search for: dendros

Hi, I'm trying to build dendrograms to pass to heatmap(). The dendrograms I build plot properly, but when I pass them to heatmap() I get the error message "row dendrogram ordering gave index of wrong length" (see output log below). I looked in the code of heatmap() and saw that the error was due to a NULL return value from order.dendrogram(), which in turn got a NULL return value

I am having difficulty getting the dynamic tree cut package to work. Given the data table "myddtable" LengthPlaceColorAge5HRed224ABlue205WGreen243GRed222GBlue236WGreen255ARed194H Blue23 I created a similarity matrix using DAISY and Gower metric and specified Place and Color columns as characters (since they are categorical variables) > dd.daisy<-daisy(myddtable, metric =

Hi, I am using the function heatmap(stats) to draw a microarray heatmap, columns are samples and rows are gene features. I did a 2D clustering during the heatmap drawing. The features and samples indeed cluster into several blocks both vertically and horizontally. I can get the index of re-ordered rows and columns after the heatmap drawing by typing the the return variable of the heatmap

Hello, I have been using the "cophenetic" function for objects of class "dendrogram" and I have realised that it gives different results when it is used with objects of class "hclust". For instance, running the first example in the help file of the "cophenetic" function, d1 <- dist(USArrests) hc <- hclust(d1, "ave") d2 <-

Hi, I have produced a dendrogram of categorical data in R using the hclust function, although the input was a dissimilarity matrix produced in SAS, as I have defined my own distances. The dendrogram is fine and I can view and use this. However, I was wondering if there is a method by which I can find out the optimal place to place groups, rather than relying on my visual analysis? I don't

I am having difficulty getting the dynamic tree cut package to work. Given the data table "myddtable" LengthPlaceColorAge5HRed224ABlue205WGreen243GRed222GBlue236WGreen255ARed194H Blue23 I created a similarity matrix using DAISY and Gower metric and specified Place and Color columns as characters (since they are categorical variables) > dd.daisy<-daisy(myddtable, metric =

I would like to draw horizontal or vertical lines on a heatmap to highlight the clusters at some specified cut depth of the dendrogram. As a hacked example, the following code would work if I could set the coordinates of the top and bottom of the false color image correctly (ymin and ymax), but the correct values seem to depend on the output device and its size. I realize that heatmaps use a 2x2

Note that cophenetic.default (which works on the output of hclust(dist(X))) uses the row names of X as labels. as.dendrogram.hclust does not retain those row names so cophenetic.dendrogram cannot use them (so it orders them based on the topology of the dendrogram). Bill Dunlap TIBCO Software wdunlap tibco.com On Thu, Apr 21, 2016 at 7:59 AM, William Dunlap <wdunlap at tibco.com> wrote:

...from 'irisSAS' data? D = matrix(0, n, n) nam = c('Set', 'Ver', 'Vir') rownames(D) = nam colnames(D) = nam k = 0 for (i in 1:n) { for (j in 1:i) { k = k+1 D[i,j] = Mah[k] D[j,i] = Mah[k] } } D=sqrt(D) #D2 -> D library(mclust) dendroS = hclust(as.dist(D), method='single') dendroC = hclust(as.dist(D), method='complete') win.graph(w = 3.5, h = 6) split.screen(c(2, 1)) screen(1) plot(dendroS, main='Single', sub='', xlab='', ylab='', col='blue') screen(2) plot(dendroC, main=&...

I think the results differ only in the order of the labels. The following function puts the labels in a standard order and then the results are the same: canonicalize.dist <- function (distObject) { o <- order(labels(distObject)) as.matrix(distObject)[o, o, drop = FALSE] } identical(canonicalize.dist(d2), canonicalize.dist(d3)) [1] TRUE Bill Dunlap TIBCO Software

...179.38471, 17.20107, 0) n = 3 D = matrix(0, n, n) nam = c('Set', 'Ver', 'Vir') rownames(D) = nam colnames(D) = nam k = 0 for (i in 1:n) { for (j in 1:i) { k = k+1 D[i,j] = Mah[k] D[j,i] = Mah[k] } } D=sqrt(D) #D2 -> D library(mclust) dendroS = hclust(as.dist(D), method='single') dendroC = hclust(as.dist(D), method='complete') win.graph(w = 3.5, h = 6) split.screen(c(2, 1)) screen(1) plot(dendroS, main='Single', sub='', xlab='', ylab='', col='blue') screen(2) plot(dendroC, main=&...

I am having trouble displaying a dendrogram of evolutionary relationships (a phylogram imported from the ape package) as the vertical component of a heatmap, but keeping the hierarchical clustering of the horizontal component. The relationships of the vertical component in the generated heatmap are not that of the dendrogram, although the ordering is. In more detail, I am attempting to generate

Dear R experts, For quite some time I have been trying to solve a mistery of reading a seemingly trouble-free text file. The data is temperature reconstruction arranged as a huge grid, preceded by seven "header lines" (which you see better if file is opened in Firefox or Chrome). This is the data (gridded temperature reconstruction)

Dear List, I have a 47 species * 83 samples matrix containing percentage abundance data. I have two cluster analyses one of the samples and one of the species, and have ordered the rows and columns of the species by samples matrix according to these two cluster analyses. So far so good! Now what I want to do is create a plot with the species dendrogram at the top of the plot, the samples

Dear R users, I'm trying to create images of 2 symmetrical matrices using heatmap.2 of the 'gplots' library. Both matrices have the same row and column names For the first matrix I'm using the default clustering and column denrogram options: heatmap.2(Matrix1, symm=T, breaks=20, col=rich.colors(19), main="matrix1", density.info="histogram",

Hi all, I'm having a problem where by I'm trying to use the NeatMap draw.dendrogram function as it is more versatile in placement/direction of dendrograms, instead of the limited plot function. Using plot I can get my dendrograms to display the species name for each leaf of the tree. But I cannot figure out how to make this happen using draw.dendrogram.. genetic<-

Dear r-list, I am trying to visually seperate the two main clusters of a dendrogram. The idea is to use: 'edgePar=list(lty=3)' for 'dend1[[1]]' and 'edgePar=list(lty=1)' for 'dend1[[2]]' I have not found a way to solve this. Any suggestions? Patrick hc <- hclust(dist(USArrests), "ave") (dend1 <- as.dendrogram(hc)) par(mfrow=c(2,2)) plot(dend1)

Apologies, I thought I was following exactly that sentence and trying to make a minimal post that would waste as little developer bandwidth as possible given the lack of a better system. Anyway, I have been using R for like forever (20 years). In my current project, I have run into problems with stack overflows in R's dendrogram code when trying to use either str() or as.hclust() on very

I've rolled up R-1.9.1.tgz a short while ago. This is a maintenance version mainly to fix a number of minor bugs and issues (the most annoying one seems to have been the change to barplots of tables) and some installation issues. Because of the relocation the CVS archives, there is no direct access to the files until they show up on the CRAN master site. You can then get it from

I've rolled up R-1.9.1.tgz a short while ago. This is a maintenance version mainly to fix a number of minor bugs and issues (the most annoying one seems to have been the change to barplots of tables) and some installation issues. Because of the relocation the CVS archives, there is no direct access to the files until they show up on the CRAN master site. You can then get it from