For loops are really, really slow in R. In general, you want to avoid them like the plague. If you absolutely must insist on using them in large, computationally intense and complex code, consider implementing the relevant parts in C, say, and calling that from R.
Staying within R, you can probably considerably speed up that code by storing gx and gy as a multi-dimensional arrays. (e.g. for sample data, something like rawGy = sample( 1:240, 240^2* 241, replace = T) rawGx = sample( 1:240, 240^2 *241, replace = T) gx = array(rawGx, dim = c(length(s) - 1, 240, max(rawGx)+1 ) ) gy = array(rawGy, dim = c(length(s) - 1, 240, max(rawGy)+1 ) ) ), in which case, you can easily do the computation without loops by gxa = (gx[ ,a,1]+ 1) gya =(gy[ ,a, 1] +1) uv = gx[cbind(1:(length(s) - 1) , b, gxa)] / gx[cbind(1:(length(s) - 1) , a, gxa)] - gy[cbind(1:(length(s) - 1) ,b, gya)]/gy[cbind(1:(length(s) - 1) ,a, gya)] or similar, which will be enormously faster (on my computer, there's an over 30x speed up). With a bit of thought, I'm sure you can also figure out how to let it vectorise in a, as well... Zhou -- View this message in context: http://r.789695.n4.nabble.com/For-calculation-is-so-slow-tp4630830p4630855.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org 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.
