# [R] Plotting a section of a dendrogram

Wiener, Matthew matthew_wiener at merck.com
Mon Jul 22 14:37:03 CEST 2002

```Christophe --

Here are a couple of routines I've written that might help.  You call
f.get.subtrees, and get back a list, each element of which is one of the
subtrees.  You can then plot each subtree individually.  (This won't give
you the relation between the subtrees, though.

k and h are the same as in cutree.  You probably don't need add.element; I
used it to attach some additional information to the tree.

Hope this helps.

Matthew Wiener
Applied Computer Science & Mathematics Dept.
Merck Research Labs
Rahway, NJ 07090
732-594-5303

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"f.get.subtrees" <-
function(tree, k = NULL, h = NULL,
## tree is a tree, k and h are as in cutree.
## connected with tree nodes. The additional elements referred
## to should have the same length as tree\$labels.
##
groups <- cutree(tree, k, h)
subtrees <- list()
for(i in 1:length(unique(groups))){
subtrees[[i]] <- f.make.subtree(tree, groups, i,
}
subtrees
}

"f.make.subtree" <-
function (tree, groups, n = 1, add.element = NULL)
{
which.nodes <- which(groups == n)
names(which.nodes) <- NULL
## steal some code from plot.hclust
use.labels <-
if (is.null(tree\$labels))
paste(1:(nrow(tree\$merge) + 1))
else as.character(tree\$labels)

subtree.labels <- use.labels[which.nodes]

subtree.order <- tree\$order[use.labels[tree\$order] %in%
subtree.labels]
which.merge.elements <- which(-tree\$merge %in% which.nodes)
which.merge.rows <- sort(unique(which.merge.elements%%nrow(tree\$merge)))
which.merge.rows[which.merge.rows == 0] <- nrow(tree\$merge)
old.length <- 0
new.length <- length(which.merge.rows)
if (length(which.merge.elements) == 1) {
res <- list(merge = NULL, heights = tree\$height[which.merge.rows],
order = 1,
labels = {if(is.null(tree\$labels)) NULL else
subtree.labels},
method = tree\$method,
call = tree\$call, dist.method = tree\$dist.method)
}
else {
while (new.length - old.length > 0) {
old.length <- new.length
new.rows <- numeric(0)
more.new.rows <- numeric(0)
row.elements <- as.vector(tree\$merge[which.merge.rows,
])
pos.elements <- row.elements[row.elements > 0]
if (length(pos.elements) > 0)
new.rows <- pos.elements[apply(tree\$merge[pos.elements,
, drop = F], 1,
function(x) {
any(x > 0) & all(-x[x <
0] %in% which.nodes)
})]
more.new.rows <- which(apply(tree\$merge, 1, function(x) {
all(x %in% which.merge.rows)
}))
which.merge.rows <- sort(unique(c(which.merge.rows,
new.rows, more.new.rows)))
new.length <- length(which.merge.rows)
}
merge.list <- tree\$merge[which.merge.rows, , drop = F]
merge.height <- tree\$height[which.merge.rows]
pos.elements <- sort(merge.list[merge.list > 0])
for (i in seq(along = pos.elements)) merge.list[merge.list ==
pos.elements[i]] <- which(pos.elements[i] ==
which.merge.rows)
neg.elements <- merge.list[merge.list < 0]
minus.neg.elements <- -neg.elements
num.elements <- length(neg.elements)
change.table <- cbind(sort(minus.neg.elements), 1:num.elements)
for (i in 1:num.elements) {
merge.list[merge.list == -change.table[i, 1]] <- -change.table[i,
2]
}
old.order <- subtree.order
subtree.order <- match(use.labels[old.order], subtree.labels)
res <- list(merge = merge.list, height = merge.height,
order = subtree.order,
labels = {if(is.null(tree\$labels)) NULL else
subtree.labels},
method = tree\$method,
call = list(match.call(), tree\$call), dist.method =
tree\$dist.method)
class(res) <- "hclust"
for (i in seq(along = add.element)) {
}
}
res
}

-----Original Message-----
From: White.Denis at epamail.epa.gov [mailto:White.Denis at epamail.epa.gov]
Sent: Friday, July 19, 2002 7:49 PM
To: Christophe Champod
Cc: r-help at stat.math.ethz.ch
Subject: Re: [R] Plotting a section of a dendrogram

> I have performed clustering analysis with hclust (Ward's method) on a
> database of 800 samples. As you may imagine the full dendrogram is not
> really readable. I have obtained groups with cutree. I would like to
plot
> sub-sections of my big dendrogram to show group 1, group 2 and so on.

I don't think R has anything like subtree in Splus, unfortunately.  I
think what has been recommended in the past is to create a very large
format plot (using a large paper size in pdf(), for example) and then
zoom in to look at subtrees.

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```