similar to: swiss.x

Here are the functions documented in the Blue Book that I found missing in R (ignoring the ones which are obviously outdated). aggregate allocated amatch axes chull clorder cutree cycle date debugger dget discr faces interp l1fit labclust lag loglin monthplot mstree mulbar napsack odometer persp plclust plotfit rep.int restore rreg sabl sablplot set.seed smooth sort.list Stable stars

Good afternoon, After cuting a hierarchical tree using cutree(), how to check correspondances between classes and branches? This is what we do: srndpchc <- hclust(dist(srndpc$x[1:1000,1:3]),method="ward") #creation of hierarchical tree plclust(srndpchc,hmin=20000) #visualisation srndpchc20000 = cutree(srndpchc,h=20000) #returns 4 classes table(srndpchc20000 ) srndclass20000 =

Thanks a lot, Michael! I cc to R-help, where this question really belongs {as the 'Subject' suggests itself...} -- please drop 'bioconductor' from CC'ing further replies. >>>>> "michael" == michael watson (IAH-C) <michael.watson at bbsrc.ac.uk> >>>>> on Thu, 17 Jun 2004 09:16:59 +0100 writes: michael> OK, admittedly it

I have Windows XP Professional Version 2002 and the R-Version 2.1.1. I did cluster analysis with the cluster package and the agnes (method = ?ward?). The results are satisfactory. But the dendrogram of agnes is confused to work with the results. Is there a tool, I can get a clear arrangement of the results for the cluster analysis. For example a matrix with different numbers for each group.

>>>>> Ross Ihaka writes: > I have got a little side-tracked (from graphics) and am putting > together a little multivariate analysis library. This is just > intended to be a "core" library rather than anything exhaustive. > Mainly it is a matter of putting togther code which already exists at > StatLib. Here is my present list (only some of which is

Hello everyone, Does anyone know how to implement the argument "unit" in R's plclust function ? I used to use Splus where this argument exists but it has not been implemented in R's plclust. The reason why I switched from Splus to R is that Ward's method is not implemented for S's hclust whereas it is implemented for R's hclust. What I would need is S's plclust

Dear R I recently performed a cluster analysis. It produced the dendogram no problem but unfortunately the font size of the row.names were all cluttered due to their large size So I tried to change the font size using plclust(cluster.results, labels=iris$specie, cex=0.8) and R came back to me saying Error in plclust(cluster.results, labels = iris$specie, cex = 0.8) : unused argument(s)

Hello all, A question/suggestion: I was wondering if there is a chance of changing stats::cutree to be S3 and use cutree.hclust? For example: cutree <- function(tree, k = NULL, h = NULL,...) { UseMethod("cutree") } cutree.hclust <- stats::cutree # This will obviously need the actual content of stats::cutree This would be nicer for people like me to add new methods to

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")

Hi there, is it possible in r to use the Initial partition established by using the HAC partition with the kmean clustering? E.g. perform the HCA, write the cluster affiliation in a seperate column > DF$hclus.label <- assignCluster(model.matrix(~-1 + A15 + B12 + C70 + E14 + + H61 + N56 + P48 + T69 + W32 + Y43, DF), DF, cutree(HClust.1, k = 3) -> use this as initial partition in the

Hello, I've been clustering my data using hclust and cutting the resulting tree with cutree. Separately, I visualize the clusterings with heatmap. Is it possible to have the dendrogram on the heatmap reflect the cutree results? That is, instead of having one large dendrogram, it would have 4 or 25 in the example below. Any guidance on if that's possible or not, and what kinds of

Hi, I have the distance matrix computed and I feed it to hclust function. The plot function produces a dense dendrogram as well. But, the cutree function applied does not produce the desired list. Here is the code x=data.frame(similarity_matrix) colnames(x) = c(source_tags_vec) rownames(x) = c(source_tags_vec) clust_tree=hclust(as.dist(x),method="complete") plot(clust_tree)

im using plclust and want the labels to be different colors. i took a look at getS3method("plot","hclust") and saw a call to .Internal. i looked at the help on .Internal and dont know where to go next. any help appreciated! thanks, rafael

Puede que no me haya expresado certeramente. Es lo que pasa al tocar de oído. Tengo unos datos (df) de los que se alguien calculó unas distancias (dis.df) de las que se hicieron/hacen unos clusters (plot.clus.dis.df). Todo esto usando un programa on line al que no tengo acceso para ver cómo hace los cálculos. Yo tengo los datos, y el programa también me da la matriz de distancias. Quiero,

Hi Everyone, I'm trying to use cutree to get the clusters after hclust. What I used is: mycluster<-cutree(cnclust,h=0.5) Now, my problem is, how can I get the actual clusters? Thanks! Best, Baoqiang Cao

I've found that while cutree() and rect.hclust() make the same classes for a given height in the dendrogram, the actual labeling of the classes is different. For example, both produce the same 4 classes but class 1 according to cutree() is class 4 according to rect.hclust(). Would it be possible that future versions provide the same labeling? rect.hclust() is useful to display the classes

Hello, I would like to cut a hclust tree into several groups at a specific similarity. I assume this can be achieved by specifying the "h" argument with the specified similarity, e.g.: clust<-hclust(dist,"average") cut<-cutree(clust,h=0.65) Now, I would like to draw rectangles around the branches of the dendrogram highlighting the corresponding clusters, as is done by

Hi !! Is there a good tutorial for the R language ? I really find it hard to find methods in R. Thanks and Cheers ../Murli

Hi, I have a gene expression experiment with 20 samples and 25000 genes each. I'd like to perform clustering on these. It turned out to become much faster when I transform the underlying matrix with t(matrix). Unfortunately then I'm not anymore able to use cutree to access individual clusters. In general I do something like this: hc <- hclust(dist(USArrests), "ave")