I have a very sparse square matrix which is < 20K rows & columns and I am
trying to row standardize the matrix for the rows that have non-missing
value as follows:
row_sums <- rowSums(M,na.rm=TRUE)
nonzero_idxs <- which(row_sums>0)
nonzero_M <- M[nonzero_idxs,]/row_sums[nonzero_idxs]
M[nonzero_idxs,] <- nonzero_M
Each line completes well under a second except the last line which takes
well over 10 seconds which is simply assigning the sub-matrix of rows that
have non-missing values to the complete matrix. I am curious to know why it
is so slow and how to speed it up. Should I be doing this differently or try
a different sparse matrix library?
Any feedback is appreciated.
thanks,
Scott
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