Joao Pedro W. de Azevedo wrote:
Dear R users,
I've written a loop to generate Moran's test (spdep package) on serval
subsamples of a large dataset. See below a short example.
My loop is working fine, however I would like to be able to store the test
results as lines of a matrix, that I would latter be able to export as a
dataset. My problem is that I'm not sure how I could do this using R.
Any help will be much appreciated.
All the very best,
JP
coords2 <- as.matrix(jcdist.data[1:87, 6:7])
col.tri.nb<-tri2nb(coords2)
for(n in c(1,88,175,262,349)) {
f<- n+86
work <- jcdist.data[n:f, 10:12]
res <-moran.test(spNamedVec("res1", work), nb2listw(col.tri.nb,
style="W"))
moran<-res$estimate[1]
upper<-res$estimate[1] + (qnorm(0.025, lower.tail=FALSE)
*sqrt(res$estimate[3]))
lower<-res$estimate[1] - (qnorm(0.025, lower.tail=FALSE)
*sqrt(res$estimate[3]))
print(moran)
print(upper)
print(lower)
[...]
> }
What you really want is something along the lines (untested!):
coords2 <- as.matrix(jcdist.data[1:87, 6:7])
col.tri.nb <- tri2nb(coords2)
N <- c(1, 88, 175, 262, 349)
# generate the transposed matrix of results, because
# it is faster to assign into columns rathger than into rows:
results <- mattrix(nrow = 3, ncol = length(N))
# the following calculations shouldn't be done within the loop -
# efficiency!
f <- N + 86
qn <- qnorm(0.025, lower.tail = FALSE)
for(i in seq(along = N)) {
work <- jcdist.data[n[i]:f[i], 10:12]
res <- moran.test(spNamedVec("res1", work),
nb2listw(col.tri.nb, style = "W"))
# calculate the following just once:
ci <- qn * sqrt(res$estimate[3])
results[,i] <- res$estimate[1] + c(1, ci, -ci)
}
t(results)
colnames(results) <- c("Moran", "upper", "lower")
Uwe Ligges
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