0 1 1 0
3 0 1 1 1
4 0 0 1 0
to this
X1 X2 X3 X4
X1 0 0 1 0
X2 0 0 2 1
X3 1 2 0 1
X4 0 1 1 0
275
7.5A. To produce the co-occurrence matrices for a given value of d, we merely need to calculate the numbers of cases for which pixels a distance d apart have intensity values i and j. Here, we content ourselves with the two cases d=(1, 0) and . We thus obtain the matrices shown in Fig.
Unlike the Occurrence matrix which is a rectangular matrix, the co-occurrence matrix is a square matrix where it depicts the co-occurrence of two terms in a context. Thus, the co-occurrence matrix is also sometimes called the term-term matrix.
A co-occurrence matrix or co-occurrence distribution (also referred to as : gray-level co-occurrence matrices GLCMs) is a matrix that is defined over an image to be the distribution of co-occurring pixel values (grayscale values, or colors) at a given offset.
This will do the trick:
X <- as.matrix(X)
out <- crossprod(X) # Same as: t(X) %*% X
diag(out) <- 0 # (b/c you don't count co-occurrences of an aspect with itself)
out
# [,1] [,2] [,3] [,4]
# [1,] 0 0 1 0
# [2,] 0 0 2 1
# [3,] 1 2 0 1
# [4,] 0 1 1 0
To get the results into a data.frame exactly like the one you showed, you can then do something like:
nms <- paste("X", 1:4, sep="")
dimnames(out) <- list(nms, nms)
out <- as.data.frame(out)
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