xcluster(data,distance="euclidean",clean=FALSE,tmp.in="tmp.txt",tmp.out="tmp.gtr")"euclidean", "pearson" or "notcenteredpearson".
Any unambiguous substring can be given.clean=FALSE), or you want a clean dendrogrammerge describes the merging of clusters
at step $i$ of the clustering.
If an element $j$ in the row is negative,
then observation $-j$ was merged at this stage.
If $j$ is positive then the merge
was with the cluster formed at the (earlier) stage $j$
of the algorithm.
Thus negative entries in merge indicate agglomerations
of singletons, and positive entries indicate agglomerations
of non-singletons.method for the particular agglomeration.merge will not have
crossings of the branches.d
(only returned if the distance object has a "method"
attribute).Xcluster does not use usual agglomerative methods (single, average, complete), but compute the distance between each groups' barycenter for the distance between two groups.
This have a problem for this kind of data:
| A | 0 |
| 0 | B |
| 0 | 1 |
| C | 0.9 |
| 0.5 | A |
Ie: a triangular in R$^2$, the distance between A and B is larger than the distance between the group A,B and C (with euclidean distance).
For that case it can be useful to use clean=TRUE and that mean
that you must not consider A and B as a group without C.
r2xcluster, xcluster2r,hclust, hcluster# Create data
set.seed(1)
m <- matrix(rep(1,3*24),ncol=3)
m[9:16,3] <- 3 ; m[17:24,] <- 3 #create 3 groups
m <- m+rnorm(24*3,0,0.5) #add noise
m <- floor(10*m)/10 #just one digits
# And once you have Xcluster program:
#
#h <- xcluster(m)
#
#plot(h)
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