Hi,

I'm looking for a more elegant (and faster) solution to my current

problem than the code at the end. I'm sure there is one, but can't think

where to look - any pointers would be very welcome. The problem is one

of resampling within an array. This array consists of 0s, 1s and NAs.

For each level of dimension z, I would like to rewrite the array such

that it looks up the values in the matrix on either side (through a

variable number of cells) and determines if there is a 1 in any of them

- if so, the new value is 1, otherwise 0. This process is repeated

across the entire matrix: I will obviously end up with a lot more 1s in

the new array than I did before. The code at the end works, but is very

slow for large arrays (it needs package (magic) to work). Any

suggestions/pointers would be gratefully recieved

Colin

An example dataset could be:

size = 1 #determines how many values to sample around the

focal

library (magic)

set.seed(1)

A <- array (sample (c (0,0,0,1), 35, replace = T), dim = c (5,4,2))

temp <- apad (apad (A, c (size,size,0)), c(size,size,0), post =

FALSE) # pads array

temp[,c (1, (4 + 2 * size)),] <- NA # makes additional

rows/cols = NA

temp[c (1, (5 + 2 * size)),,] <- NA

for (y in 1: 5) { # recalculates

within size

for (x in 1: 4) {

for (z in 1: 2) {

A[y,x,z] <- ifelse (sum (temp[(y):(y+2 * size),(x):(x+2 *

size),z], na.rm = TRUE) == 0, 0, 1)

}

}

}

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