prabclus package overview

Description

Here is a list of the main functions in package prabclus. Most other functions are auxiliary functions for these.

Initialisation

prabinit

Initialises presence/absence-, abundance- and multilocus data with dominant markers for use with most other key prabclus-functions.

alleleinit

Initialises multilocus data with codominant markers for use with key prabclus-functions.

alleleconvert

Generates the input format required by alleleinit.

Tests for clustering and nestedness

prabtest

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).

abundtest

Computes the test introduced in Hausdorf and Hennig (2007) for abundance data.

homogen.test

A classical distance-based test for homogeneity going back to Erdos and Renyi (1960) and Ling (1973).

Clustering

prabclust

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 prabinit or alleleinit. See also stressvals for help with choosing the number of MDS-dimensions.

hprabclust

An unpublished alternative to prabclust using hierarchical clustering methods.

lociplots

Visualisation of clusters of genetic markers vs. clusters of species.

NNclean

Nearest neighbor based classification of observations as noise/outliers according to Byers and Raftery (1998).

Dissimilarity matrices

alleledist

Shared allele distance (see the corresponding help pages for references).

dicedist

Dice distance.

geco

geco coefficient, taking geographical distance into account.

jaccard

Jaccard distance.

kulczynski

Kulczynski dissimilarity.

qkulczynski

Quantitative Kulczynski dissimilarity for abundance data.

Communities

communities

Constructs communities from geographical distances between individuals.

communitydist

chord-, phiPT- and various versions of the shared allele distance between communities.

Tests for equality of dissimilarity-based regression

regeqdist

Jackknife-based test for equality of two independent regressions between distances (Hausdorf and Hennig 2019).

regdistbetween

Jackknife-based test for equality of regression involving all distances and regression involving within-group distances only (Hausdorf and Hennig 2019).

regdistbetweenone

Jackknife-based test for equality of regression involving within-group distances of a reference group only and regression involving between-group distances (Hausdorf and Hennig 2019).

Small conversion functions

coord2dist

Computes geographical distances from geographical coordinates.

geo2neighbor

Computes a neighborhood list from geographical distances.

alleleconvert

A somewhat restricted function for conversion of different file formats used for genetic data with codominant markers.

Data sets

kykladspecreg, siskiyou, veronica, tetragonula.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/

References

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.

Hausdorf, B. and Hennig, C. (2019) Species delimitation and geography. Submitted.

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.

Parametric bootstrap test for clustering in abundance matrices

Description

Parametric bootstrap test of a null model of i.i.d., but spatially autocorrelated species against clustering of the species' population patterns. Note that most relevant functionality of prabtest (except of the use of the geco distance) is also included in abundtest, so that abundtest can also be used on binary presence-absence data. In spite of the lots of parameters, a standard execution (for the default test statistics, see parameter teststat below) will be
prabmatrix <- prabinit(file="path/abundmatrixfile", neighborhood="path/neighborhoodfile")
test <- abundtest(prabmatrix)
summary(test)
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

abundtest(prabobj, teststat = "distratio", tuning = 0.25,
                       times = 1000, p.nb = NULL, 
                       prange = c(0, 1), nperp = 4, step = 0.1, step2 = 0.01, 
                       twostep = TRUE, species.fixed=TRUE, prab01=NULL,
                       groupvector=NULL,
                       sarestimate=prab.sarestimate(prabobj),
                       dist = prabobj$distance,
                       n.species = prabobj$n.species)

Arguments

Details

For presence-absence data, the routine is described in prabtest. For abundance data, the first step under the null model is to simulated presence-absence patterns as in prabtest. The second step is to fit a simultaneous autoregression (SAR) model (Ripley 1981, section 5.2) to the log-abundances, see prab.sarestimate. The simulation from the null model is implemented in regpop.sar. For more details see Hennig and Hausdorf (2004) for presence-absence data and Hausdorf and Hennig (2007) for abundance data and the test statistics "mean" and "groups", which can also be applied to binary data.

If p.nb=NA was specified, a diagnostic plot for the estimation of pd is plotted by autoconst. For details see Hennig and Hausdorf (2004) and the help pages of the cited functions.

Value

An object of class prabtest, which is a list with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

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.

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.

Ripley, B. D. (1981) Spatial Statistics. Wiley.

See Also

prabinit generates objects of class prab.

autoconst estimates pd from such objects.

prabtest (analogous function for presence-absence data).

regpop.sar generates populations from the null model.

prab.sarestimate (parameter estimators for simultaneous autoregression model). This calls

errorsarlm (original estimation function from package spdep).

Some more information on the test statistics is given in homogen.test, lcomponent, distratio, nn, incmatrix.

Summary and print methods: summary.prabtest.

Examples

# Note: NOT RUN.
# This needs package spdep and a bunch of packages that are
# called by spdep!
# data(siskiyou)
# set.seed(1234)
# x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb,
#              distance="logkulczynski")
# a1 <- abundtest(x, times=5, p.nb=0.0465)
# a2 <- abundtest(x, times=5, p.nb=0.0465, teststat="groups",
#                 groupvector=siskiyou.groups)
# These settings are chosen to make the example execution
# faster; usually you will use abundtest(x).
# summary(a1)
# summary(a2)

Converts alleleobject into binary matrix

Description

Converts alleleobject with codominant markers into binary matrix with a column for each marker.

Usage

  allele2zeroone(alleleobject)

Arguments

Value

A 0-1-matrix with individuals as rows and markers (alleles) as columns.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

Examples

  data(tetragonula)
  ta <- alleleconvert(strmatrix=tetragonula[21:50,])
  tai <- alleleinit(allelematrix=ta)
  allele2zeroone(tai)

Format conversion for codominant marker data

Description

Codominant marker data (which here means: data with several diploid loci; two alleles per locus) can be represented in various ways. This function converts the formats "genepop" and "structure" into "structurama" and "prabclus". "genepop" is a version of the format used by the package GENEPOP (Rousset, 2008), "structure" is a version of what is used by STRUCTURE (Pritchard et al., 2000), another one is "structureb". "structurama" is a version of what is used by STRUCTURAMA (Huelsenbeck and Andolfatto, 2007) and "prabclus" is required by the function alleleinit in the present package.

Usage

  alleleconvert(file=NULL,strmatrix=NULL, format.in="genepop",
                          format.out="prabclus",
                          alength=3,orig.nachar="000",new.nachar="-",
                          rows.are.individuals=TRUE, firstcolname=FALSE,
                          aletters=intToUtf8(c(65:90,97:122),multiple=TRUE),
                          outfile=NULL,skip=0)

Arguments

Details

The formats are as follows (described is the format within R, i.e., for the input, the format of strmatrix; if file is specified, the file is read with read.table(file,colClasses="character") and should give the format explained below - note that colClasses="character" implies that quotes are not needed in the input file):

genepop

Alleles are coded by strings of length alength and there is no space between the two alleles in a locus, so a value of "258260" means that in the corresponding locus the two alleles have codes 258 and 260.

structure

Alleles are coded by strings of arbitrary length. Two rows correspond to each inidividual, the first row containing the first alleles in all loci and the second row containing the second ones.

structureb

Alleles are coded by strings of arbitrary length. One row corresponds to each inidividual, containing first and second alleles in all loci (first and second allele of first locus, first and second allele of second locus etc.). This starts in the third row (first two have locus names and other information).

structurama

Alleles are coded by strings of arbitrary length. the two alleles in each locus are written with brackets around them and a comma in between, so "258260" in "genepop" corresponds to "(258,260)" in "structurama".

prabclus

Alleles are coded by a single character and there is no space between the two alleles in a locus (e.g., "AC").

Value

A matrix of strings in the format specified as format.out with an attribute "alevels", a vector of all used allele codes if format.out=="prabclus", otherwise vector of allele codes of last locus.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Huelsenbeck, J. P., and P. Andolfatto (2007) Inference of population structure under a Dirichlet process model. Genetics 175, 1787-1802.

Pritchard, J. K., M. Stephens, and P. Donnelly (2000) Inference of population structure using multi-locus genotype data. Genetics 155, 945-959.

Rousset, F. (2008) genepop'007: a complete re-implementation of the genepop software for Windows and Linux. Molecular Ecology Resources 8, 103-106.

See Also

alleleinit

Examples

  data(tetragonula)
# This uses example data file Heterotrigona_indoFO.dat
  str(alleleconvert(strmatrix=tetragonula))
  strucmatrix <-
    cbind(c("I1","I1","I2","I2","I3","I3"),
    c("122","144","122","122","144","144"),c("0","0","21","33","35","44"))
  alleleconvert(strmatrix=strucmatrix,format.in="structure",
    format.out="prabclus",orig.nachar="0",firstcolname=TRUE)
  alleleconvert(strmatrix=strucmatrix,format.in="structure",
    format.out="structurama",orig.nachar="0",new.nachar="-9",firstcolname=TRUE)

Shared allele distance for diploid loci

Description

Shared allele distance for codominant markers (Bowcock et al., 1994). One minus proportion of alleles shared by two individuals averaged over loci (loci with missing values for at least one individual are ignored).

Usage

  alleledist(allelelist,ni,np,count=FALSE)

Arguments

Value

A symmetrical matrix of shared allele distances between individuals.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Bowcock, A. M., Ruiz-Linares, A., Tomfohrde, J., Minch, E., Kidd, J. R., Cavalli-Sforza, L. L. (1994) High resolution of human evolutionary trees with polymorphic microsatellites. Nature 368, 455-457.

See Also

alleleinit, unbuild.charmatrix

Examples

  data(tetragonula)
  tnb <-
  coord2dist(coordmatrix=tetragonula.coord[1:50,],cut=50,file.format="decimal2",neighbors=TRUE)
  ta <- alleleconvert(strmatrix=tetragonula[1:50,])
  tai <- alleleinit(allelematrix=ta,neighborhood=tnb$nblist,distance="none")
  str(alleledist((unbuild.charmatrix(tai$charmatrix,50,13)),50,13)) 

Diploid loci matrix initialization

Description

alleleinit converts genetic data with diploid loci as generated by alleleconvert into an object of class alleleobject. print.alleleobject is a print method for such objects.

Usage

alleleinit(file = NULL, allelematrix=NULL,
                        rows.are.individuals = TRUE, 
    neighborhood = "none", distance = "alleledist", namode="variables",
                       nachar="-", distcount=FALSE) 


## S3 method for class 'alleleobject'
print(x, ...)

Arguments

Details

The required input format is the output format "prabclus" of alleleconvert. Alleles are coded by a single character, so diploid loci need to be pairs of characters without space between the two alleles (e.g., "AC"). The input needs to be an individuals*loci matrix or data frame (or a file that produces such a data frame by read.table(file,stringsAsFactors=FALSE))

Value

alleleinit produces an object of class alleleobject (note that this is similar to class prab; for example both can be used with prabclust), which is a list with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

See Also

alleleconvert, alleledist, prabinit.

Examples

# Only 50 observations are used in order to have a fast example.
  data(tetragonula)
  tnb <-
  coord2dist(coordmatrix=tetragonula.coord[1:50,],cut=50,file.format="decimal2",neighbors=TRUE)
  ta <- alleleconvert(strmatrix=tetragonula[1:50,])
  tai <- alleleinit(allelematrix=ta,neighborhood=tnb$nblist)
  print(tai)

Internal: compares two pairs of alleles

Description

Used for computation of the genetic distances alleledist.

Usage

  allelepaircomp(allelepair1,allelepair2,method="sum")

Arguments

Value

If method=="sum", number of shared alleles (0, 1 or 2), or NA. If method=="geometrical", 0, 0.5, sqrt(0.5) (in case that one of the allelepairs is double such as in c("A","B"),c("A","A")) or 1, or NA.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

See Also

alleledist

Examples

  allelepaircomp(c("A","B"),c("A","C"))

Spatial autocorrelation parameter estimation

Description

Monte Carlo estimation of the disjunction/spatial autocorrelation parameter pd for the simulation model used in randpop.nb, used for tests for clustering of presence-absence data.

autoconst is the main function; autoreg performs the simulation and is executed within autoconst.

Usage

autoconst(x, prange = c(0, 1), twostep = TRUE, step1 = 0.1,
step2 = 0.01, plot = TRUE, nperp = 4, ejprob = NULL,
species.fixed = TRUE, pdfnb=FALSE, ignore.richness=FALSE)

autoreg(x, probs, ejprob, plot = TRUE, nperp = 4, species.fixed = TRUE,
pdfnb=FALSE, ignore.richness=FALSE)

Arguments

Details

The spatial autocorrelation parameter pd of the model for the generation of presence-absence data sets used by randpop.nb can be estimated by use of the observed disjuction probability ejprob which is the sum of all species' connectivity components minus the number of species divided by the number of "presence" entries minus the number of species. This is done by a simulation of artificial data sets with characteristics of x and different pd-values, governed by prange, step1, step2 and nperp. ejprob is then calculated for all simulated populations. A linear regression of ejprob on pd is performed and the estimator of pd is determined by computing the inverse of the regression function for the ejprob-value of x.

Value

autoconst produces the same list as autoreg with additional component ejprob. The components are

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Hausdorf, B. and Hennig, C. (2003) Biotic Element Analysis in Biogeography. To appear in Systematic Biology.

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.

Hennig, C. and Hausdorf, B. (2004) Distance-based parametric bootstrap tests for clustering of species ranges. Computational Statistics and Data Analysis 45, 875-896.

See Also

randpop.nb, prabinit, con.comp

Examples

options(digits=4)
data(kykladspecreg)
data(nb)
set.seed(1234)
x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb)
ax <- autoconst(x,nperp=2,step1=0.3,twostep=FALSE)

Internal: create character matrix out of allele list

Description

For use in alleleinit. Creates a matrix of characters in which there are two rows for every individual corresponding to the two alleles in every locus (column) out of a list of lists, such as required by alleledist.

Usage

   build.charmatrix(allelelist,n.individuals,n.variables)

Arguments

Value

A matrix of characters in which there are two rows for every individual corresponding to the two alleles in every locus (column).

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

See Also

alleleinit, unbuild.charmatrix

Examples

  alist <- list()
  alist[[1]] <- list(c("A","A"),c("B","A"),c(NA,NA))
  alist[[2]] <- list(c("A","C"),c("B","B"),c("A","D"))
  build.charmatrix(alist,3,2)

Internal: generates neighborhood list for diploid loci

Description

This is for use in alleleinit. Given a neighborhood list of individuals, a new neighborhood list is generated in which there are two entries for each individual (entry 1 and 2 refer to individual one, 3 and 4 to individual 2 and so on). Neighborhoods are preserved and additionally the two entries belonging to the same individual are marked as neighbors.

Usage

  build.ext.nblist(neighbors,n.individuals=length(neighbors))

Arguments

Value

list with 2*n.inidividuals vectors of integers as described above.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

See Also

alleleinit

Examples

  data(veronica)
  vnb <- coord2dist(coordmatrix=veronica.coord[1:20,], cut=20,
    file.format="decimal2",neighbors=TRUE)
  build.ext.nblist(vnb$nblist)

Generate spatial weights from prabclus neighborhood list

Description

This generates a listw-object as needed for estimation of a simultaneous autoregression model in package spdep from a neighborhood list of the type generated in prabinit.

Usage

build.nblist(prabobj,prab01=NULL,style="C")

Arguments

Value

A 'listw' object with the following members:

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

See Also

nb2listw (which is called)

Examples

# Not run; requires package spdep
# data(siskiyou)
# x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb,
#             distance="logkulczynski")
# build.nblist(x)

Simulation of presence-absence matrices (clustered)

Description

Generates a simulated matrix where the rows are interpreted as regions and the columns as species, 1 means that a species is present in the region and 0 means that the species is absent. Species are generated in order to produce 2 clusters of species with similar ranges. Spatial autocorrelation of a species' presences is governed by the parameter p.nb and a list of neighbors for each region.

Usage

cluspop.nb(neighbors, p.nb = 0.5, n.species, clus.specs, reg.group,
grouppf = 10, n.regions = length(neighbors),
vector.species = rep(1, n.species), pdf.regions = rep(1/n.regions,
n.regions), count = TRUE, pdfnb = FALSE)

Arguments

Details

The non-clustered species are generated as explained on the help page for randpop.nb. The general principle for the clustered species is the same, but with modified probabilities for the regions. For each clustered species, one of the two groups of regions is drawn, distributed according to the sum of its regions' probability given by pdf.regions. The first region of such a species is only drawn from the regions of this group.

Value

A 0-1-matrix, rows are regions, columns are species.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

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.

See Also

randpop.nb,

autoconst estimates p.nb from matrices of class prab. These are generated by prabinit.

Examples

data(nb)
set.seed(888)
cluspop.nb(nb, p.nb=0.1, n.species=10, clus.specs=9, reg.group=1:17,
vector.species=c(10))

Construct communities from individuals

Description

Construct communities from individuals using geographical distance and hierarchical clustering. Communities are clusters of geographically close individuals, formed by hclust with specified distance cutoff.

Usage

communities(geodist,grouping=NULL,
                        cutoff=1e-5,method="single")

Arguments

Value

Vector of community memberships for the individuals (integer numbers from 1 to the number of communities without interruption.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

See Also

communitydist

Examples

  data(veronica)
  ver.geo <- coord2dist(coordmatrix=veronica.coord[1:90,],file.format="decimal2")
  species <-c(rep(1,64),rep(2,17),rep(3,9))
  communities(ver.geo,species)
  

Distances between communities

Description

Constructs distances between communities: chord- (Cavalli-Sforza and Edwards, 1967), phiPT/phiST (Peakall and Smouse, 2012, Meirmans, 2006), three versions of the shared allele distance between communities, and geographical distance between communities.

Usage

communitydist(alleleobj,comvector="auto",distance="chord",
                          compute.geodist=TRUE,out.dist=FALSE,
                          grouping=NULL,geodist=NA,diploid=TRUE,
                          phiptna=NA,...)

Arguments

Details

All genetic distances between communities are based on the information given in alleleobj; either on the alleles directly or on a genetic distance (distmat-component, see alleleinit). The possible genetic distance measures between communities are as follows:

• "chord": chord-distance (Cavalli-Sforza and Edwards, 1967)

• "phipt": phiPT-distance implemented according to Peakall and Smouse, 2012. This also appears in the literature under the name phiST (Meirmans, 2006, although the definition there is incomplete and we are not sure whether this is identical).

• "shared.average": average of between-community genetic distances.

• "shared.chakraborty": between-community shared allele distance according to Chakraborty and Jin (1993).

• "shared.problist": this implements the shared allele distance (Bowcock et al., 1994) for individuals directly for communities (one minus proportion of alleles shared by two communities averaged over loci).

Value

list with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Bowcock, A. M., Ruiz-Linares, A., Tomfohrde, J., Minch, E., Kidd, J. R., Cavalli-Sforza, L. L. (1994) High resolution of human evolutionary trees with polymorphic microsatellites. Nature 368, 455-457.

Cavalli-Sforza, L. L. and Edwards, A. W. F. (1967) Phylogenetic Analysis - Models and Estimation Procedures. The American Journal of Human Genetics 19, 233-257.

Chakraborty, R. and Jin, L. (1993) Determination of relatedness between individuals using DNA fingerprinting. Human Biology 65, 875-895.

Meirmans, P. G. (2006) Using the AMOVA framework to estimate a standardized genetic differentiation measure. Evolution 60, 2399-2402.

Peakall, R. and Smouse P.E. (2012) GenAlEx Tutorial 2. https://biology-assets.anu.edu.au/GenAlEx/Tutorials.html

See Also

communities; refer to phipt for computation of distances between specific pairs of communities. diploidcomlist produces relative frequencies for all alles of all loci in all communities (on which the chord- and the "shared.problist"-distances are based).

Examples

  options(digits=4)
  data(tetragonula)
  tnb <-
  coord2dist(coordmatrix=tetragonula.coord[83:120,],cut=50,
    file.format="decimal2",neighbors=TRUE)
  ta <- alleleconvert(strmatrix=tetragonula[83:120,])
  tai <- alleleinit(allelematrix=ta,neighborhood=tnb$nblist)
  tetraspec <- c(rep(1,11),rep(2,13),rep(3,14))
  tetracoms <-
  c(rep(1:3,each=3),4,5,rep(6:11,each=2),12,rep(13:19,each=2))
  c1 <- communitydist(tai,comvector=tetracoms,distance="chord",
    geodist=tnb$distmatrix,grouping=tetraspec)
  c2 <- communitydist(tai,comvector=tetracoms,distance="phipt",
    geodist=tnb$distmatrix,grouping=tetraspec,compute.geodist=FALSE)
  c3 <- communitydist(tai,comvector=tetracoms,distance="shared.average",
    geodist=tnb$distmatrix,grouping=tetraspec,compute.geodist=FALSE)
  c4 <- communitydist(tai,comvector=tetracoms,distance="shared.chakraborty",
    geodist=tnb$distmatrix,grouping=tetraspec,compute.geodist=FALSE)
  c5 <- communitydist(tai,comvector=tetracoms,distance="shared.problist",
    geodist=tnb$distmatrix,grouping=tetraspec,compute.geodist=FALSE)
  round(c1$cgeodist,digits=1)
  c1$comvector
  c2$comvector
  c3$comvector
  c4$comvector
  c5$comvector
  round(c1$dist,digits=2)
  round(c2$dist,digits=2)
  round(c3$dist,digits=2)
  round(c4$dist,digits=2)
  round(c5$dist,digits=2)  

Compare species clustering and species groups

Description

Tests for independence between a clustering and another grouping of species. This is simply an interface to chisq.test.

Usage

comp.test(cl,spg)

Arguments

Details

chisq.test with simulated p-value is used.

Value

Output of chisq.test.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Hausdorf, B. and Hennig, C. (2003) Biotic Element Analysis in Biogeography. Systematic Biology 52, 717-723.

See Also

chisq.test, prabclust.

Examples

set.seed(1234)
g1 <- c(rep(1,34),rep(2,12),rep(3,15))
g2 <- sample(3,61,replace=TRUE)
comp.test(g1,g2)

Connectivity components of an undirected graph

Description

Computes the connectivity components of an undirected graph from a matrix giving the edges.

Usage

con.comp(comat)

Arguments

Details

The "depth-first search" algorithm of Cormen, Leiserson and Rivest (1990, p. 477) is used.

Value

An integer vector, giving the number of the connectivity component for each vertice.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Cormen, T. H., Leiserson, C. E. and Rivest, R. L. (1990), Introduction to Algorithms, Cambridge: MIT Press.

See Also

hclust, cutree for cutted single linkage trees (often equivalent).

Examples

  set.seed(1000)
  x <- rnorm(20)
  m <- matrix(0,nrow=20,ncol=20)
  for(i in 1:20)
    for(j in 1:20)
      m[i,j] <- abs(x[i]-x[j])
  d <- m<0.2
  cc <- con.comp(d)
  max(cc) # number of connectivity components
  plot(x,cc)
  # The same should be produced by
  # cutree(hclust(as.dist(m),method="single"),h=0.2).

Connected regions per species

Description

Returns a vector of the numbers of connected regions per species for a presence-absence matrix.

Usage

con.regmat(regmat, neighbors, count = FALSE)

Arguments

Details

Uses con.comp.

Value

Vector of numbers of connected regions per species.

Note

Designed for use in prabtest.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

See Also

con.comp, prabtest

Examples

data(nb)
set.seed(888) 
cp <- cluspop.nb(nb, p.nb=0.1, n.species=10, clus.specs=9,
                 reg.group=1:17,vector.species=c(10))
con.regmat(cp,nb)

Geographical coordinates to distances

Description

Computes geographical distances from geographical coordinates

Usage

  coord2dist(file=NULL, coordmatrix=NULL, cut=NULL,
                       file.format="degminsec",
                       output.dist=FALSE, radius=6378.137,
              fp=1/298.257223563, neighbors=FALSE)

Arguments

Value

If neighbors==TRUE, a list with components

If neighbors==FALSE, only the distance matrix.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

German Wikipedia from 29 August 2010: https://de.wikipedia.org/wiki/Orthodrome

See Also

geo2neighbor

Examples

  options(digits=4)
  data(veronica)
  coord2dist(coordmatrix=veronica.coord[1:20,], cut=20, file.format="decimal2",neighbors=TRUE)

Region-wise cluster membership

Description

Produces a matrix with clusters as rows and regions as columns, indicating how many species present in a region belong to the clusters

Usage

crmatrix(x,xc,percentages=FALSE)

Arguments

Value

A clusters time regions matrix as explained above.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

Examples

  options(digits=3)
  data(kykladspecreg)
  data(nb)
  set.seed(1234)
  x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb)
  xc <- prabclust(x)

  crmatrix(x,xc)
  crmatrix(x,xc, percentages=TRUE)

Dice distance matrix

Description

Computes a distance derived from Dice's coincidence index between the columns of a 0-1-matrix.

Usage

  dicedist(regmat)

Arguments

Details

The Dice distance between two species is 1 minus the Coincidence Index, which is (2*number of regions where both species are present)/(2*number of regions where both species are present plus number of regions where at least one species is present). This is S23 in Shi (1993).

Value

A symmetrical matrix of Dice distances.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Shi, G. R. (1993) Multivariate data analysis in palaeoecology and palaeobiogeography - a review. Palaeogeography, Palaeoclimatology, Palaeoecology 105, 199-234.

See Also

kulczynski,jaccard

Examples

options(digits=4)
data(kykladspecreg)
dicedist(t(kykladspecreg))

Distance ratio test statistics for distance based clustering

Description

Calculates the ratio between the prop smallest and largest distances of a distance matrix.

Usage

distratio(distmat, prop = 0.25)

Arguments

Details

Rounding is by floor for small and ceiling for large distances.

Value

A list with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

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.

See Also

prabtest

Examples

options(digits=4)
data(kykladspecreg)
j <- jaccard(t(kykladspecreg))
distratio(j)

geco distance matrix

Description

Computes geco distances between the columns of a 0-1-matrix, based on a distance matrix between regions (usually, but not necessarily, this is a geographical distance).

Usage

geco(regmat,geodist=as.dist(matrix(as.integer(!diag(nrow(regmat))))),
                   transform="piece",
                   tf=0.1,
                   countmode=ncol(regmat)+1)

Arguments

Details

The geco distance between two species is 0.5*(mean distance between region where species 1 is present and closest region where species 2 is present plus mean distance between region where species 2 is present and closest region where species 1 is present). 'closest' to a region could be the regions itself. It is recommended (Hennig and Hausdorf, 2006) to transform the distances first, because the differences between large distances are usually not meaningful or at least much less meaningful than differences between small distances for dissimilarity measurement between species ranges. See parameter transform.

If the between-regions distance is 1 for all pairs of non-equal regions, the geco distance degenerates to the Kulczynski distance, see kulczynski.

Value

A symmetrical matrix of geco distances.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Hennig, C. and Hausdorf, B. (2006) A robust distance coefficient between distribution areas incorporating geographic distances. Systematic Biology 55, 170-175.

See Also

kulczynski

Examples

options(digits=4)
data(kykladspecreg)
data(waterdist)
geco(t(kykladspecreg),waterdist)

Neighborhood list from geographical distance

Description

Generates a neighborhood list as required by prabinit from a matrix of geographical distances.

Usage

geo2neighbor(geodist,cut=0.1*max(geodist))

Arguments

Value

A list of integer vectors, giving the set of neighbors for every region.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

Examples

data(waterdist)
geo2neighbor(waterdist)

Classical distance-based test for homogeneity against clustering

Description

Classical distance-based test for homogeneity against clustering. Test statistics is number of isolated vertices in the graph of smallest distances. The homogeneity model is a random graph model where ne edges are drawn from all possible edges.

Usage

homogen.test(distmat, ne = ncol(distmat), testdist = "erdos")

Arguments

Details

The "ling"-test is one-sided (rejection if the number of isolated vertices is too large), the "erdos"-test computes a one-sided as well as a two-sided p-value.

Value

A list with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

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.

Godehardt, E. and Horsch, A. (1995) Graph-Theoretic Models for Testing the Homogeneity of Data. In Gaul, W. and Pfeifer, D. (Eds.) From Data to Knowledge, Springer, Berlin, 167-176.

Ling, R. F. (1973) A probability theory of cluster analysis. Journal of the American Statistical Association 68, 159-164.

See Also

prabtest

Examples

options(digits=4)
data(kykladspecreg)
j <- jaccard(t(kykladspecreg))
homogen.test(j, testdist="erdos")
homogen.test(j, testdist="ling")

Clustering of species ranges from presence-absence matrices (hierarchical methods)

Description

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. Often, the prabclust-solutions is more convincing due to higher flexibility of that method. However, hprabclust may be more stable sometimes.

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

hprabclust(prabobj, cutdist=0.4, cutout=1,
method="average", nnout=2, mdsplot=TRUE, mdsmethod="classical")

## S3 method for class 'comprabclust'
print(x, ...)

Arguments

Value

hprabclust generates an object of class comprabclust. This is a list with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

See Also

hclust, cutree, prabclust.

Examples

data(kykladspecreg)
data(nb)
data(waterdist)
x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb,
              geodist=waterdist, distance="geco")
hprabclust(x,mdsplot=FALSE)

Nestedness matrix

Description

Computes species*species nestedness matrix and number of nestings (inclusions) from regions*species presence-absence matrix.

Usage

incmatrix(regmat)

Arguments

Value

A list with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Hausdorf, B. and Hennig, C. (2003) Nestedness of nerth-west European land snail ranges as a consequence of differential immigration from Pleistocene glacial refuges. Oecologia 135, 102-109.

See Also

prabtest

Examples

data(kykladspecreg)
incmatrix(t(kykladspecreg))$ninc

Jaccard distance matrix

Description

Computes Jaccard distances between the columns of a 0-1-matrix.

Usage

jaccard(regmat)

Arguments

Details

The Jaccard distance between two species is 1-(number of regions where both species are present)/(number of regions where at least one species is present). As a similarity coefficient, this is S22 in Shi (1993).

Thank you to Laurent Buffat for improving this function!

Value

A symmetrical matrix of Jaccard distances.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Shi, G. R. (1993) Multivariate data analysis in palaeoecology and palaeobiogeography - a review. Palaeogeography, Palaeoclimatology, Palaeoecology 105, 199-234.

See Also

kulczynski, dicedist

Examples

options(digits=4)
data(kykladspecreg)
jaccard(t(kykladspecreg))

Kulczynski distance matrix

Description

Computes Kulczynski distances between the columns of a 0-1-matrix.

Usage

kulczynski(regmat)

Arguments

Details

The Kulczynski distance between two species is 1-(mean of (number of regions where both species are present)/(number of regions where species 1 is present) and (number of regions where both species are present)/(number of regions where species 2 is present)). The similarity version of this is S28 in Shi (1993).

Value

A symmetrical matrix of Kulczynski distances.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Shi, G. R. (1993) Multivariate data analysis in palaeoecology and palaeobiogeography - a review. Palaeogeography, Palaeoclimatology, Palaeoecology 105, 199-234.

See Also

jaccard, geco,qkulczynski , dicedist

Examples

options(digits=4)
data(kykladspecreg)
kulczynski(t(kykladspecreg))

Snail presence-absence data from Aegean sea

Description

0-1-matrix where rows are snail species and columns are islands in the Aegean sea. An entry of 1 means that the species is present in the region.

Usage

data(kykladspecreg)

Format

A 0-1 matrix with 80 rows and 34 columns.

Details

Reads from example data file kykladspecreg.dat.

Source

B. Hausdorf and C. Hennig (2005) The influence of recent geography, palaeography and climate on the composition of the faune of the central Aegean Islands. Biological Journal of the Linnean Society 84, 785-795.

See Also

nb provides neighborhood information about the 34 islands. waterdist provides a geographical distance matrix between the islands.

Examples

data(kykladspecreg)

Largest connectivity component

Description

Computes the size of the largest connectivity component of the graph of ncol(distmat) vertices with edges defined by the smallest ne distances.

Usage

lcomponent(distmat, ne = floor(3*ncol(distmat)/4))

Arguments

Value

list with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

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.

See Also

prabtest

Examples

data(kykladspecreg)
j <- jaccard(t(kykladspecreg))
lcomponent(j)

Visualises clusters of markers vs. species

Description

Given a clustering of individuals from prabclust (as generated in species delimitation) and a clustering of markers (for example dominant markers of genetic loci), lociplots visualises the presence of markers against the clustering of individuals and computes some statistics.

Usage

  lociplots(indclust,locclust,locprab,lcluster,
                      symbols=NULL,brightest.grey=0.8,darkest.grey=0,
                      mdsdim=1:2)

Arguments

Details

Plot and statistics are based on the individual marker percentage, which is the percentage of markers present in an individual of the markers belonging to cluster no. lcluster. In the plot, the grey value visualises the marker percentage.

Value

list with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

See Also

prabclust

Examples

  options(digits=4)
  data(veronica)
  vei <- prabinit(prabmatrix=veronica[1:50,],distance="jaccard")
  ppv <- prabclust(vei)
  veloci <- prabinit(prabmatrix=veronica[1:50,],rows.are.species=FALSE)
  velociclust <- prabclust(veloci,nnk=0)
  lociplots(ppv,velociclust$clustering,veloci,lcluster=3)

Missing values statistics for matrix

Description

Computes column-wise and row-wise numbers of missing values.

Usage

  nastats(amatrix, nastr="--")

Arguments

Value

A list with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

Examples

  xx <- cbind(c(1,2,3),c(0,0,1),c(5,3,1))
  nastats(xx,nastr=0)

Neighborhood list for Aegean islands

Description

List of neighboring islands for 34 Aegean islands.

Usage

data(nb)

Format

List with 34 components, all being vetors of integers (or numeric(0) in case of no neighbors) indicating the neighboring islands.

Details

Reads from example data file nb.dat.

Source

B. Hausdorf and C. Hennig (2005) The influence of recent geography, palaeography and climate on the composition of the faune of the central Aegean Islands. Biological Journal of the Linnean Society 84, 785-795.

Examples

data(nb)
# nb <- list()
# for (i in 1:34)
#   nb <- c(nb,list(scan(file="(path/)nb.dat",
#                   skip=i-1,nlines=1)))

Test of neighborhood list

Description

Tests a list of neighboring regions for proper format. Neighborhood is tested for being symmetrical. Causes an error if tests fail.

Usage

nbtest(nblist, n.regions=length(nblist))

Arguments

Value

invisible{TRUE}.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

See Also

prabinit.

Examples

data(nb)
nbtest(nb)
nb[[1]][1] <- 1
try(nbtest(nb))

Mean distance to kth nearest neighbor

Description

Computes the mean of the distances from each point to its neth nearest neighbor.

Usage

nn(distmat, ne = 1)

Arguments

Value

numerical.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

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.

See Also

prabtest

Examples

data(kykladspecreg)
j <- jaccard(t(kykladspecreg))
nn(j,4)

Nearest neighbor based clutter/noise detection

Description

Detects if data points are noise or part of a cluster, based on a Poisson process model.

Usage

NNclean(data, k, distances = NULL, edge.correct = FALSE, wrap = 0.1,
convergence = 0.001, plot=FALSE, quiet=TRUE)

## S3 method for class 'nnclean'
print(x, ...)

Arguments

Details

The assumption is that the noise is distributed as a homogeneous Poisson process on a certain region and the clusters are distributed as a homogeneous Poisson process with larger intensity on a subregion (disconnected in case of more than one cluster). The distances are then distributed according to a mixture of two transformed Gamma distributions, and this mixture is estimated via the EM-algorithm. The points are assigned to noise or cluster component by use of the estimated a posteriori probabilities.

Value

NNclean returns a list of class nnclean with components

Note

The software can be freely used for non-commercial purposes, and can be freely distributed for non-commercial purposes only.

Author(s)

R-port by Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en,
original Splus package by S. Byers and A. E. Raftery.

References

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.

Examples

library(mclust)
data(chevron)
nnc <-  NNclean(chevron[,2:3],15,plot=TRUE)
plot(chevron[,2:3],col=1+nnc$z)

Distances between communities, auxiliary functions

Description

Auxiliary functions for communitydist. phipt computes phiPT/phiST (Peakall and Smouse, 2012, Meirmans, 2006) between two communities. cfchord computes the chord-distance (Cavalli-Sforza and Edwards, 1967) between two lists or locus-wise relative allele frequencies. shared.problist computes a straightforward generalisation of the shared allele distance (Bowcock et al., 1994) between individuals for communities, namely the ‘overlap’, i.e., sum of the minima of the allele relative frequencies. diploidcomlist constructs the input lists for cfchord and shared.problist from an alleleobject. It provides relative frequencies for all alles of all loci in all communities.

Usage

phipt(alleleobj,comvector,i,j)
cfchord(p1,p2)
shared.problist(p1,p2)
diploidcomlist(alleleobj,comvector,diploid=TRUE)

Arguments

Value

cfchord gives out the value of the chord distance. shared.problist gives out the distance value. diploidcomlist gives out a two-dimensional list. The list has one entry for each community, which is itself a list. This community list has one entry for each locus, which is a vector that gives the relative frequencies of the different alleles in phipt gives out a list with components phipt, vap, n0, sst, ssg, msa, msw. These refer to the notation on p.2.12 and 2.15 of Peakall and Smouse (2012).

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Bowcock, A. M., Ruiz-Linares, A., Tomfohrde, J., Minch, E., Kidd, J. R., Cavalli-Sforza, L. L. (1994) High resolution of human evolutionary trees with polymorphic microsatellites. Nature 368, 455-457.

Cavalli-Sforza, L. L. and Edwards, A. W. F. (1967) Phylogenetic Analysis - Models and Estimation Procedures. The American Journal of Human Genetics 19, 233-257.

Meirmans, P. G. (2006) Using the AMOVA framework to estimate a standardized genetic differentiation measure. Evolution 60, 2399-2402.

Peakall, R. and Smouse P.E. (2012) GenAlEx Tutorial 2. https://biology-assets.anu.edu.au/GenAlEx/Tutorials.html

See Also

communitydist

Examples

  options(digits=4)
  data(tetragonula)
  tnb <-
  coord2dist(coordmatrix=tetragonula.coord[83:120,],cut=50,file.format="decimal2",neighbors=TRUE)
  ta <- alleleconvert(strmatrix=tetragonula[83:120,])
  tai <- alleleinit(allelematrix=ta,neighborhood=tnb$nblist)
  tetracoms <-
  c(rep(1:3,each=3),4,5,rep(6:11,each=2),12,rep(13:19,each=2))
  phipt(tai,tetracoms,4,6)
  tdip <- diploidcomlist(tai,tetracoms,diploid=TRUE)
  cfchord(tdip[[4]],tdip[[6]])
  shared.problist(tdip[[4]],tdip[[6]])
  

Piecewise linear transformation for distance matrices

Description

Piecewise linear transformation for distance matrices, utility function for geco.

Usage

piecewiselin(distmatrix, maxdist=0.1*max(distmatrix))

Arguments

Details

Transforms large distances to 1, 0 to 0 and continuously linear between 0 and maxdist.

Value

A symmetrical matrix.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

See Also

geco

Examples

options(digits=4)
data(waterdist)
piecewiselin(waterdist)

Plots for within-groups and between-groups distance regression

Description

Visualisation of various regressions on distance (or dissimilarity) data where objects are from two groups.

Usage

plotdistreg(dmx,dmy,grouping,groups=levels(as.factor(grouping))[1:2],
                        cols=c(1,2,3,4),
                        pchs=rep(1,3),
                        ltys=c(1,2,1,2),
                        individual=TRUE,jointwithin=TRUE,jointall=TRUE,
                        oneplusjoint=TRUE,jittering=TRUE,bcenterline=TRUE,
                        xlim=NULL,ylim=NULL,xlab="geographical distance",
                        ylab="genetic distance",...)

Arguments

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Hausdorf, B. and Hennig, C. (2019) Species delimitation and geography. Submitted.

See Also

regeqdist, regdistbetween, regdistbetweenone, regdistdiffone

Examples

  options(digits=4)
  data(veronica)
  ver.geo <- coord2dist(coordmatrix=veronica.coord[173:207,],file.format="decimal2")
  vei <- prabinit(prabmatrix=veronica[173:207,],distance="jaccard")

  species <-c(rep(1,13),rep(2,22))
  loggeo <- log(ver.geo+quantile(as.vector(as.dist(ver.geo)),0.25))
  plotdistreg(dmx=loggeo,dmy=vei$distmat,grouping=species,
  jointwithin=FALSE,jointall=FALSE,groups=c(1,2))
  legend(5,0.75,c("within species 1",
  "within species 2","species 1 and between","species 2 and between"),lty=c(1,1,2,2),col=c(1,2,1,2))
  plotdistreg(dmx=loggeo,dmy=vei$distmat,grouping=species,
  jointwithin=TRUE,jointall=TRUE,oneplusjoint=FALSE,groups=c(1,2))
  legend(5,0.75,c("within species 1",
  "within species 2","all distances","all within species"),lty=c(1,1,1,2),col=c(1,2,3,3))
  

p-value simulation for presence-absence matrices clustering test

Description

Parametric bootstrap simulation of the p-value of a test of a homogeneity hypothesis against clustering (or significant nestedness). Designed for use within prabtest. The null model is defined by randpop.nb.

Usage

pop.sim(regmat, neighbors, h0c = 1, times = 200, dist = "kulczynski",
teststat = "isovertice", testc = NULL, geodist=NULL, gtf=0.1,
n.species = ncol(regmat),
specperreg = NULL, regperspec = NULL, species.fixed=FALSE, pdfnb=FALSE,
ignore.richness=FALSE)

Arguments

Value

List with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

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.

Hausdorf, B. and Hennig, C. (2003) Biotic Element Analysis in Biogeography. Systematic Biology 52, 717-723.

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.

See Also

prabtest, randpop.nb, jaccard, kulczynski, homogen.test, lcomponent, distratio, nn, incmatrix.

Examples

options(digits=4)
data(kykladspecreg)
data(nb)
set.seed(1234)
pop.sim(t(kykladspecreg), nb, times=5, h0c=0.35, teststat="nn", testc=3)

Estimates SAR model from log-abundance matrix of prab-object.

Description

This is either an interface for the function errorsarlm for abundance data stored in an object of class prab implemented for use in abundtest, or, in case that spatial information should be ignored, it estimates a two-way additive unreplicated linear model for log-abundances on factors species and region.

Usage

prab.sarestimate(abmat, prab01=NULL,sarmethod="eigen",
                             weightstyle="C",
                             quiet=TRUE, sar=TRUE,
                             add.lmobject=TRUE)

Arguments

Value

A list with the following components:

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

See Also

errorsarlm, abundtest

Examples

  options(digits=4)
  data(siskiyou)
  x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb,
             distance="none")
#  Not run; this needs package spdep 
#  prab.sarestimate(x)
  prab.sarestimate(x, sar=FALSE)

Clustering for biotic elements or for species delimitation (mixture method)

Description

Clusters a presence-absence matrix object (for clustering ranges/finding biotic elements, Hennig and Hausdorf, 2004) or an object of genetic information (for species delimitation, Hausdorf and Hennig, 2010) 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 (using the default distance of prabinit) will be
prabmatrix <- prabinit(file="path/prabmatrixfile", neighborhood="path/neighborhoodfile")
clust <- prabclust(prabmatrix)
print(clust)
Examples for species delimitation are given below in the examples section. Note: Data formats are described on the prabinit and alleleinit help pages. You may also consider the example datasets kykladspecreg.dat, nb.dat, Heterotrigona_indoFO.txt or MartinezOrtega04AFLP.dat.
Note: prabclust calls the function mclustBIC in package mclust. An alternative is the use of hprabclust.

Usage

prabclust(prabobj, mdsmethod = "classical", mdsdim = 4, nnk =
ceiling(prabobj$n.species/40), nclus = 0:9, modelid = "all", permutations=0)

## S3 method for class 'prabclust'
print(x, bic=FALSE, ...)

Arguments

Details

Note that if mdsmethod!="classical", zero distances between non-identical objects are replaced by the smallest nonzero distance divided by 10 to prevent the MDS methods from producing an error.

Value

print.prabclust does not produce output. prabclust generates an object of class prabclust. This is a list with components

Note

Note that we used mdsmethod="kruskal" in our publications, but mdsmethod="classical" is now the default, because of occasional numerical instabilities of the isoMDS-implementation for Jaccard, Kulczynski or geco distance matrices.

Sometimes, prabclust produces an error because mclustBIC cannot handle all models properly. In this case we recommend to change the modelid parameter. "noVVV" and "VVV" are reasonable alternative choices (one of these is expected to reproduce the error, but the other one might work).

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Fraley, C. and Raftery, A. E. (1998) How many clusters? Which clustering method? - Answers via Model-Based Cluster Analysis. Computer Journal 41, 578-588.

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. http://stat.ethz.ch/Research-Reports/110.html.

See Also

mclustBIC, summary.mclustBIC, NNclean, cmdscale, isoMDS, sammon, prabinit, hprabclust, alleleinit, stressvals.

Examples

# Biotic element/range clustering:
data(kykladspecreg)
data(nb)
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))

# Here is an example for species delimitation with codominant markers;
# only 50 individuals were used in order to have a fast example. 
data(tetragonula)
ta <- alleleconvert(strmatrix=tetragonula[1:50,])
tai <- alleleinit(allelematrix=ta)
print(prabclust(tai))

# Here is an example for species delimitation with dominant markers;
# only 50 individuals were used in order to have a fast example.
# You may want to use stressvals to choose mdsdim.
data(veronica)
vei <- prabinit(prabmatrix=veronica[1:50,],distance="jaccard")
print(prabclust(vei,mdsmethod="kruskal",mdsdim=3))

Presence-absence/abundance matrix initialization

Description

prabinit converts a matrix into an object of class prab (presence-absence). The matrix may be read from a file or an R-object. It may be a 0-1 matrix or a matrix with non-negative entries (usually abundances). print.prab is a print method for such objects.

Documentation here is in terms of biotic elements analysis (species are to be clustered). For species delimitation with dominant markers, see Hausdorf and Hennig (2010), individuals take the role of species and loci take the role of regions.

Usage

prabinit(file = NULL, prabmatrix = NULL, rows.are.species = TRUE,
neighborhood = "none", nbbetweenregions=TRUE, geodist=NULL, gtf=0.1,
distance = "kulczynski", toprab = FALSE, toprabp
= 0.05, outc = 5.2)

## S3 method for class 'prab'
print(x, ...)

Arguments

Details

Species that are absent in all regions are omitted.

Value

prabinit produces an object of class prab, which is a list with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Hausdorf, B. and Hennig, C. (2010) Species Delimitation Using Dominant and Codominant Multilocus Markers. Systematic Biology, 59, 491-503.

See Also

read.table, jaccard, kulczynski, geco, qkulczynski, nbtest, alleleinit

Examples

# If you want to use your own ASCII data files, use
# x <- prabinit(file="path/prabmatrixfile",
# neighborhood="path/neighborhoodfile")
data(kykladspecreg)
data(nb)
prabinit(prabmatrix=kykladspecreg, neighborhood=nb)

Parametric bootstrap test for clustering in presence-absence matrices

Description

Parametric bootstrap test of a null model of i.i.d., but spatially autocorrelated species against clustering of the species' occupied areas (or alternatively nestedness). In spite of the lots of parameters, a standard execution (for the default test statistics, see parameter teststat below) will be
prabmatrix <- prabinit(file="path/prabmatrixfile", neighborhood="path/neighborhoodfile")
test <- prabtest(prabmatrix)
summary(test)
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

prabtest(prabobject, teststat = "distratio", tuning = switch(teststat, 
    distratio = 0.25, lcomponent = floor(3 * ncol(prabobject$distmat)/4), 
    isovertice = ncol(prabobject$distmat), nn = 4, NA), times = 1000, 
    pd = NULL, prange = c(0, 1), nperp = 4, step = 0.1, step2=0.01,
                      twostep = TRUE, 
    sf.sim = FALSE, sf.const = sf.sim, pdfnb = FALSE, ignore.richness=FALSE) 


## S3 method for class 'prabtest'
summary(object, above.p=object$teststat %in%
         c("groups","inclusions","mean"),
         group.outmean=FALSE,...)

## S3 method for class 'summary.prabtest'
print(x, ...)

Arguments

Details

From the original data, the distribution of the range sizes of the species, the autocorrelation parameter pd (estimated by autoconst) and the distribution on the regions induced by the relative species numbers are taken. With these parameters, times populations according to the null model implemented in randpop.nb are generated and the test statistic is evaluated. The resulting p-value is number of simulated statistic values more extreme than than the value of the original data+1 divided by times+1. "More extreme" means smaller for "lcomponent", "distratio", "nn", larger for "inclusions", and twice the smaller number between the original statistic value and the "border", i.e., a two-sided test for "isovertice". If pd=NA was specified, a diagnostic plot for the estimation of pd is plotted by autoconst. For details see Hennig and Hausdorf (2004) and the help pages of the cited functions.

Value

prabtest prodices an object of class prabtest, which is a list with components

summary.prabtest produces an object of class summary.prabtest, which is a list with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

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.

Hausdorf, B. and Hennig, C. (2003) Biotic Element Analysis in Biogeography. Systematic Biology 52, 717-723.

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.

See Also

prabinit generates objects of class prab.

autoconst estimates pd from such objects.

randpop.nb generates populations from the null model. An alternative model is given by cluspop.nb.

Some more information on the test statistics is given in homogen.test, lcomponent, distratio, nn, incmatrix.

The simulations are computed by pop.sim.

Examples

options(digits=4)
data(kykladspecreg)
data(nb)
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")
kpt <- prabtest(x, times=5, pd=0.35)
# These settings are chosen to make the example execution
# a bit faster; usually you will use prabtest(kprab).
summary(kpt)

Quantitative Kulczynski distance matrix

Description

Computes quantitative Kulczynski distances between the columns of an abundance matrix.

Usage

qkulczynski(regmat, log.distance=FALSE)

Arguments

Details

The quantitative Kulczynski distance between two species is 1-(mean of (mean of over regions minimum abundance of both species)/(sum of abundances of species 1) and (mean of over regions minimum abundance of both species)/(sum of abundances of species 2)). If the abundance matrix is a 0-1-matrix, this gives the standard Kulczynski distance.

Value

A symmetrical matrix of quantitative Kulczynski distances.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

D. P. Faith, P. R. Minchin and L. Belbin (1987) Compositional dissimilarity as a robust measure of ecological distance. Vegetation 69, 57-68.

See Also

kulczynski

Examples

options(digits=4)
data(kykladspecreg)
qkulczynski(t(kykladspecreg))

Simulation of presence-absence matrices (non-clustered)

Description

Generates a simulated matrix where the rows are interpreted as regions and the columns as species, 1 means that a species is present in the region and 0 means that the species is absent. Species are generated i.i.d.. Spatial autocorrelation of a species' presences is governed by the parameter p.nb and a list of neighbors for each region.

Usage

randpop.nb(neighbors, p.nb = 0.5, n.species, n.regions =
length(neighbors), vector.species = rep(1, n.species),
species.fixed = FALSE, pdf.regions = rep(1/n.regions, n.regions),
count = TRUE, pdfnb = FALSE)

Arguments

Details

The principle is that a single species with given size is generated one-by-one region. The first region is drawn according to pdf.regions. For all following regions, a neighbor or non-neighbor of the previous configuration is added (if possible), as explained in pdf.regions, p.nb.

Value

A 0-1-matrix, rows are regions, columns are species.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

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.

Hausdorf, B. and Hennig, C. (2003) Biotic Element Analysis in Biogeography. Systematic Biology 52, 717-723.

Hausdorf, B. and Hennig, C. (2003) Nestedness of nerth-west European land snail ranges as a consequence of differential immigration from Pleistocene glacial refuges. Oecologia 135, 102-109.

See Also

autoconst estimates p.nb from matrices of class prab. These are generated by prabinit.

prabtest uses randpop.nb as a null model for tests of clustering. An alternative model is given by cluspop.nb.

Examples

data(nb)
set.seed(2346)
randpop.nb(nb, p.nb=0.1, n.species=5, vector.species=c(1,10,20,30,34))

Regression between subsets of dissimilarity matrices

Description

Given two dissimilarity matrices dmx and dmy and an indicator vector x, this computes a standard least squares regression between the dissimilarity between objects indicated in x.

Usage

regdist(x,dmx,dmy,xcenter=0,param)

Arguments

Value

If param=NULL, the output object of lm. If param=1 the intercept. If param=2 the slope.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Hausdorf, B. and Hennig, C. (2019) Species delimitation and geography. Submitted.

Examples

  options(digits=4)
  data(veronica)
  ver.geo <- coord2dist(coordmatrix=veronica.coord[1:20,],file.format="decimal2")
  vei <- prabinit(prabmatrix=veronica[1:20,],distance="jaccard")
  regdist(c(rep(TRUE,10),rep(FALSE,10)),ver.geo,vei$distmat,param=1)

Testing equality of within-groups and between-groups distances regression

Description

Jackknife-based test for equality of two regressions between distances. Given two groups of objects, this tests whether the regression involving all distances is compatible with the regression involving within-group distances only.

Usage

regdistbetween(dmx,dmy,grouping,groups=levels(as.factor(grouping))[1:2])

## S3 method for class 'regdistbetween'
print(x,...)

Arguments

Details

The null hypothesis that the regressions based on all distances and based on within-group distances only are equal is tested using jackknife pseudovalues. This assumes that a single regression is appropriate at least for the within-group distances alone. The test statistic is the difference between fitted values with x (explanatory variable) fixed at the center of the between-group distances. The test is run one-sided, i.e., the null hypothesis is only rejected if the between-group distances are larger than expected under the null hypothesis, see below.

The test cannot be run in case that within-group regressions or jackknifed within-group regressions are ill-conditioned.

This was implemented having in mind an application in which the explanatory distances represent geographical distances, the response distances are genetic distances, and groups represent species or species-candidates. In this application, for testing whether the regression patterns are compatble with the two groups behaving like a single species, one would first use regeqdist to test whether a joint regression for the within-group distances of both groups makes sense. If this is not rejected, regdistbetween is run to see whether the between-group distances are compatible with the within-group distances. This is only rejected if the between-group distances are larger than expected under equality of regressions, because if they are smaller, this is not an indication against the groups belonging together genetically.

If a joint regression on within-group distances is rejected by regeqdist, regdistbetweenone can be used to test whether the between-group distances are at least compatible with the within-group distances of one of the groups, which can still be the case within a single species, see Hausdorf and Hennig (2019).

Value

list of class "regdistbetween" with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Hausdorf, B. and Hennig, C. (2019) Species delimitation and geography. Submitted.

See Also

regeqdist, regdistbetweenone

Examples

  options(digits=4)
  data(veronica)
  ver.geo <- coord2dist(coordmatrix=veronica.coord[173:207,],file.format="decimal2")
  vei <- prabinit(prabmatrix=veronica[173:207,],distance="jaccard")
  loggeo <- log(ver.geo+quantile(as.vector(as.dist(ver.geo)),0.25))

  species <-c(rep(1,13),rep(2,22))
 
  rtest2 <-
  regdistbetween(dmx=loggeo,dmy=vei$distmat,grouping=species,groups=c(1,2))
  print(rtest2)

Testing equality of one within-group and between-two groups distances regression

Description

Jackknife-based test for equality of two regressions between distances. Given two groups of objects, this tests whether the regression involving the distances within one of the groups is compatible with the regression involving the same within-group distances together with the between group distances.

Usage

regdistbetweenone(dmx,dmy,grouping,groups=levels(as.factor(grouping))[1:2],rgroup)

Arguments

Details

The null hypothesis that the regressions based on the distances within group species and based on these distances together with the between-groups distances are equal is tested using jackknife pseudovalues. The test statistic is the difference between fitted values with x (explanatory variable) fixed at the center of the between-group distances. The test is run one-sided, i.e., the null hypothesis is only rejected if the between-group distances are larger than expected under the null hypothesis, see below. For the jackknife, observations from both groups are left out one at a time. However, the roles of the two groups are different (observations from group species are used in both regressions whereas observations from the other group are only used in one of them), and therefore the corresponding jackknife pseudovalues can have different variances. To take this into account, variances are pooled, and the degrees of freedom of the t-test are computed by the Welch-Sattertwaithe approximation for aggregation of different variances.

The test cannot be run and many components will be NA in case that within-group regressions or jackknifed within-group regressions are ill-conditioned.

This was implemented having in mind an application in which the explanatory distances represent geographical distances, the response distances are genetic distances, and groups represent species or species-candidates. In this application, for testing whether the regression patterns are compatble with the two groups behaving like a single species, one would first use regeqdist to test whether a joint regression for the within-group distances of both groups makes sense. If this is not rejected, regdistbetween is run to see whether the between-group distances are compatible with the within-group distances. If a joint regression on within-group distances is rejected by regeqdist, regdistbetweenone can be used to test whether the between-group distances are at least compatible with the within-group distances of one of the groups, which can still be the case within a single species, see Hausdorf and Hennig (2019). This is only rejected if the between-group distances are larger than expected under equality of regressions, because if they are smaller, this is not an indication against the groups belonging together genetically. To this end, regdistbetweenone needs to be run twice using both groups as species. This will produce two p-values. The null hypothesis that the regressions are compatible for at least one group can be rejected if the maximum of the two p-values is smaller than the chosen significance level.

Value

list of class "regdistbetween" with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Hausdorf, B. and Hennig, C. (2019) Species delimitation and geography. Submitted.

See Also

regeqdist, regdistbetweenone

Examples

  options(digits=4)
  data(veronica)
  ver.geo <- coord2dist(coordmatrix=veronica.coord[173:207,],file.format="decimal2")
  vei <- prabinit(prabmatrix=veronica[173:207,],distance="jaccard")

  species <-c(rep(1,13),rep(2,22))
  loggeo <- log(ver.geo+quantile(as.vector(as.dist(ver.geo)),0.25))
  rtest3 <-
  regdistbetweenone(dmx=loggeo,dmy=vei$distmat,grouping=species,groups=c(1,2),rgroup=1)
  print(rtest3)

Regression difference between within-group dissimilarities

Description

Given two dissimilarity matrices dmx and dmy, an indicator vector x and a grouping, this computes the difference between standard least squares regression predictions at point xcenterbetween. The regressions are based on the dissimilarities in dmx vs. dmy for objects indicated in x. grouping indicates the two groups, and the difference is computed between regressions based on the within-group distances of the two groups.

Usage

regdistdiff(x,dmx,dmy,grouping,xcenter=0,xcenterbetween=0)

Arguments

Value

Difference between standard least squares regression predictions for the two groups at point xcenterbetween.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Hausdorf, B. and Hennig, C. (2019) Species delimitation and geography. Submitted.

See Also

regdistbetween

Examples

  options(digits=4)
  data(veronica)
  ver.geo <- coord2dist(coordmatrix=veronica.coord[173:207,],file.format="decimal2")
  vei <- prabinit(prabmatrix=veronica[173:207,],distance="jaccard")

  species <-c(rep(1,13),rep(2,22))
  regdistdiff(rep(TRUE,35),ver.geo,vei$distmat,grouping=species,xcenter=0,xcenterbetween=100)

Regression difference within reference group and between-group dissimilarities

Description

Given two dissimilarity matrices dmx and dmy, an indicator vector x and a grouping, this computes the difference between standard least squares regression predictions at point xcenterbetween. The regressions are based on the dissimilarities in dmx vs. dmy for objects indicated in x. grouping indicates the two groups, and the difference is computed between regressions based on (a) the within-group distances of the reference group rgroup and (b) these together with the between-group distances.

Usage

regdistdiffone(x,dmx,dmy,grouping,xcenter=0,xcenterbetween=0,rgroup)

Arguments

Value

Difference between standard least squares regression predictions for the two regressions at point xcenterbetween.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Hausdorf, B. and Hennig, C. (2019) Species delimitation and geography. Submitted.

See Also

regdistbetweenone

Examples

  options(digits=4)
  data(veronica)
  ver.geo <- coord2dist(coordmatrix=veronica.coord[173:207,],
    file.format="decimal2")
  vei <- prabinit(prabmatrix=veronica[173:207,],distance="jaccard")

  species <-c(rep(1,13),rep(2,22))
  regdistdiffone(rep(TRUE,35),ver.geo,vei$distmat,grouping=species,
    xcenter=0,xcenterbetween=100,rgroup=2)

Testing equality of two distance-regressions

Description

Jackknife-based test for equality of two regressions between distance matrices.

Usage

regeqdist(dmx,dmy,grouping,groups=levels(as.factor(grouping))[1:2])

## S3 method for class 'regeqdist'
print(x,...)

Arguments

Details

The null hypothesis that the regressions within the two groups are equal is tested using jackknife pseudovalues independently in both groups allowing for potentially different variances of the pseudovalues, and aggregating as in Welch's t-test. Tests are run separately for intercept and slope and aggregated by Bonferroni's rule.

The test cannot be run and many components will be NA in case that within-group regressions or jackknifed within-group regressions are ill-conditioned.

This was implemented having in mind an application in which the explanatory distances represent geographical distances, the response distances are genetic distances, and groups represent species or species-candidates. In this application, for testing whether the regression patterns are compatble with the two groups behaving like a single species, one would first use regeqdist to test whether a joint regression for the within-group distances of both groups makes sense. If this is not rejected, regdistbetween is run to see whether the between-group distances are compatible with the within-group distances. On the other hand, if a joint regression on within-group distances is rejected, regdistbetweenone can be used to test whether the between-group distances are at least compatible with the within-group distances of one of the groups, which can still be the case within a single species, see Hausdorf and Hennig (2019).

Value

list of class "regeqdist" with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

Hausdorf, B. and Hennig, C. (2019) Species delimitation and geography. Submitted.

See Also

regdistbetween, regdistbetweenone

Examples

  options(digits=4)
  data(veronica)
  ver.geo <- coord2dist(coordmatrix=veronica.coord[173:207,],file.format="decimal2")
  vei <- prabinit(prabmatrix=veronica[173:207,],distance="jaccard")
  loggeo <- log(ver.geo+quantile(as.vector(as.dist(ver.geo)),0.25))

  species <-c(rep(1,13),rep(2,22))
  rtest <- regeqdist(dmx=loggeo,dmy=vei$distmat,grouping=species,groups=c(1,2))
  print(rtest)

Simulation of abundance matrices (non-clustered)

Description

Generates a simulated matrix where the rows are interpreted as regions and the columns as species, and the entries are abundances. Species are generated i.i.d. in two steps. In the first step, a presence-absence matrix is generated as in randpop.nb. In the second step, conditionally on presence in the first step, abundance values are generated according to a simultaneous autoregression (SAR) model for the log-abundances (see errorsarlm for the model; estimates are provided by the parameter sarestimate). Spatial autocorrelation of a species' presences is governed by the parameter p.nb, sarestimate and a list of neighbors for each region.

Usage

regpop.sar(abmat, prab01=NULL, sarestimate=prab.sarestimate(abmat),
                    p.nb=NULL,
                    vector.species=prab01$regperspec,
                    pdf.regions=prab01$specperreg/(sum(prab01$specperreg)),
                   count=FALSE)

Arguments

Value

A matrix of abundance values, rows are regions, columns are species.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

References

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.

See Also

autoconst estimates p.nb from matrices of class prab. These are generated by prabinit.

abundtest uses regpop.sar as a null model for tests of clustering.

randpop.nb (analogous function for simulating presence-absence data)

Examples

options(digits=4)
data(siskiyou)
set.seed(1234)
x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb,
             distance="none")
# Not run; this needs package spdep.
# regpop.sar(x, p.nb=0.046)
regpop.sar(x, p.nb=0.046, sarestimate=prab.sarestimate(x,sar=FALSE))

Herbs of the Siskiyou Mountains

Description

Distributions of species of herbs in relation to elevation on quartz diorite in the central Siskiyou Mountains. All values are per mille frequencies in transects (The number of 1 m2 quadrats, among 1000 such quadrats, in which a species was observed, based on 1250 1m2 quadrats in the first 5 transects, and 400 1m2 quadrats in 6. transect). Observed presences in the transect, outside the sampling plots, were coded as 0.2. Rows correspond to species, columns correspond to regions.

Usage

data(siskiyou)

Format

Three objects are generated:

siskiyou

numeric matrix giving the 144*6 abundance values.

siskiyou.nb

neighborhood list for the 6 regions.

siskiyou.groups

integer vector of length 144, giving group memberships for the 144 species.

Details

Reads from example data files LeiMik1.dat, LeiMik1NB.dat, LeiMik1G.dat.

Source

Whittaker, R. H. 1960. Vegetation of the Siskiyou Mountains, Oregon and California. Ecol. Monogr. 30: 279-338 (table 14).

Examples

data(siskiyou)

Average within-group distances for given groups

Description

Generates average within-group distances (overall and group-wise) from a dissimilarity matrix and a given grouping.

Usage

  specgroups(distmat,groupvector, groupinfo) 

Arguments

Value

A list with parameters

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

Examples

  options(digits=4)
  data(siskiyou)
  x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb,
              distance="logkulczynski")  
  groupvector <- as.factor(siskiyou.groups)
  ng <- length(levels(groupvector))
  lg <- levels(groupvector)
  nsg <- numeric(0)
    for (i in 1:ng) nsg[i] <- sum(groupvector==lg[i])
  groupinfo <- list(lg=lg,ng=ng,nsg=nsg)
  specgroups(x$distmat,groupvector,groupinfo)

Stress values for different dimensions of Kruskal's MDS

Description

Computes Kruskal's nonmetric multidimensional scaling isoMDS on alleleobject or prab-objects for different output dimensions in order to compare stress values.

Usage

  stressvals(x,mdsdim=1:12,trace=FALSE)

Arguments

Details

Note that zero distances between non-identical objects are replaced by the smallest nonzero distance divided by 10 to prevent isoMDS from producing an error.

Value

A list with components

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

Examples

  options(digits=4)
  data(tetragonula)
  set.seed(112233)
  taiselect <- sample(236,40)
# Use data subset to make execution faster.
  tnb <-
  coord2dist(coordmatrix=tetragonula.coord[taiselect,],
    cut=50,file.format="decimal2",neighbors=TRUE)
  ta <- alleleconvert(strmatrix=tetragonula[taiselect,])
  tai <- alleleinit(allelematrix=ta,neighborhood=tnb$nblist)
  stressvals(tai,mdsdim=1:3)$MDSstress

Microsatellite genetic data of Tetragonula bees

Description

Genetic data for 236 Tetragonula (Apidae) bees from Australia and Southeast Asia, see Franck et al. (2004). The data give pairs of alleles (codominant markers) for 13 microsatellite loci.

Usage

data(tetragonula)

Format

Two objects are generated:

tetragonula

A data frame with 236 observations and 13 string variables. Strings consist of six digits each. The format is derived from the data format used by the software GENEPOP (Rousset 2008). Alleles have a three digit code, so a value of "258260" on variable V10 means that on locus 10 the two alleles have codes 258 and 260. "000" refers to missing values.

tetragonula.coord

a 236*2 matrix. Coordinates of locations of individuals in decimal format, i.e. the first number is latitude (negative values are South), with minutes and seconds converted to fractions. The second number is longitude (negative values are West).

Details

Reads from example data file Heterotrigona_indoFO.dat.

Source

Franck, P., E. Cameron, G. Good, J.-Y. Rasplus, and B. P. Oldroyd (2004) Nest architecture and genetic differentiation in a species complex of Australian stingless bees. Mol. Ecol. 13, 2317-2331.

Rousset, F. (2008) genepop'007: a complete re-implementation of the genepop software for Windows and Linux. Molecular Ecology Resources 8, 103-106.

Examples

data(tetragonula)

Convert abundance matrix into presence/absence matrix

Description

Converts abundance matrix into binary (logical) presence/absence matrix (TRUE if abundance>0).

Usage

toprab(prabobj)

Arguments

Value

Logical matrix with same dimensions as prabobj$prab as described above.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

Examples

  data(siskiyou)
  x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb,
             distance="none")
  toprab(x)

Internal: create allele list out of character matrix

Description

Creates a list of lists, such as required by alleledist, from the charmatrix component of an alleleobject.

Usage

   unbuild.charmatrix(charmatrix,n.individuals,n.variables)

Arguments

Value

A list of lists. In the "outer" list, there are n.variables lists, one for each locus. In the "inner" list, for every individual there is a vector of two codes (typically characters, see alleleinit) for the two alleles in that locus.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

See Also

alleleinit, build.charmatrix

Examples

  data(tetragonula)
  tnb <-
  coord2dist(coordmatrix=tetragonula.coord[1:50,],cut=50,file.format="decimal2",neighbors=TRUE)
  ta <- alleleconvert(strmatrix=tetragonula[1:50,])
  tai <- alleleinit(allelematrix=ta,neighborhood=tnb$nblist,distance="none")
  str(unbuild.charmatrix(tai$charmatrix,50,13)) 

Genetic AFLP data of Veronica plants

Description

0-1 data indicating whether dominant markers are present for 583 different AFLP bands ranging from 61 to 454 bp of 207 plant individuals of Veronica (Pentasepalae) from the Iberian Peninsula and Morocco (Martinez-Ortega et al., 2004).

Usage

data(veronica)

Format

Two objects are generated:

veronica

0-1 matrix with 207 individuals (rows) and 583 AFLP bands (columns).

veronica.coord

a 207*2 matrix. Coordinates of locations of individuals in decimal format, i.e. the first number is latitude (negative values are South), with minutes and seconds converted to fractions. The second number is longitude (negative values are West).

Details

Reads from example data files MartinezOrtega04AFLP.dat, MartinezKoord.dat.

Source

Martinez-Ortega, M. M., L. Delgado, D. C. Albach, J. A. Elena-Rossello, and E. Rico (2004). Species boundaries and phylogeographic patterns in cryptic taxa inferred from AFLP markers: Veronica subgen. Pentasepalae (Scrophulariaceae) in the Western Mediterranean.Syst. Bot. 29, 965-986.

Examples

data(veronica)

Overwater distances between islands in the Aegean sea

Description

Distance matrix of overwater distances in km between 34 islands in the Aegean sea.

Usage

data(waterdist)

Format

A symmetric 34*34 distance matrix.

Details

Reads from example data file Waterdist.dat, in which there is a 35th column and line with distances to Turkey mainland.

Source

B. Hausdorf and C. Hennig (2005) The influence of recent geography, palaeography and climate on the composition of the faune of the central Aegean Islands. Biological Journal of the Linnean Society 84, 785-795.

Examples

data(waterdist)