Hello, Thanks for your note. I recognize that the points per cluster is random, and also that it is possible to set the mean number of points per cluster through the function. What I was hoping was that I could specify a maximum number of points overall across all clusters, but conceptually I don't know how that could even be implemented. I ended up adjusting the parameters of the function until I produced right around 2,000 points in a 10x10 box, and then I just multiplied everything by 100. Not sure whether it's perfect, but I suspect that it will work for my needs currently.
I'll look into the rThomas() function, too. I am much more of an applied stats person, so the subtle (or even not-so-subtle) differences and advantages/disadvantages between a Thomas Process and a Matern Process are unclear to me at the moment. Jason On Thu, Apr 9, 2009 at 7:06 PM, David Winsemius <dwinsem...@comcast.net> wrote: > > 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 > > -- Jason L. Simms, M.A. USF Graduate Multidisciplinary Scholar Co-President, Graduate Assistants United Ph.D. / M.P.H. Student Departments of Anthropology and Environmental and Occupational Health University of South Florida ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.