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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