as.seqtree {WeightedCluster} | R Documentation |
Convert a hierarchical clustering object to a seqtree object which can then be displayed using seqtreedisplay
.
as.seqtree(object, seqdata, diss, weighted=TRUE, ...) ## S3 method for class 'twins' as.seqtree(object, seqdata, diss, weighted=TRUE, ncluster, ...) ## S3 method for class 'hclust' as.seqtree(object, seqdata, diss, weighted=TRUE, ncluster, ...)
object |
An object to be converted to a |
seqdata |
State sequence object. |
diss |
A dissimilarity matrix or a dist object (see |
weighted |
Logical. If |
ncluster |
Maximum number of cluster. The tree will be builded until this number of cluster. |
... |
Additionnal parameters passed to/from methods. |
By default as.seqtree
try to convert the object to a data.frame
assuming that it contains a list of nested clustering solutions.
Be aware that seqtree
and as.seqtree
only support binary splits.
If object
is an hclust
or twins
objects (i.e. hierarchical clustering output, see hclust
, diana
or agnes
), the function returns a seqtree
object reproducing the agglomerative schedulde.
A seqtree
object.
data(mvad) ## Aggregating state sequence aggMvad <- wcAggregateCases(mvad[, 17:86], weights=mvad$weight) ## Creating state sequence object mvad.seq <- seqdef(mvad[aggMvad$aggIndex, 17:86], weights=aggMvad$aggWeights) ## COmpute distance using Hamming distance diss <- seqdist(mvad.seq, method="HAM") ## Ward clustering wardCluster <- hclust(as.dist(diss), method="ward", members=aggMvad$weight) st <- as.seqtree(wardCluster, seqdata=mvad.seq, diss=diss, weighted=TRUE, ncluster=10) print(st) ## You typically want to run (You need to install GraphViz before) ## seqtreedisplay(st, type="d", border=NA)