prabclus (version 2.0-2)

prabclust: Clustering of species ranges from presence-absence matrices (mixture method)

Description

Clusters a presence-absence matrix object by calculating an MDS from the distances, and applying maximum likelihood Gaussian mixtures clustering with "noise" (package mclust02) 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 = "all")
## 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

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). modelid="all" compares all possible models. 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".

References

Hennig, C. and Hausdorf, B. (2004) Distance-based parametric bootstrap tests for clustering of species ranges. Computational Statistics and Data Analysis 45, 875-896. http://stat.ethz.ch/Research-Reports/110.html.

See Also

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

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))