如何根据定义的组为树形图的标签着色？ （在R中）

3 回答

• 0

  我怀疑你要找的功能是 color_labels 或 get_leaves_branches_col . 标签的第一种颜色基于 cutree （如 color_branches ），第二种颜色允许您获取每个叶子的分支颜色，然后使用它为树的标签着色（如果您使用不寻常的方法为树枝着色（正如使用 branches_attr_by_labels 时那样） . 例如：

  # define dendrogram object to play with:
  hc <- hclust(dist(USArrests[1:5,]), "ave")
  dend <- as.dendrogram(hc)
  
  library(dendextend)
  par(mfrow = c(1,2), mar = c(5,2,1,0))
  dend <- dend %>%
           color_branches(k = 3) %>%
           set("branches_lwd", c(2,1,2)) %>%
           set("branches_lty", c(1,2,1))
  
  plot(dend)
  
  dend <- color_labels(dend, k = 3)
  # The same as:
  # labels_colors(dend)  <- get_leaves_branches_col(dend)
  plot(dend)
  

  

  无论哪种方式，你应该总是看看 set 函数，了解你的树形图可以做什么（这节省了记住所有不同函数名称的麻烦） .

  回复于 2024-04-29T16:53:36+08:00
• 8

  您可以查看本教程，该教程显示了几种可视化R组中树状图的解决方案

  https://rstudio-pubs-static.s3.amazonaws.com/1876_df0bf890dd54461f98719b461d987c3d.html

  但是，我认为最适合您数据的解决方案是由'dendextend'包提供的 . 请参阅教程（有关'iris'数据集的示例，与您的问题类似）：https://nycdatascience.com/wp-content/uploads/2013/09/dendextend-tutorial.pdf

  另见插图：http://cran.r-project.org/web/packages/dendextend/vignettes/Cluster_Analysis.html

  回复于 2024-04-29T16:53:36+08:00
• 1

  您可以尝试此解决方案，仅使用'MS.groups'和'var'更改'labs'，并将'MS.groups'转换为数字（可能使用as.numeric） . 它来自How to colour the labels of a dendrogram by an additional factor variable in R

  ## The data
  df <- structure(list(labs = c("a1", "a2", "a3", "a4", "a5", "a6", "a7", 
  "a8", "b1", "b2", "b3", "b4", "b5", "b6", "b7"), var = c(1L, 1L, 2L,     
  1L,2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L), td = c(13.1, 14.5, 16.7, 
  12.9, 14.9, 15.6, 13.4, 15.3, 12.8, 14.5, 14.7, 13.1, 14.9, 15.6, 14.6), 
  fd = c(2L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 2L, 4L, 2L, 1L, 4L, 3L, 3L)), 
  .Names = c("labs", "var", "td", "fd"), class = "data.frame", row.names = 
  c(NA, -15L))
  
  ## Subset for clustering
  df.nw = df[,3:4]
  
  # Assign the labs column to a vector
  labs = df$labs
  
  d = dist(as.matrix(df.nw))                          # find distance matrix 
  hc = hclust(d, method="complete")                   # apply hierarchical clustering 
  
  ## plot the dendrogram
  
  plot(hc, hang=-0.01, cex=0.6, labels=labs, xlab="") 
  
  ## convert hclust to dendrogram 
  hcd = as.dendrogram(hc)                             
  
  ## plot using dendrogram object
  plot(hcd, cex=0.6)                                  
  
  Var = df$var                                        # factor variable for colours
  varCol = gsub("1","red",Var)                        # convert numbers to colours
  varCol = gsub("2","blue",varCol)
  
  # colour-code dendrogram branches by a factor 
  
  # ... your code
  colLab <- function(n) {
    if(is.leaf(n)) {
      a <- attributes(n)
      attr(n, "label") <- labs[a$label]
      attr(n, "nodePar") <- c(a$nodePar, lab.col = varCol[a$label]) 
    }
    n
  }
  
  ## Coloured plot
  plot(dendrapply(hcd, colLab))
  

  回复于 2024-04-29T16:53:36+08:00