Description Usage Arguments Details Value Note Author(s) See Also Examples
Function takes a hierarchical clustering tree from
hclust
and a vector of values and reorders the
clustering tree in the order of the supplied vector, maintaining the
constraints on the tree. This is a method of generic function
reorder
and an alternative to reordering a
"dendrogram"
object with reorder.dendrogram
1 2 3 4 5 6 7 |
x |
hierarchical clustering from |
wts |
numeric vector for reordering. |
agglo.FUN |
a function for weights agglomeration, see below. |
display |
return |
... |
additional arguments (ignored). |
Dendrograms can be ordered in many ways. The reorder
function
reorders an hclust
tree and provides an alternative to
reorder.dendrogram
which can reorder a
dendrogram
. The current function will also work
differently when the agglo.FUN
is "mean"
: the
reorder.dendrogram
will always take the direct mean of
member groups ignoring their sizes, but this function will used
weighted.mean
weighted by group sizes, so that the
group mean is always the mean of member leaves (terminal nodes). If
you want to ignore group sizes, you can use unweighted mean with
"uwmean"
.
The function accepts only a limited list of agglo.FUN
functions for assessing the value of wts
for groups. The
ordering is always ascending, but the order of leaves can be
reversed with rev
.
Function scores
finds the coordinates of nodes as a two-column
matrix. For terminal nodes (leaves) this the value at which the item
is merged to the tree, and the labels can still hang
below this
level (see plot.hclust
).
Reordered hclust
result object with added item
value
that gives the value of the statistic at each merge
level.
These functions should really be in base R.
Jari Oksanen
hclust
for getting clustering trees,
as.hclust.spantree
to change a vegan minimum
spanning tree to an hclust
object, and
dendrogram
and reorder.dendrogram
for an
alternative implementation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ## reorder by water content of soil
data(mite, mite.env)
hc <- hclust(vegdist(wisconsin(sqrt(mite))))
ohc <- with(mite.env, reorder(hc, WatrCont))
plot(hc)
plot(ohc)
## label leaves by the observed value, and each branching point
## (internal node) by the cluster mean
with(mite.env, plot(ohc, labels=round(WatrCont), cex=0.7))
ordilabel(scores(ohc), label=round(ohc$value), cex=0.7)
## Slightly different from reordered 'dendrogram' which ignores group
## sizes in assessing means.
den <- as.dendrogram(hc)
den <- with(mite.env, reorder(den, WatrCont, agglo.FUN = mean))
plot(den)
|
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