.BG
.FN llmht
.TL
Loglikelihood for model-based hierarchical clustering.
.SH DESCRIPTION
Gives the loglikelihood for each stage of model-based hierarchical clustering.
.CS
llmht(tree, data, Vinv)
.PP
.RA
.AG tree
an `"mhtree"' object.
.AG data
the data used to produce the `"mhtree"' object.
.OA
.AG Vinv
approximate recoprocal hypervolume of the region from which the data is drawn.
The default (for those methods that need this quantity) is determined by the
function `hypvol'.
.RT
the loglikelihood corresponding to the initial partition and to each stage of 
merging in hierarchical clustering, together with the following attribute:
.RC nmerge
the number of clusters merged at each stage.
.SH NOTES
The value given is equal to the loglikelihood up to an additive constant. 
.br
For those models in which they arise, indeterminate terms are 
assigned the value `-k*log(volume)', where `k' is the number of observations 
associated with the term. 
.br
If you scaled your data before using `mhtree', be 
sure to use the same scaling when supplying the data to `llmht'.
.SA
`mhtree', `awe'
.EX
> data <- matrix(aperm(iris, c(1,3,2)), 150, 4)
> llmht(mhtree(iris.data),data)
.KW clustering
.WR


