I have a stack of 4 rasters. I would like the average correlation through time between a pixel and each of its 8 neighbors.
some data:
library(raster)
r1=raster(matrix(runif(25),nrow=5))
r2=raster(matrix(runif(25),nrow=5))
r3=raster(matrix(runif(25),nrow=5))
r4=raster(matrix(runif(25),nrow=5))
s=stack(r1,r2,r3,r4)
so for a pixel at position x, which has 8 neighbors at the NE, E, SE, S etc positions, I want the average of
cor(x,NE)
cor(x,E)
cor(x,SE)
cor(x,S)
cor(x,SW)
cor(x,W)
cor(x,NW)
cor(x,N)
and the average value saved at position x in the resulting raster. The edge cells would be NA or, if possible a flag to calculate the average correlation just with the cells it touches (either 3 or 5 cells). Thanks!
I don't believe @Pascal's suggestion of using focal()
could work because focal()
takes a single raster layer as an argument, not a stack. This is the solution that is easiest to understand. It could be made more efficient by minimizing the number of times you extract values for each focal cell:
library(raster)
set.seed(2002)
r1 <- raster(matrix(runif(25),nrow=5))
r2 <- raster(matrix(runif(25),nrow=5))
r3 <- raster(matrix(runif(25),nrow=5))
r4 <- raster(matrix(runif(25),nrow=5))
s <- stack(r1,r2,r3,r4)
## Calculate adjacent raster cells for each focal cell:
a <- adjacent(s, 1:ncell(s), directions=8, sorted=T)
## Create column to store correlations:
out <- data.frame(a)
out$cors <- NA
## Loop over all focal cells and their adjacencies,
## extract the values across all layers and calculate
## the correlation, storing it in the appropriate row of
## our output data.frame:
for (i in 1:nrow(a)) {
out$cors[i] <- cor(c(s[a[i,1]]), c(s[a[i,2]]))
}
## Take the mean of the correlations by focal cell ID:
r_out_vals <- aggregate(out$cors, by=list(out$from), FUN=mean)
## Create a new raster object to store our mean correlations in
## the focal cell locations:
r_out <- s[[1]]
r_out[] <- r_out_vals$x
plot(r_out)
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