search for: maxclust

...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]]

Dear All According to the identify.hclust documentation the function "cuts the tree at the vertical position of the pointer and highlights the cluster containing the horizontal position of the pointer". When I carry out this, the tree isn't cut where I click - in fact, there seems to be a limit below which I cannot go. Consider the following code: mat <- matrix(rnorm(5000),

...means(), but it's in C and FORTRAN and I'm not quite sure how to get at it. Has anybody looked into the differences in the implementations or have any thoughts on the matter? Below is the code I'm using in each case. fit=kmeans(obs[,-1],centers,nstart=25) * proc* *fastclus* data=std maxclusters=*2* maxiter=*100* outiter drift converge=*0.01* outseed=centers out=cluster; var x y z; * run*; Thanks, Andy [[alternative HTML version deleted]]

...when in include a FUN argument. I am using V2.4.0. > hc Call: hclust(d = dist(mtx2, method = "manh"), method = "average") Cluster method : average Distance : manhattan Number of objects: 9 > plot(hc) > identify(hc) Error in cutree(x, k = 2:MAXCLUSTER) : elements of 'k' must be between 1 and 9 > David Farrar, Ph.D. New River Analytic [[alternative HTML version deleted]]