Arrange elements on a matrix according to rowSums + short 'apply' Q
My goal is to create a Markov transition matrix (probability of moving from one state to another) with the 'highest traffic' portion of the matrix occupying the top-left section. Consider the following sample:
inputData <- c( c(5, 3, 1, 6, 7), c(9, 7, 3, 10, 11), c(1, 2, 3, 4, 5), c(2, 4, 6, 8, 10), c(9, 5, 2, 1, 1) ) MAT <- matrix(inputData, nrow = 5, ncol = 5, byrow = TRUE) colnames(MAT) <- c("A", "B", "C", "D", "E") rownames(MAT) <- c("A", "B", "C", "D", "E") rowSums(MAT)
I wan to re-arrange the elements of this matrix such that the elements with the largest row sums are placed to the top-left, in descending order. Does this make sense? In this case the order I'm looking for would be B, D, A, E, C Any thoughts?
As an aside, here is the function I've written to construct the transition matrix. Is there a more elegant way to do this that doesn't involve a double transpose?
TMAT <- apply(t(MAT), 2, function(X) X/sum(X)) TMAT <- t(TMAT)
I tried the following:
TMAT <- apply(MAT, 1, function(X) X/sum(X))
But my the custom function is still getting applied over the columns of the array, rather than the rows. For a check try:
Row sums here should equal 1...
Many thanks in advance, Aaron
Use rowSums and colSums more! The first problem can be done with simple:
The second with:
this is a bit hacky, but becomes obvious if you recall that matrix is also a by-column vector. Taking also recycling into account it can be also done just by:
For your first request, this gets the desired descending reordering by row sums:
And this gets your TMAT in one step. The apply function returns a series of vectors and the column major ordering of matrices will transpose the results from what beginning users of R expect.
> TMAT <- t(apply(MAT, 1, function(X) X/sum(X)) ) > rowSums(TMAT) A B C D E 1 1 1 1 1