| hprabclust {prabclus} | R Documentation |
Clusters a presence-absence matrix object by taking the
'h-cut'-partition of a hierarchical clustering and
declaring all members of too small clusters as 'noise' (this gives a
distance-based clustering method, which estimates the number of
clusters and allows for noise/non-clustered points). Note that this
is experimental and up to now, the prabclust-solutions
have been more convincing for all data sets we have tested.
Note: Data formats are described
on the prabinit help page. You may also consider the example datasets
kykladspecreg.dat and nb.dat. Take care of the
parameter rows.are.species of prabinit.
hprabclust(prabobj, cutdist=0.4, cutout=cutdist,
method="complete", nnout=2)
prabobj |
object of class prab as
generated by prabinit. Presence-absence data to be analyzed. |
cutdist |
non-negative integer. Cutoff distance to determine the
partition, see cutree. |
cutout |
non-negative integer. Points that have at most
nnout distances smaller or equal than cutout are
treated as noise. |
method |
string. Clustering method, see hclust. |
nnout |
non-negative integer. Members of clusters with less or
equal than nnout points or that have less or equal than
nnout neighbors closer than cutout are treated as noise. |
hprabclust generates an object of class comprabclust. This is a
list with components
clustering |
vector of integers indicating the cluster memberships of
the species (cutout-outliers are noise, but small clusters
are allowed). Noise can be recognized by output component symbols. |
rclustering |
vector of integers indicating the cluster memberships of
the species, noise as described under nnout.
Noise can be recognized by output component symbols. |
cutdist |
see above. |
cutout |
see above. |
nnout |
see above. |
noisen |
number of points minus cutout-outliers. |
symbols |
vector of characters, similar to clustering, but
indicating estimated noise by "N". |
hclustering |
output of hclust. |
Christian Hennig chrish@stats.ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche
data(kykladspecreg)
data(nb)
data(waterdist)
x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb,
geodist=waterdist, distance="geco")
hprabclust(x)