prabclus package overview
Here is a list of the main functions in package prabclus. Most other functions are auxiliary functions for these.
Initialises presence/absence-, abundance- and multilocus data with dominant markers for use with most other key prabclus-functions.
Initialises multilocus data with codominant markers for use with key prabclus-functions.
Generates the input format required by
Tests for clustering and nestedness
Computes the tests introduced in Hausdorf and Hennig (2003) and Hennig and Hausdorf (2004; these tests occur in some further publications of ours but this one is the most detailed statistical reference) for presence/absence data. Allows use of the geco-dissimilarity (Hennig and Hausdorf, 2006).
Computes the test introduced in Hausdorf and Hennig (2007) for abundance data.
A classical distance-based test for homogeneity going back to Erdos and Renyi (1960) and Ling (1973).
Species clustering for biotic element analysis (Hausdorf and Hennig, 2007, Hennig and Hausdorf, 2004 and others), clustering of individuals for species delimitation (Hausdorf and Hennig, 2010) based on Gaussian mixture model clustering with noise as implemented in R-package
mclust, Fraley and Raftery (1998), on output of multidimensional scaling from distances as computed by
alleleinit. See also
stressvalsfor help with choosing the number of MDS-dimensions.
An unpublished alternative to
prabclustusing hierarchical clustering methods.
Visualisation of clusters of genetic markers vs. clusters of species.
Nearest neighbor based classification of observations as noise/outliers according to Byers and Raftery (1998).
Shared allele distance (see the corresponding help pages for references).
geco coefficient, taking geographical distance into account.
Quantitative Kulczynski dissimilarity for abundance data.
Small conversion functions
Computes geographical distances from geographical coordinates.
Computes a neighborhood list from geographical distances.
A somewhat restricted function for conversion of different file formats used for genetic data with codominant markers.
Byers, S. and Raftery, A. E. (1998) Nearest-Neighbor Clutter Removal for Estimating Features in Spatial Point Processes, Journal of the American Statistical Association, 93, 577-584.
Erdos, P. and Renyi, A. (1960) On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences 5, 17-61.
Fraley, C. and Raftery, A. E. (1998) How many clusters? Which clusterin method? - Answers via Model-Based Cluster Analysis. Computer Journal 41, 578-588.
Hausdorf, B. and Hennig, C. (2003) Nestedness of north-west European land snail ranges as a consequence of differential immigration from Pleistocene glacial refuges. Oecologia 135, 102-109.
Hausdorf, B. and Hennig, C. (2007) Null model tests of clustering of species, negative co-occurrence patterns and nestedness in meta-communities. Oikos 116, 818-828.
Hausdorf, B. and Hennig, C. (2010) Species Delimitation Using Dominant and Codominant Multilocus Markers. Systematic Biology, 59, 491-503.
Hennig, C. and Hausdorf, B. (2004) Distance-based parametric bootstrap tests for clustering of species ranges. Computational Statistics and Data Analysis 45, 875-896.
Hennig, C. and Hausdorf, B. (2006) A robust distance coefficient between distribution areas incorporating geographic distances. Systematic Biology 55, 170-175.
Ling, R. F. (1973) A probability theory of cluster analysis. Journal of the American Statistical Association 68, 159-164.
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