On Apr 9, 2009, at 5:01 PM, Jason L. Simms wrote:
Hello,
I am fairly new to R, but I am not new to programming at all. I want
to generate random clusters in a 1,000x1,000 box such that I end up
with a total of about 2,000 points. Once done, I need to export the
X,Y coordinates of the points.
I have looked around, and it seems that the spatstat package has what
I need. The rMatClust() function can generate random clusters, but I
have run into some problems.
First, I can't seem to specify that I want x number of points.
The number of points per cluster IS random.
So, right now it appears that if I want around 2,000 total points
that I
must play around with the parameters of the function (e.g., mean
number of points per cluster, cluster radius, etc.) until I end up
with roughly 2,000 points.
More problematic, however, is that specifying a 1,000x1,000 box is too
much to handle. I have been running the following function for over
24 hours straight on a decent computer and it has not stopped yet:
clust <- rMatClust(1, 50, 5, win=owin(c(0,1000),c(0,1000)))
It might well be due to the 1000 x 1000 dimensions but it is because
of your parameters. It took a significant amount of time to yield 4-10
points on a 1 x 1 window. Whereas this particular invocation much more
quickly produced 2707 points with a mean of 100 points per uniform
cluster within a 1 x 1 square:
Y <- rMatClust(20, 0.05, 100)
If you wanted the x and y dimensions to be in the range of 0-1000,
couldn't you just multiply the x and y values inside Y by 1000.
Y$x <- 1000*Y$x
Y$y <- 1000*Y$y
plot(Y) # cannot see any points, probably because the plot.kkpm
method is using
# internal ranges inside that Y object. So you might loose the ability
to use
# other functions in that package
plot(Y$x, Y$y) # as expected and took seconds at most.
I would think that the most important task would be deciding on the
function that controls the intensity process of the "offspring
points". The points in this simple example clearly violate my notions
of randomness because of the sharp edges at the cluster boundaries.
So, you may want to examine rThomas(...) in the same package.
There is, of course, a SIG spatial stats mailing list full of people
better qualified than I on such questions.
Clearly, I need to rethink my strategy. Could I generate the points
in a 10x10 box with a radius of .5 and then multiply out the resulting
point coordinates by 100? Is there another package that might meet my
needs better than spatstat for easy cluster generation?
Any suggestions are appreciated.
--
Jason L. Simms, M.A.
USF Graduate Multidisciplinary Scholar
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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