dendrogram

问题 Please see my previous question for details relating to test data and commands used to create a dendrogram: Using R to cluster based on euclidean distance and a complete linkage metric, too many vectors? Here is a quick summary of my commands to make the dendrogram: un_exprs <- as.matrix(read.table("sample.txt", header=TRUE, sep = "\t", row.names = 1, as.is=TRUE)) exprs <- t(un_exprs) eucl_dist=dist(exprs,method = 'euclidean') hie_clust=hclust(eucl_dist, method = 'complete')\ dend <- as

问题 In Matlab you can designate the number of nodes in a dendrogram that you wish to plot as part of the dendrogram function: dendrogram(tree,P) generates a dendrogram plot with no more than P leaf nodes. My attempts to do the same with heatmap2 in R have failed miserably. The posts to stackoverflow and biostars have suggested using cutree but heatmap2 gets stuck with postings' suggestions on Rowv option. Here "TAD" is the data matrix 8 columns by 831 rows. # cluster it hr <- hclust(dist(TAD,

问题 I am trying to draw a dendrogram so that the labels on the branches match the group number from my cluster analysis. Currently the branches are simply labelled from left to right in the order that they appear, not the actual group number. Here is my current R code and resulting dendrogram: dst <- dist(Model_Results,method="binary") hca <- hclust(dst) clust <- cutree(hca,k=40) dend <-as.dendrogram(hca) library(dendextend) dend1 <- color_branches(dend, k = 40, groupLabels = TRUE) plot(dend1)

问题 I have data of dimension 8000x100. I need to cluster these 8000 items. I am more interested in the ordering of these items. I could get the desired result from the above code for small data but for higher dimension, I keep getting runtime error "RuntimeError: maximum recursion depth exceeded while getting the str of an object". Is there an alternate way to to get the reordered column from "Z". from hcluster import pdist, linkage, dendrogram import numpy from numpy.random import rand x = rand

问题 From a dendrogram which i created with hc<-hclust(kk) hcd<-as.dendrogram(hc) i picked a subbranch k=hcd[[2]][[2]][[2]][[2]][[2]][[2]][[2]][1] When i simply have k displayed, this gives: > k [[1]] [[1]][[1]] [1] 243 attr(,"label") [1] "NAfrica_002" attr(,"members") [1] 1 attr(,"height") [1] 0 attr(,"leaf") [1] TRUE [[1]][[2]] [1] 257 attr(,"label") [1] "NAfrica_016" attr(,"members") [1] 1 attr(,"height") [1] 0 attr(,"leaf") [1] TRUE attr(,"members") [1] 2 attr(,"midpoint") [1] 0.5 attr(,

问题 This question was migrated from Cross Validated because it can be answered on Stack Overflow. Migrated 6 years ago . I would like to create a dendrogram in R which has colored branches, like the one shown below. So far I used following commands to create a standard dendrogram: d <- dist(as.matrix(data[,29])) # find distance matrix hc <- hclust(d) # apply hirarchical clustering plot(hc,labels=data[,1], main="", xlab="") # plot the dendrogram How should I modify this code to obtain desired

问题 I have a list of hclust objects resulting from slight variations in one variable (for calculating the distance matrix) now I would like to make a consensus tree from this list. Is there a generic package to do this? I am hacking my way through some code from maanova and it seems to work - but it's ugly and it needs a lot of hacking since I am not doing "normal" bootstrapping (it's chemical data). /Palle Villesen, Denmark c1_list <- seq(10,100,by=10) c2 <- 30 e<- 1 mboot <- list() for (i in 1:

问题 Let's do a quick 3-clusters classification on the iris dataset with the FactoMineR package: library(FactoMineR) model <- HCPC(iris[,1:4], nb.clust = 3) summary(model$data.clust$clust) 1 2 3 50 62 38 We see that 50 observations are in cluster 1, 62 in cluster 2 and 38 in cluster 3. Now, we want to visualize these 3 clusters in a dendrogram, with the package dendextend which enables to make pretty ones: library(dendextend) library(dplyr) model$call$t$tree %>% as.dendrogram() %>% color_branches

问题 The R heatmap() documentation says for Rowv and Colv (i.e. row and column ordering parameters): If either is missing, as by default, then the ordering of the corresponding dendrogram is by the mean value of the rows/columns, i.e., in the case of rows, Rowv <- rowMeans(x, na.rm = na.rm). I thought it's as easy as that but now I guess there must be something more in the default ordering algorithm. Let's have this correlation matrix: m = matrix(nrow=7, ncol = 7, c(1,0.578090870728824,0

问题 I'm trying to learn how to use dendrograms in Python using SciPy . I want to get clusters and be able to visualize them; I heard hierarchical clustering and dendrograms are the best way. How can I "cut" the tree at a specific distance? In this example, I just want to cut it at distance 1.6 I looked up a tutorial on https://joernhees.de/blog/2015/08/26/scipy-hierarchical-clustering-and-dendrogram-tutorial/#Inconsistency-Method but the guy did some really confusing wrapper function using *