similar to: cutree (PR#1067)

The function heatmap() allows to specify row/column labels via the options labRow/labCol. From the code of heatmap(), I understand that when no labels are specified, the row/column labels (or indices) of the input matrix are taken as labels and re-ordered together with the rows and columns of the matrix before plotting, whereas labels supplied via labRow/labCol are plotted in the original order.

Full_Name: Anja von Heydebreck Version: 1.9.1 OS: Linux Submission from: (NULL) (155.250.128.25) I obtained a negative p-value from the fisher.test() function: > fisher.test(matrix(c(14576,3023,89,68),2), alternative="g")$p.value [1] -8.426593e-13 With R 1.8.1, I got a p-value of -6.239231e-12 for this example. Anja von Heydebreck

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

Full_Name: Anja Eggert Version: 2.4.1 OS: Submission from: (NULL) (139.30.70.203) I want to install the pheno package. This is not working. What can I do? Thank you very much, Anja

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, 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)

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 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

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 =

I try to use the routine cutree to cut a tree (created by hclust) into several groups by specifiing the hight of the cut. a<-1:10 cutree(tree, h=a*100) The Matrix with group meberships returned is ok for most of the hights, but in some cases (as for example h=800 and h=900) the results don't make sense (group membership=0 or 58965231, it looks like the range of data allowed by the data

I have the following code that perform hiearchical clustering and plot them in heatmap. __ library(gplots) set.seed(538) # generate data y <- matrix(rnorm(50), 10, 5, dimnames=list(paste("g", 1:10, sep=""), paste("t", 1:5, sep=""))) # the actual data is much larger that the above # perform hiearchical clustering and plot heatmap test <- heatmap.2(y)

hi all, I have a matrix and named each row and column as like below... a<-matrix(c(seq(3,45,3),seq(10,6,-1)),4,5,byrow=F) > col<-c("peter","david","richrd","vincent","selva") > rows<-c("julius","caeser","anja","maya") > dimnames(a)<-list(rows,col) > a peter david

Hallo, I'd like to write a GUI (first choice with GTK+). I've surfed through the R- an Omegahat-Pages, because I'd like to use RGTK2, GTK 2.10.11 in combination with glade on Windows XP (perhaps later Unix, Mac). I've found a lot of different information. Because of the information I'm not sure, if this combination is running on Windows XP and I'm unsure how it works. Is

Greetings, I have "inherited" a cDNA macroarray dataset that is structured as follows. Three different stressors were tested. For each stressor, there are two treatments (control and stressed). For each treatment, two biological replicates exist, and these are paired (i.e., there is a stressed array for colony A and a control array from this same colony). For one of these samples,

Basic question: Is it correct to assume that when using cutree to set the # clusters (say k=4), cutree determines the clusters by the largest distances among all potential clusters? I've read the R help for cutree and am using it to define the number of groups to obtain Dunn Index scores (using clValid library) for cluster analysis (using Euclidean Distance and Ward's method) More

Dear R-Team! I am a beginner in R and use the WISP library for a project. I am using mark-recapture to estimate abundance. I would like to know if it is possible to only survey a part of the created area (with the created population) and not all of it. I am trying to quantify bias introduced by non random sampling design (i.e. the area is 24x100 but I only wanna search 24x40) Thank you already in

Dear everyone, Your code is slow and you are interested in performance optimization for scientific software? Figuring out where’s the bottlenecks guided by performance evaluation tools. If you are interested in porting your code to a HPC hardware platform and architecture, than OpenACC as a user-driven directive-based performance-portable parallel programming model might be a solution. What are

When I use the function cutree(), the numbers of the clusters are not in the same order as the plotted dendrogram. Is there any way of sorting them so that they match the tree? Is it possible to plot a dendrogram horizontally, preferably with the branches to the right? This would enable some practical composite plots, e.g. labels that were an entire table with several columns of information, or