Hi list folks,
I have been trying to figure out how I can run the script that I have included below. I have 1.2 gigs of memory on the computer, but this is not enough, given the size of the input datasets. The input matrix ('input') is a little under 2400 x 2900 cells, and is about 60 megs when stored on hard drive...what is R doing, that this (small) size of file takes up so much memory? The script uses three variables of this size, but I don't see why that would add up to over 1.2 gigs...Is the way that I call variables in the for-loops causing this problem, with some sort of leakiness? I had heard of leaky memory problems with version 1.5.1, but I am using 1.9.1. Alternatively, I had wondered if it was just the way memory works in R, as I had read a post earlier today that suggested that R requires 5-10 times more memory than the input to meaninfully calculate anything...
Thanks...Sam
bflow <- 2 pie <- 3.141592654
input <- read.table ("xxx.txt", sep="", header=FALSE, na.strings = "-9999") rowsize <- dim(input)[1] colsize <- dim(input)[2]
result <- matrix (NA, rowsize, colsize)
for (i in 1:rowsize) for (j in 1:colsize)
if (is.na (input [i,j]) == FALSE) {probability <- matrix (NA, rowsize, colsize)
for (p in 1:rowsize) {
or (q in 1:colsize){
distance <- sqrt ((p - i)^2 + (q - j)^2)
probability [p,q] <- (2 / (bflow * pie * (1 + (distance / bflow)^2))) } }
kernsum <- sum ((probability * input / input), na = TRUE) intmean <- sum (input * probability, na = TRUE) / kernsum result [i,j] <- sum (((input - intmean)^2 * probability), na = TRUE) / kernsum
}
} }
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