On Sat, Aug 3, 2013 at 7:16 AM, Enrico Schumann <[email protected]> wrote: > On Fri, 02 Aug 2013, Anika Masters <[email protected]> writes: > >> This is not critical, but I am curious to learn. Are there any >> suggestions for speeding up the process to calculate a moving row sum? >> (Ideally from within R, as opposed to suing C, etc.) >> Using rollapply on a matrix of 45,000 rows and 400 columns takes 83 minutes. >> >> date() >> mymatrix <- matrix(data=1:45000, nrow=45000, ncol=400) >> temp <- t(rollapply(t(mymatrix), width=12, FUN=sum, by.column=T, >> fill=NA, partial=FALSE, align="left")) >> date() >> > > Write a function that *quickly* computes the moving sum of a single row; > then loop over the rows. > > Here is such a function, which is a slightly modified copy of the > function MA ("moving average") in the NMOF package. > > rsum <- function (y, order, pad = NULL) { > n <- length(y) > ma <- cumsum(y) > ma[order:n] <- ma[order:n] - c(0, ma[1L:(n - order)]) > if (!is.null(pad) && order > 1L) > ma[1L:(order - 1L)] <- pad > ma > } > > The main 'trick' is the use of 'cumsum'. Some speed comparions can > given here
That is the same trick that rollsum in zoo uses. There is some overhead in zoo due to things like converting it to a zoo object internally but its basically the same alogorithm. ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
