.BG
.FN awe
.TL
Approximate weight of evidence for model-based hierarchical clustering.
.SH DESCRIPTION
Computes a Bayesian criterion for assessing the number of clusters present
in the data.
.CS
awe(tree, data)
.PP
.RA
.AG tree
an `"mhtree"' object.
.AG data
the data used to produce the `"mhtree"' object.
.RT
the approximate weight of evidence for each possible stage of merging.
.SH NOTES
Since `"mhtree"' allows stopping and
starting at any stage, the result will contain NAs for those stages that have 
been eliminated. 
.br
If you scaled your data before using `mhtree', be sure
to use the same scaling when supplying the data to `awe'.
.SH REFERENCES
J. D. Banfield and A. E. Raftery, Model-based Gaussian and non-Gaussian
Clustering, \fIBiometrics, \fR49:803-821 (1993).
.SA
`mhtree', `llmht'
.EX
> data <- matrix(aperm(iris, c(1,3,2)), 150, 4)
> awe(mhtree(data)), data)
> plot(awe)

.KW clustering
.WR


