| prabclust {prabclus} | R Documentation |
Clusters a presence-absence matrix object by calculating an MDS from
the distances, and applying maximum likelihood Gaussian mixtures clustering
with "noise" (package mclust) to the MDS points. The solution
is plotted. A standard execution will be
prabmatrix <- prabinit(file="path/prabmatrixfile",
neighborhood="path/neighborhoodfile")
clust <- prabclust(prabmatrix)
print(clust)
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.
prabclust(prabobj, mdsmethod = "classical", mdsdim = 4, nnk = ceiling(prabobj$n.species/40), nclus = 0:9, modelid = "noVVV") ## S3 method for class 'prabclust': print(x, bic=FALSE, ...)
prabobj |
object of class prab as
generated by prabinit. Presence-absence data to be analyzed.
|
mdsmethod |
"classical", "kruskal", or
"sammon". The MDS method
to transform the distances to data points. "classical" indicates
metric MDS by function cmdscale, "kruskal" is
non-metric MDS. |
mdsdim |
integer. Dimension of the MDS points. |
nnk |
integer. Number of nearest neighbors to determine the
initial noise estimation by NNclean. |
nclus |
vector of integers. Numbers of clusters to perform the mixture estimation. |
modelid |
string. Model name for EMclustN (see the
corresponding help page). Additionally, "noVVV" is possible, which
fits all methods except "VVV". |
x |
object of class prabclust. Output of
prabclust. |
bic |
logical. If TRUE, information about the BIC
criterion to choose the model is displayed. |
... |
necessary for summary method. |
print.prabclust does not produce output.
prabclust generates an object of class prabclust. This is a
list with components
clustering |
vector of integers indicating the cluster memberships of
the species. Noise can be recognized by output component symbols. |
clustsummary |
output object of summary.EMclustN. A list
giving the optimal (according to BIC) parameters,
conditional probabilities `z', and loglikelihood, together with
the associated classification and its uncertainty. |
bicsummary |
output object of EMclustN. Bayesian Information
Criterion for the specified mixture models and numbers of clusters. |
points |
numerical matrix. MDS configuration. |
nnk |
see above. |
mdsdim |
see above. |
mdsmethod |
see above. |
symbols |
vector of characters, similar to clustering, but
indicating estimated noise and points belonging to
one-point-components (which should be interpreted as some kind of
noise as well) by "N". |
Note that we used mdsmethod="kruskal" in our publications, but
we prefer the new default mdsmethod="classical" now, because we
discovered some numerical instabilities of the
isoMDS-implementation in connection with our distance matrices.
Christian Hennig hennig@math.uni-hamburg.de http://www.math.uni-hamburg.de/home/hennig/
Hennig, C. and Hausdorf, B. (2002) Distance-based parametric bootstrap tests for clustering of species ranges, submitted, http://stat.ethz.ch/Research-Reports/110.html.
EMclustN, summary.EMclustN,
NNclean, cmdscale,
isoMDS, sammon,
prabinit.
data(kykladspecreg)
# Note: If you do not use the installed package, replace this by
# kykladspecreg <- read.table("(path/)kykladspecreg.dat")
data(nb)
# Note: If you do not use the installed package, replace this by
# nb <- list()
# for (i in 1:34)
# nb <- c(nb,list(scan(file="(path/)nb.dat",
# skip=i-1,nlines=1)))
set.seed(1234)
x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb)
# If you want to use your own ASCII data files, use
# x <- prabinit(file="path/prabmatrixfile",
# neighborhood="path/neighborhoodfile")
print(prabclust(x))