Charles C. Berry writes:
> On Tue, 9 Dec 2008, tyler wrote:
>
> > I'm analyzing a large number of large simulation datasets, and I've
> > isolated one of the bottlenecks. Any help in speeding it up would be
> > appreciated.
>
> Cast the neighborhoods as an indicator matrix, then use matrix
Hello,
how about changing the last loop to an apply?
time.test2 <- function(dat) {
cen <- dat
grps <- 5
n.rich <- numeric(grps^2)
n.ind <- 1
for(i in 1:grps)
for (j in 1:grps) {
n.cen <- numeric(ncol(cen) - 2)
neighbours <- expand.grid(X=(j-1):(j+1), Y=(i-1):(i
On Tue, 9 Dec 2008, tyler wrote:
Hi,
I'm analyzing a large number of large simulation datasets, and I've
isolated one of the bottlenecks. Any help in speeding it up would be
appreciated.
Cast the neighborhoods as an indicator matrix, then use matrix
multiplications:
system.time(tmp <- t
Hi,
I'm analyzing a large number of large simulation datasets, and I've
isolated one of the bottlenecks. Any help in speeding it up would be
appreciated.
`dat` is a dataframe of samples from a regular grid. The first two
columns are the spatial coordinates of the samples, the remaining 20
columns
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