prabclust {prabclus}R Documentation

Clustering of species ranges from presence-absence matrices

Description

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.

Usage

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, ...)

Arguments

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.

Value

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

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.

Author(s)

Christian Hennig hennig@math.uni-hamburg.de http://www.math.uni-hamburg.de/home/hennig/

References

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.

See Also

EMclustN, summary.EMclustN, NNclean, cmdscale, isoMDS, sammon, prabinit.

Examples

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))

[Package prabclus version 1.0-2 Index]