Frederico, This is not exactly what you're after, but perhaps it will help. In this example I fit a cluster analysis to the data, then I "cut the tree" at a height of 3 (you would do this with your data at a height of 40). It's not a perfect solution, but it might be good enough, depending on the spatial distribution of your points.
# example data frame with x and y (on the same scale) df <- data.frame(x = rnorm(100), y = rnorm(100)) # cluster analysis tree <- hclust(dist(df[, c("x", "y")], method="euclidean"), method="complete") # define groups as those that are at least 3 units apart df$group <- cutree(tree, h=3) # plot the data, using color and symbol to identify group membership eqscplot(df$x, df$y, col=df$group, pch=df$group) Jean "Frederico Mestre" <mestre.freder...@gmail.com> wrote on 07/30/2012 07:29:00 AM: > > Hello: > > What I want to do is quite simple, but I can't find a way. > > I have a data frame with several points (x and y coords). I want to add > another column with cluster membership. For example aggregate all the points > that stand within a distance of 40 from each other. > > I've tried using "nncluster" from the package nnclust, but the results are > not correct, for some reason (probably my mistake). > > This is what I did: > > x <- nncluster(as.matrix(dframe[,1:2]), threshold=35, fill = 1, maxclust = > NULL, give.up = 500,verbose=FALSE,start=NULL)#avaliar as clusters > > Thanks, > > Frederico [[alternative HTML version deleted]] ______________________________________________ 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.