prabclus/0000755000176200001440000000000014515610012012060 5ustar liggesusersprabclus/NAMESPACE0000644000176200001440000000172513475237015013320 0ustar liggesusers# Remove the previous line if you edit this file # Automatically generated one not changed but nicked # Export all names exportPattern(".") # Import all packages listed as Imports or Depends import( MASS, mclust ) # register # print.alleleobject print.comprabclust print.nnclean print.prab # print.prabclust print.summary.prabtest summary.prabtest S3method(print, alleleobject) S3method(print, comprabclust) S3method(print, nnclean) S3method(print, prab) S3method(print, prabclust) S3method(print, summary.prabtest) S3method(print, regdistbetween) S3method(print, regeqdist) S3method(summary, prabtest) importFrom("grDevices", "grey") importFrom("graphics", "abline", "box", "hist", "lines", "plot", "points") importFrom("stats", "as.dist", "chisq.test", "cmdscale", "coef", "cutree", "dist", "hclust", "lm", "mad", "median", "pnorm", "ppois", "rnorm", "runif", "model.matrix", "pt", "sd", "var") importFrom("utils", "read.table", "write.table") prabclus/data/0000755000176200001440000000000013604677755013022 5ustar liggesusersprabclus/data/waterdist.txt.gz0000644000176200001440000000276713604677755016224 0ustar liggesusersYn6D .4!@-^K=TUk͵Yr_y\w:K-sYhܷsu-;|G;'hba\ ‘nkA$}r];[kcS3q+IDLSxũ-}8>57=|u{qoy$ɜ&Js ^ ]o-B聓Ĕ\(<7L(Gg 48%vG+3UWžm'~aq|fŠp\p[%r/V ?`ɃmXۓ}e nK&Z"0 @押fo#ّtރe<_7~EQb Cѳ!+e|mtqkз$[#%Owm!OB _]@gR:ՖYU̩_Ϋu-NЎCUIL=zҒ NRBT簓J+ G N*lO&Phɽ FGdf=7XwCt籇:-=tG`D6 MDO̹MPb1Va{f)GU'>}QyE\_DʛԷezK<"Tu]ǐ 8WiG4p|d9"In 5ǸiRdSUwVh}07 ЛFS7katN*FW PWdFJؚ}L9)2<nlEx#36> z֟E]ߜcfĂmh2T{d℘\+C ^+gD Yi=1%Y}Ep9Ac\\WbUѕV)9͕B"Cm!o=IY_oJ4=PP}9o8|貎MEh<ׅ5i+}r;v=BMp+;eۤ5c˨R|X(^Quc`3g u,Q%m_#ժ^'^vPNޱKâIӊ9癜;SGURe#jՍ2B@o[[7ښ[Hk;`RQuH#Fm%; 9VM&l=^uކ| qC ԤdͱxK9&пݣƶ[/~y.W*BA9Adi39_bP[+SzD>ʥM VfK ߺprabclus/data/nb.rda0000644000176200001440000000036512015142422014062 0ustar liggesusersM 04Bt…RDDjkՅ<=gȞ@Lq2cKysyPB%T(S+sIÆoM 904\WO>juQ 0a1b۽c=${R9'ÂuOiljz<ugK-I(ܜ!g_aW:}wT‘ؿ"N8>>{G=9D6Dprabclus/data/siskiyou.rda0000644000176200001440000000252012015142356015343 0ustar liggesusersKoE;kBf$|!BB(r G#cd5^\ ({GG']c Y%-SU=5ݞ:19WscW>W++fIR߹w>\6:oJ= -]ׅPJ\Yp虓?Ԏ3e3++q})NS:)d+Ey5yN~xGH<~7#umno 7~Cn > _D? ?wݫ,1u|?9&ls!Z>Y> 8"?7QF??+gFnᏛ쿭^kwS%Ɠ\8R{ ϊx6R>nޟ6HdνE7CK"lOd?{YzS%ٟՏC"37g{^whva<< /:#/ՋI~#\.zH뙇Off!绕}qvHU<[9{Ύ>ԏ;'BM<J7]*FhGG2sO;xx V7`vv}J=|/O}$c}k͍>r{Wh픿Z)XoϟHE˂Lml|Ot!> =;"s{&D^`~  \Gۭ[mč/r1h 8̳X$&|![Nݠh,_ŽP.g.E1!707I T~^, \dXxl$oĔ~p{Ur\p2^صUo귩g~~ q 8cЁ`1~y]|ɟx9 ڧIsI(s0yŁ ~tJ8#9R?@:R7bk%Iؗ|HNT? dUR{!prabclus/data/waterdist.rda0000644000176200001440000000330513604677755015521 0ustar liggesusersSGu&(D)=O( &Z@VV*jqwL}dsϽ'j**R~TUȏ;o{UA/_|A_pT. q . ଠG0($ aAfˊOpJ&LQop4\$u9癛V|jbuʠK fcK/\DIpFЍc |99''(N~pʠW?:'~JЌN|gXm3p1B!wq~7fX ڳmq`OI𑹷ϊO1%|ـOccNJ=W /`IXJ+s? 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Species are generated i.i.d.. Spatial autocorrelation of a species' presences is governed by the parameter \code{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{neighbors}{A list with a component for every region. The components are vectors of integers indicating neighboring regions. A region without neighbors (e.g., an island) should be assigned a list \code{numeric(0)}.} \item{p.nb}{numerical between 0 and 1. The probability that a new region is drawn from the non-neighborhood of the previous regions belonging to a species under generation. Note that for a given presence-absence matrix, this parameter can be estimated by \code{autoconst} (called \code{pd} there).} \item{n.species}{integer. Number of species.} \item{n.regions}{integer. Number of regions.} \item{vector.species}{vector of integers. If \code{species.fixed=TRUE}, \code{vector.species} must have length \code{n.species} and gives the sizes (i.e., numbers of regions) of the species to generate. Else, the sizes are generated randomly from the empirical distribution of \code{vector.species}.} \item{species.fixed}{logical. See \code{vector.species}.} \item{pdf.regions}{numerical vector of length \code{n.species}. The entries must sum up to 1 and give probabilities for the regions to be drawn during the generation of a species. These probabilities are used conditional on the new region being a neighbor or a non-neighbor of the previous regions of the species, see \code{p.nb}.} \item{count}{logical. If \code{TRUE}, the number of the currently generated species is printed.} \item{pdfnb}{logical. If \code{TRUE}, the probabilities of the regions are modified according to the number of neighboring regions by dividing them relative to the others by min(1,number of neighbors).} } \details{ The principle is that a single species with given size is generated one-by-one region. The first region is drawn according to \code{pdf.regions}. For all following regions, a neighbor or non-neighbor of the previous configuration is added (if possible), as explained in \code{pdf.regions}, \code{p.nb}. } \value{ A 0-1-matrix, rows are regions, columns are species. } \references{ Hennig, C. and Hausdorf, B. (2004) Distance-based parametric bootstrap tests for clustering of species ranges. \emph{Computational Statistics and Data Analysis} 45, 875-896. \url{http://stat.ethz.ch/Research-Reports/110.html}. Hausdorf, B. and Hennig, C. (2003) Biotic Element Analysis in Biogeography. \emph{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. \emph{Oecologia} 135, 102-109. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{autoconst}} estimates \code{p.nb} from matrices of class \code{prab}. These are generated by \code{\link{prabinit}}. \code{\link{prabtest}} uses \code{randpop.nb} as a null model for tests of clustering. An alternative model is given by \code{\link{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)) } \keyword{spatial}% at least one, from doc/KEYWORDS prabclus/man/prabclust.Rd0000644000176200001440000002024714515525150015136 0ustar liggesusers\name{prabclust} \alias{prabclust} \alias{print.prabclust} %- Also NEED an `\alias' for EACH other topic documented here. \title{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 \code{mclust}) to the MDS points. The solution is plotted. A standard execution (using the default distance of \code{prabinit}) will be \cr \code{prabmatrix <- prabinit(file="path/prabmatrixfile", neighborhood="path/neighborhoodfile")}\cr \code{clust <- prabclust(prabmatrix)}\cr \code{print(clust)} \cr Examples for species delimitation are given below in the examples section. \bold{Note:} Data formats are described on the \code{\link{prabinit}} and \code{\link{alleleinit}} help pages. You may also consider the example datasets \code{kykladspecreg.dat}, \code{nb.dat}, \code{Heterotrigona_indoFO.txt} or \code{MartinezOrtega04AFLP.dat}. \cr \bold{Note:} \code{prabclust} calls the function \code{\link[mclust]{mclustBIC}} in package mclust. An alternative is the use of \code{\link{hprabclust}}. } \usage{ prabclust(prabobj, mdsmethod = "classical", mdsdim = 4, nnk = ceiling(prabobj$n.species/40), nclus = 0:9, modelid = "all", permutations=0) \method{print}{prabclust}(x, bic=FALSE, ...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{prabobj}{object of class \code{prab} as generated by \code{prabinit}. Presence-absence data to be analyzed. (This can be geographical information for range clustering Can also be an object of class \code{alleleobject} as generated by \code{alleleinit}. } \item{mdsmethod}{\code{"classical"}, \code{"kruskal"}, or \code{"sammon"}. The MDS method to transform the distances to data points. \code{"classical"} indicates metric MDS by function \code{cmdscale}, \code{"kruskal"} is non-metric MDS.} \item{mdsdim}{integer. Dimension of the MDS points. For \code{mdsmethod=="kruskal"}, \code{\link{stressvals}} can be used to see how the stress depends on \code{mdsdim} in order to choose \code{mdsdim} to get a small stress (smaller than 5\%, say).} \item{nnk}{integer. Number of nearest neighbors to determine the initial noise estimation by \code{NNclean}. \code{nnk=0} fits the model without a noise component.} \item{nclus}{vector of integers. Numbers of clusters to perform the mixture estimation.} \item{modelid}{string. Model name for \code{mclustBIC} (see the corresponding help page; all models or combinations of models mentioned there are possible). \code{modelid="all"} compares all possible models. Additionally, \code{"noVVV"} is possible, which fits all methods except \code{"VVV"}.} \item{permutations}{integer. It has been found occasionally that depending on the order of observations the algorithms \code{isoMDS} and \code{mclustBIC} converge to different solutions. This is because these methods require an ordering of the distances, which, if equal distance values are involved, may depend on the order. \code{prabclust} uses a standard ordering which should give a reproducible solution in these cases as well. However, if \code{permutations>0}, which gives a number of random permutations of the observations, the algorithm is carried out for every permutation and the best solution (in terms of the BIC, based on the lowest stress MDS configuration) is given out (for many datasets this won't change anything except increasing the computing time).} \item{x}{object of class \code{prabclust}. Output of \code{prabclust}.} \item{bic}{logical. If \code{TRUE}, information about the BIC criterion to choose the model is displayed.} \item{...}{necessary for summary method.} } \details{ Note that if \code{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{ \code{print.prabclust} does not produce output. \code{prabclust} generates an object of class \code{prabclust}. This is a list with components \item{clustering}{vector of integers indicating the cluster memberships of the species. Noise can be recognized by output component \code{symbols}.} \item{clustsummary}{output object of \code{summary.mclustBIC}. A list giving the optimal (according to BIC) parameters, conditional probabilities `z', and loglikelihood, together with the associated classification and its uncertainty. Note that the numbering of clusters may differ from \code{clustering}, see \code{csreorder}.} \item{bicsummary}{output object of \code{mclustBIC}. Bayesian Information Criterion for the specified mixture models and numbers of clusters. } \item{points}{numerical matrix. MDS configuration.} \item{nnk}{see above.} \item{mdsdim}{see above.} \item{mdsmethod}{see above.} \item{symbols}{vector of characters, similar to \code{clustering}, but indicating estimated noise and points belonging to one-point-components (which should be interpreted as some kind of noise as well) by \code{"N"}. } \item{permchange}{logical. If \code{TRUE}, \code{permutations>0} has been used and the best solution is different from the one obtained by the standard ordering. (This is just for information and has no further operational consequences.)} } \references{ Fraley, C. and Raftery, A. E. (1998) How many clusters? Which clustering method? - Answers via Model-Based Cluster Analysis. \emph{Computer Journal} 41, 578-588. Hausdorf, B. and Hennig, C. (2010) Species Delimitation Using Dominant and Codominant Multilocus Markers. \emph{Systematic Biology}, 59, 491-503. Hennig, C. and Hausdorf, B. (2004) Distance-based parametric bootstrap tests for clustering of species ranges. \emph{Computational Statistics and Data Analysis} 45, 875-896. \url{http://stat.ethz.ch/Research-Reports/110.html}. } \note{ Note that we used \code{mdsmethod="kruskal"} in our publications, but \code{mdsmethod="classical"} is now the default, because of occasional numerical instabilities of the \code{isoMDS}-implementation for Jaccard, Kulczynski or geco distance matrices. Sometimes, \code{prabclust} produces an error because \code{mclustBIC} cannot handle all models properly. In this case we recommend to change the \code{modelid} parameter. \code{"noVVV"} and \code{"VVV"} are reasonable alternative choices (one of these is expected to reproduce the error, but the other one might work). } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link[mclust]{mclustBIC}}, \code{\link[mclust]{summary.mclustBIC}}, \code{\link{NNclean}}, \code{\link{cmdscale}}, \code{\link{isoMDS}}, \code{\link{sammon}}, \code{\link{prabinit}}, \code{\link{hprabclust}}, \code{\link{alleleinit}}, \code{\link{stressvals}}. } \examples{ \donttest{ # 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)) } } \keyword{cluster}% at least one, from doc/KEYWORDS \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/lcomponent.Rd0000644000176200001440000000213113473260375015314 0ustar liggesusers\name{lcomponent} \alias{lcomponent} %- Also NEED an `\alias' for EACH other topic documented here. \title{Largest connectivity component} \description{ Computes the size of the largest connectivity component of the graph of \code{ncol(distmat)} vertices with edges defined by the smallest \code{ne} distances. } \usage{ lcomponent(distmat, ne = floor(3*ncol(distmat)/4)) } %- maybe also `usage' for other objects documented here. \arguments{ \item{distmat}{symmetric distance matrix.} \item{ne}{integer.} } \value{ list with components \item{lc}{size of the largest connectivity component.} \item{ne}{see above.} } \references{ Hennig, C. and Hausdorf, B. (2004) Distance-based parametric bootstrap tests for clustering of species ranges. \emph{Computational Statistics and Data Analysis} 45, 875-896.} \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{\code{\link{prabtest}}} \examples{ data(kykladspecreg) j <- jaccard(t(kykladspecreg)) lcomponent(j) } \keyword{cluster}% at least one, from doc/KEYWORDS prabclus/man/waterdist.Rd0000644000176200001440000000135113473260375015147 0ustar liggesusers\name{waterdist} \alias{waterdist} % \non_function{} \title{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.} \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. \emph{Biological Journal of the Linnean Society} 84, 785-795. } \details{ Reads from example data file \code{Waterdist.dat}, in which there is a 35th column and line with distances to Turkey mainland. } \examples{ data(waterdist) } \keyword{datasets} % \keyword{spatial} prabclus/man/prabinit.Rd0000644000176200001440000001403413473260375014753 0ustar liggesusers\name{prabinit} \alias{prabinit} \alias{print.prab} \alias{prab} %- Also NEED an `\alias' for EACH other topic documented here. \title{Presence-absence/abundance matrix initialization} \description{ \code{prabinit} converts a matrix into an object of class \code{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). \code{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) \method{print}{prab}(x, ...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{file}{string. non-negative matrix ASCII file (such as example dataset \code{kykladspecreg.dat}) from which the matrix is read by \code{read.table}. The usual interpretation is that it is a species-by-regions matrix of species presences/absences (0-1 matrix) or abundances.} \item{prabmatrix}{matrix with non-negative entries. Either \code{file} or \code{prabmatrix} should be \code{NA}.} \item{rows.are.species}{logical. If \code{TRUE}, rows are interpreted as species and columns are interpreted as regions. In this case, rows and columns are interchanged by \code{prabinit}.} \item{neighborhood}{A string or a list with a component for every region. The components are vectors of integers indicating neighboring regions. A region without neighbors (e.g., an island) should be assigned a vector \code{numeric(0)}. If \code{neighborhood} is a filename, it is attempted to read such a list from a file, where every row should correspond to one region (such as example dataset \code{nb.dat}). If \code{neighborhood="none"}, all neighborhoods are set to \code{numeric(0)}. The neighborhood can be tested by \code{\link{nbtest}} for consistency.} \item{nbbetweenregions}{logical. If \code{TRUE}, the neighborhood is defined between regions as explained above. Otherwise it is defined between species (or individuals, if this is used for species delimitation).} \item{geodist}{matrix of non-negative reals. Geographical distances between regions. Only used if \code{distance="geco"}.} \item{gtf}{tuning constant for geco-distance if \code{distance="geco"}, see \code{geco}.} \item{distance}{\code{"kulczynski"}, \code{"jaccard"}, \code{"geco"}, \code{"qkulczynski"}, \code{"logkulczynski"} (this calls function \code{qkulczynski} with \code{log.distance=TRUE}), \code{"dice"}, or \code{"none"}. The distance measure between species to compute by \code{prabinit}.} \item{toprab}{logical. If \code{TRUE}, a presence-absence matrix is computed from the non-negative input matrix. "Absence", i.e., the entry 0, is chosen if the original entry is 0, or the original entry is smaller than or equal to \code{toprabp} times the sum of entries in the corresponding region, and log(original entry) is considered to be a lower outlier compared with the other entries of the corresponding species (see \code{outc}). "Presence", i.e., the entry 1, thus means that the original entry is non-negligible w.r.t. the species or w.r.t. the region.} \item{toprabp}{numerical between 0 and 1, see \code{toprab}.} \item{outc}{numerical. Tuning constant for the outlier identification associated with \code{toprab=TRUE}. An entry smaller than or equal to \code{outc*mad} times the median is considered as a lower outlier.} \item{x}{object of class \code{prab}.} \item{...}{necessary for print method.} } \details{ Species that are absent in all regions are omitted.} \value{ \code{prabinit} produces an object of class \code{prab}, which is a list with components \item{distmat}{distance matrix between species.} \item{prab}{abundance or presence/absence matrix (if presence/absence, the entries are logical). Rows are regions, columns are species.} \item{nb}{neighborhood list, see above.} \item{regperspec}{vector of the number of regions occupied by a species.} \item{specperreg}{vector of the number of species present in a region.} \item{n.species}{number of species (in the \code{prab}-object, see \code{nonzero}).} \item{n.regions}{number of regions.} \item{distance}{string denoting the chosen distance measure.} \item{geodist}{non-negative matrix. see above.} \item{gtf}{numeric. see above.} \item{spatial}{\code{TRUE}, if there is a specified neighborhood structure.} \item{nonempty.species}{logical vector. The length is the number of species in the original file/matrix. If \code{FALSE}, the corresponding species had only zero entries and was therefore absent. Note that these species are not included in any other component of a \code{prab} object, i.e., \code{n.species} is the number of \code{TRUE}-entries in \code{nonzero}.} \item{nbbetweenregions}{see above.} } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \references{ Hausdorf, B. and Hennig, C. (2010) Species Delimitation Using Dominant and Codominant Multilocus Markers. \emph{Systematic Biology}, 59, 491-503. } \seealso{\code{\link{read.table}}, \code{\link{jaccard}}, \code{\link{kulczynski}}, \code{\link{geco}}, \code{\link{qkulczynski}}, \code{\link{nbtest}}, \code{\link{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) } \keyword{spatial}% at least one, from doc/KEYWORDS \keyword{cluster}% __ONLY ONE__ keyword per line prabclus/man/geco.Rd0000644000176200001440000000526313473260375014064 0ustar liggesusers\name{geco} \alias{geco} %- Also NEED an `\alias' for EACH other topic documented here. \title{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{regmat}{0-1-matrix. Columns are species, rows are regions.} \item{geodist}{\code{dist}-object or symmetric non-negative matrix. Geographical distances between regions.} \item{transform}{transformation applied to the distances before computation of geco coefficient, see details. "piece" means piecewise linear, namely distance/(\code{tf}*maximum distance) if distance<\code{tf}*maximum distance, and 1 otherwise, "log" means \code{log((tf*distance)+1)}, "sqrt" means \code{sqrt(tf*distance)}, "none" means no transformation.} \item{tf}{tuning constant for transformation. See \code{transform}.} \item{countmode}{optional positive integer. Every 'countmode' algorithm runs 'geco' shows a message.} } \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 \code{transform}. If the between-regions distance is 1 for all pairs of non-equal regions, the geco distance degenerates to the Kulczynski distance, see \code{kulczynski}. } \value{ A symmetrical matrix of geco distances. } \references{ Hennig, C. and Hausdorf, B. (2006) A robust distance coefficient between distribution areas incorporating geographic distances. \emph{Systematic Biology} 55, 170-175. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{kulczynski}} } \examples{ options(digits=4) data(kykladspecreg) data(waterdist) geco(t(kykladspecreg),waterdist) } \keyword{cluster}% at least one, from doc/KEYWORDS \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/alleleconvert.Rd0000644000176200001440000001354213475513501015777 0ustar liggesusers\name{alleleconvert} \alias{alleleconvert} %- Also NEED an `\alias' for EACH other topic documented here. \title{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 \code{"genepop"} and \code{"structure"} into \code{"structurama"} and \code{"prabclus"}. \code{"genepop"} is a version of the format used by the package GENEPOP (Rousset, 2008), \code{"structure"} is a version of what is used by STRUCTURE (Pritchard et al., 2000), another one is \code{"structureb"}. \code{"structurama"} is a version of what is used by STRUCTURAMA (Huelsenbeck and Andolfatto, 2007) and \code{"prabclus"} is required by the function \code{\link{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{file}{string. Filename of input file, see details. One of \code{file} and \code{strmatrix} needs to be specified.} \item{strmatrix}{matrix or data frame of strings, see details. One of \code{file} and \code{strmatrix} needs to be specified.} \item{format.in}{string. One of \code{"genepop"}, \code{"structure"}, or \code{"structureb"}, see details.} \item{format.out}{string. One of \code{"structurama"} or \code{"prabclus"}, see details.} \item{alength}{integer. If \code{format.in="genepop"}, length of code for a single allele.} \item{orig.nachar}{string. Code for missing values in input data.} \item{new.nachar}{string. Code for missing values in output data.} \item{rows.are.individuals}{logical. If \code{TRUE}, rows are interpreted as individuals and columns (variables if \code{strmatrix} is a data frame) as loci.} \item{firstcolname}{logical. If \code{TRUE}, it is assumed that the first column contains row names.} \item{aletters}{character vector. String of default characters for alleles if \code{format.out=="prabclus"} (the default is fine unless there is a locus that can have more than 62 different alleles in the dataset).} \item{outfile}{string. If specified, the output matrix (omitting quotes) is written to a file of this name (including row names if \code{fistcolname==TRUE}).} \item{skip}{number of rows to be skipped when reading data from a file (\code{skip}-argument of \code{\link{read.table}}).} } \details{ The formats are as follows (described is the format within R, i.e., for the input, the format of \code{strmatrix}; if \code{file} is specified, the file is read with \code{read.table(file,colClasses="character")} and should give the format explained below - note that \code{colClasses="character"} implies that quotes are not needed in the input file): \describe{ \item{genepop}{Alleles are coded by strings of length \code{alength} and there is no space between the two alleles in a locus, so a value of \code{"258260"} means that in the corresponding locus the two alleles have codes 258 and 260.} \item{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.} \item{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).} \item{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 \code{"258260"} in \code{"genepop"} corresponds to \code{"(258,260)"} in \code{"structurama"}.} \item{prabclus}{Alleles are coded by a single character and there is no space between the two alleles in a locus (e.g., \code{"AC"}).} } } \value{ A matrix of strings in the format specified as \code{format.out} with an attribute \code{"alevels"}, a vector of all used allele codes if \code{format.out=="prabclus"}, otherwise vector of allele codes of last locus. } \references{ Huelsenbeck, J. P., and P. Andolfatto (2007) Inference of population structure under a Dirichlet process model. \emph{Genetics} 175, 1787-1802. Pritchard, J. K., M. Stephens, and P. Donnelly (2000) Inference of population structure using multi-locus genotype data. \emph{Genetics} 155, 945-959. Rousset, F. (2008) genepop'007: a complete re-implementation of the genepop software for Windows and Linux. \emph{Molecular Ecology Resources} 8, 103-106. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{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) } \keyword{manip}% __ONLY ONE__ keyword per line prabclus/man/allelepaircomp.Rd0000644000176200001440000000235213473260375016134 0ustar liggesusers\name{allelepaircomp} \alias{allelepaircomp} %- Also NEED an `\alias' for EACH other topic documented here. \title{Internal: compares two pairs of alleles} \description{ Used for computation of the genetic distances \code{\link{alleledist}}. %- \code{\link{neidist}}, \code{\link{chorddist}}. } \usage{ allelepaircomp(allelepair1,allelepair2,method="sum") } %- maybe also `usage' for other objects documented here. \arguments{ \item{allelepair1}{vector of two allele codes (usually characters), or \code{NA}.} \item{allelepair2}{vector of two allele codes (usually characters), or \code{NA}.} \item{method}{one of \code{"sum"} or \code{"geometrical"}.} } \value{ If \code{method=="sum"}, number of shared alleles (0, 1 or 2), or \code{NA}. If \code{method=="geometrical"}, 0, 0.5, \code{sqrt(0.5)} (in case that one of the allelepairs is double such as in \code{c("A","B"),c("A","A")}) or 1, or \code{NA}. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{alleledist}} %- , \code{\link{neidist}}, \code{\link{chorddist}}. } \examples{ allelepaircomp(c("A","B"),c("A","C")) } \keyword{cluster}% at least one, from doc/KEYWORDS prabclus/man/NNclean.Rd0000644000176200001440000000652613473260375014470 0ustar liggesusers\name{NNclean} \alias{NNclean} \alias{print.nnclean} %- Also NEED an `\alias' for EACH other topic documented here. \title{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) \method{print}{nnclean}(x, ...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{data}{numerical matrix or data frame.} \item{k}{integer. Number of considered nearest neighbors per point.} \item{distances}{distance matrix object of class \code{dist}. If specified, it is used instead of computing distances from the data.} \item{edge.correct}{logical. If \code{TRUE} and the data is two-dimensional, neighbors for points at the edges of the parent region of the noise Poisson process are determined after wrapping the region onto a toroid.} \item{wrap}{numerical. If \code{edge.correct=TRUE}, points in a strip of size \code{wrap*range} along the edge for each variable are candidates for being neighbors of points from the opposite.} \item{convergence}{numerical. Convergence criterion for EM-algorithm.} \item{plot}{logical. If \code{TRUE}, a histogram of the distance to kth nearest neighbor and fit is plotted.} \item{quiet}{logical. If \code{FALSE}, the likelihood is printed during the iterations.} \item{x}{object of class \code{nnclean}.} \item{...}{necessary for print methods.} } \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{ \code{NNclean} returns a list of class \code{nnclean} with components \item{z}{0-1-vector of length of the number of data points. 1 means cluster, 0 means noise.} \item{probs}{vector of estimated a priori probabilities for each point to belong to the cluster component.} \item{k}{see above.} \item{lambda1}{intensity parameter of cluster component.} \item{lambda2}{intensity parameter of noise component.} \item{p}{estimated probability of cluster component.} \item{kthNND}{distance to kth nearest neighbor.} } \references{ Byers, S. and Raftery, A. E. (1998) Nearest-Neighbor Clutter Removal for Estimating Features in Spatial Point Processes, \emph{Journal of the American Statistical Association}, 93, 577-584. } \author{R-port by Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en},\cr original Splus package by S. Byers and A. E. Raftery. } \note{The software can be freely used for non-commercial purposes, and can be freely distributed for non-commercial purposes only.} \examples{ library(mclust) data(chevron) nnc <- NNclean(chevron[,2:3],15,plot=TRUE) plot(chevron[,2:3],col=1+nnc$z) } \keyword{multivariate}% at least one, from doc/KEYWORDS \keyword{cluster}% __ONLY ONE__ keyword per line prabclus/man/geo2neighbor.Rd0000644000176200001440000000174113473260375015516 0ustar liggesusers\name{geo2neighbor} \alias{geo2neighbor} %- Also NEED an `\alias' for EACH other topic documented here. \title{Neighborhood list from geographical distance} \description{ Generates a neighborhood list as required by \code{prabinit} from a matrix of geographical distances. } \usage{ geo2neighbor(geodist,cut=0.1*max(geodist)) } %- maybe also `usage' for other objects documented here. \arguments{ \item{geodist}{\code{dist}-object or symmetric non-negative matrix. Geographical distances between regions.} \item{cut}{non-negative numerical. All pairs of regions with \code{distance<=cut} are treated as neighbors.} } \value{ A list of integer vectors, giving the set of neighbors for every region. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \examples{ data(waterdist) geo2neighbor(waterdist) } \keyword{cluster}% at least one, from doc/KEYWORDS \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/regdistdiffone.Rd0000644000176200001440000000601413475070460016132 0ustar liggesusers\name{regdistdiffone} \alias{regdistdiffone} %- Also NEED an `\alias' for EACH other topic documented here. \title{Regression difference within reference group and between-group dissimilarities} \description{ Given two dissimilarity matrices \code{dmx} and \code{dmy}, an indicator vector \code{x} and a grouping, this computes the difference between standard least squares regression predictions at point \code{xcenterbetween}. The regressions are based on the dissimilarities in \code{dmx} vs. \code{dmy} for objects indicated in \code{x}. \code{grouping} indicates the two groups, and the difference is computed between regressions based on (a) the within-group distances of the reference group \code{rgroup} and (b) these together with the between-group distances. } \usage{ regdistdiffone(x,dmx,dmy,grouping,xcenter=0,xcenterbetween=0,rgroup) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{vector of logicals of length of the number of objects on which dissimilarities \code{dmx} and \code{dmy} are based.} \item{dmx}{dissimilarity matrix or object of class \code{\link{dist}}. Explanatory dissimilarities.} \item{dmy}{dissimilarity matrix or object of class \code{\link{dist}}. Response dissimilarities.} \item{grouping}{vector of length of the number of objects on which dissimilarities \code{dmx} and \code{dmy} are based. Grouping vector. Regressions will be based on the first two values that appear in \code{unique(grouping[x])} (note that objects that are not assigned to one of these groups will be ignored); normally \code{grouping} should indicate only two groups on the objects with \code{x=TRUE}, and then these are used.} \item{xcenter}{numeric. Dissimilarities \code{dmx} are centered by this, i.e., this value is subtracted from the dissimilarities before regression.} \item{xcenterbetween}{numeric. This specifies the x- (dissimilarity) value at which predictions from the two regressions are compared. Note that this is interpreted as after centering by \code{xcenter}.} \item{rgroup}{one of the values of \code{grouping}, specifying the reference group.} } \value{ Difference between standard least squares regression predictions for the two regressions at point \code{xcenterbetween}. } \references{ Hausdorf, B. and Hennig, C. (2019) Species delimitation and geography. Submitted. } \seealso{ \code{\link{regdistbetweenone}} } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \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$distmat,grouping=species, xcenter=0,xcenterbetween=100,rgroup=2) } \keyword{regression}% __ONLY ONE__ keyword per line \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/regpop.sar.Rd0000644000176200001440000001004113473260375015215 0ustar liggesusers\name{regpop.sar} \alias{regpop.sar} %- Also NEED an `\alias' for EACH other topic documented here. \title{Simulation of abundance matrices (non-clustered)} \description{ Generates a simulated matrix where the rows are interpreted as regions and the columns as species, and the entries are abundances. Species are generated i.i.d. in two steps. In the first step, a presence-absence matrix is generated as in \code{randpop.nb}. In the second step, conditionally on presence in the first step, abundance values are generated according to a simultaneous autoregression (SAR) model for the log-abundances (see \code{\link[spdep]{errorsarlm}} for the model; estimates are provided by the parameter \code{sarestimate}). Spatial autocorrelation of a species' presences is governed by the parameter \code{p.nb}, \code{sarestimate} and a list of neighbors for each region. } \usage{ regpop.sar(abmat, prab01=NULL, sarestimate=prab.sarestimate(abmat), p.nb=NULL, vector.species=prab01$regperspec, pdf.regions=prab01$specperreg/(sum(prab01$specperreg)), count=FALSE) } %- maybe also `usage' for other objects documented here. \arguments{ \item{abmat}{object of class \code{prab}, containing the abundance or presence/absence data.} \item{prab01}{presence-absence matrix of same dimensions than the abundance matrix of \code{prabobj}. This specifies the presences and absences on which the presence/absence step of abundance-based tests is based (see details). If \code{NULL} (which is usually the only reasonable choice), \code{prab01} is computed in order to indicate the nonzeroes of \code{prabobj$prab}.} \item{sarestimate}{Estimator of the parameters of a simultaneous autoregression model corresponding to the null model for abundance data from Hausdorf and Hennig (2007) as generated by \code{prab.sarestimate}. This requires package \code{spdep}. If \code{sarestimate$sar=FALSE}, spatial structure is ignored for generating the abundance values.} \item{p.nb}{numeric between 0 and 1. The probability that a new region is drawn from the non-neighborhood of the previous regions belonging to a species under generation. If \code{NULL}, the spatial structure of the regions is ignored. Note that for a given presence-absence matrix, this parameter can be estimated by \code{autoconst} (called \code{pd} there).} \item{vector.species}{vector of integers. \code{vector.species} gives the sizes (i.e., numbers of regions) of the species to generate..} \item{pdf.regions}{numerical vector of length \code{n.species}. The entries must sum up to 1 and give probabilities for the regions to be drawn during the generation of a species. These probabilities are used conditional on the new region being a neighbor or a non-neighbor of the previous regions of the species, see \code{p.nb}.} \item{count}{logical. If \code{TRUE}, the number of the currently generated species is printed.} } \value{ A matrix of abundance values, rows are regions, columns are species. } \references{ Hausdorf, B. and Hennig, C. (2007) Null model tests of clustering of species, negative co-occurrence patterns and nestedness in meta-communities. \emph{Oikos} 116, 818-828. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{autoconst}} estimates \code{p.nb} from matrices of class \code{prab}. These are generated by \code{\link{prabinit}}. \code{\link{abundtest}} uses \code{regpop.sar} as a null model for tests of clustering. \code{\link{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)) } \keyword{spatial}% at least one, from doc/KEYWORDS prabclus/man/nastats.Rd0000644000176200001440000000146513473260375014624 0ustar liggesusers\name{nastats} \alias{nastats} %- Also NEED an `\alias' for EACH other topic documented here. \title{Missing values statistics for matrix} \description{ Computes column-wise and row-wise numbers of missing values. } \usage{ nastats(amatrix, nastr="--") } %- maybe also `usage' for other objects documented here. \arguments{ \item{amatrix}{(any) matrix.} \item{nastr}{missing value indicator.} } \value{ A list with components \item{narow}{vector of row-wise numbers of mixxing values.} \item{nacol}{vector of column-wise numbers of mixxing values.} } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{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) } \keyword{manip}% at least one, from doc/KEYWORDS prabclus/man/piecewiselin.Rd0000644000176200001440000000201713473260375015621 0ustar liggesusers\name{piecewiselin} \alias{piecewiselin} %- Also NEED an `\alias' for EACH other topic documented here. \title{Piecewise linear transformation for distance matrices} \description{ Piecewise linear transformation for distance matrices, utility function for \code{geco}. } \usage{ piecewiselin(distmatrix, maxdist=0.1*max(distmatrix)) } %- maybe also `usage' for other objects documented here. \arguments{ \item{distmatrix}{symmetric (non-negative) distance matrix.} \item{maxdist}{non-negative numeric. Larger distances are transformed to constant 1.} } \details{ Transforms large distances to 1, 0 to 0 and continuously linear between 0 and \code{maxdist}. } \value{ A symmetrical matrix. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{geco}} } \examples{ options(digits=4) data(waterdist) piecewiselin(waterdist) } \keyword{cluster}% at least one, from doc/KEYWORDS \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/incmatrix.Rd0000644000176200001440000000241413473260375015140 0ustar liggesusers\name{incmatrix} \alias{incmatrix} %- Also NEED an `\alias' for EACH other topic documented here. \title{Nestedness matrix} \description{ Computes species*species nestedness matrix and number of nestings (inclusions) from regions*species presence-absence matrix. } \usage{ incmatrix(regmat) } %- maybe also `usage' for other objects documented here. \arguments{ \item{regmat}{0-1-matrix. Columns are species, rows are regions.} } \value{ A list with components \item{m}{0-1-matrix. \code{m[i,j]=1} means that the occupied region of species j is a subset (not equal) of the region of species i.} \item{ninc}{integer. Number of strict inclusions.} \item{neq}{integer. Number of region equalities between species (not including equality between species i and i).} } \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. \emph{Oecologia} 135, 102-109. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{prabtest}} } \examples{ data(kykladspecreg) incmatrix(t(kykladspecreg))$ninc } \keyword{spatial}% at least one, from doc/KEYWORDS \keyword{array} prabclus/man/communitydist.Rd0000644000176200001440000001727213604657622016063 0ustar liggesusers\name{communitydist} \alias{communitydist} %- Also NEED an `\alias' for EACH other topic documented here. \title{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,...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{alleleobj}{if \code{diploid=TRUE}, an object of class \code{alleleobject} as produced by function \code{\link[prabclus]{alleleinit}}. This has the required information on the individuals that are grouped into communities. In case \code{diploid=FALSE}, a list that needs to have components \code{n.variables} (number of loci), \code{alevels} (vector of allele names, see \code{\link{alleleinit}}) and \code{charmatrix} (matrix of characters with one row for every individual and one column for every locus giving the alleles; see examples below for how this can be constructed for a \code{prabobject} with presence-absence data).} \item{comvector}{either a vector of integers indicating to which community an individual belongs (these need to be numbered from 1 to a maximum number without interruption), or \code{"auto"}, which indicates that communities are automatically generated by the \code{\link{communities}}-function.} \item{distance}{one of \code{"chord"}, \code{"phipt"}, \code{"shared.average"}, \code{"shared.chakraborty"}, \code{"shared.problist"}. See Details.} \item{compute.geodist}{logical, indicating whether geographical distances between communities should be generated.} \item{out.dist}{logical, indicating whether \code{dist}-objects are given out or rather distance matrices.} \item{grouping}{something that can be coerced into a factor, for passing on to \code{\link{communities}} in case that \code{comvector=="auto"}. This implies that individuals in different groups indicated by \code{grouping} cannot be together in the same community. Furthermore (also if \code{comvector} is something else), a vector of groups of communities will be computed, see output component \code{comgroup}. In any case individuals in different groups are not allowed to be in the same community.} \item{geodist}{matrix or \code{dist}-object providing geographical distances between individuals. Required if \code{compute.geodist==TRUE} or \code{comvector=="auto"}.} \item{diploid}{logical, indicating whether loci are diploid, see \code{alleleobj}.} \item{phiptna}{if \code{distance="phipt"}, value to be given out as phiPT-distance in case that the original definition amounts to 0/0 (particularly if communities have just one member).} \item{...}{optional arguments to be passed on to \code{\link{communities}}.} } \details{ All genetic distances between communities are based on the information given in \code{alleleobj}; either on the alleles directly or on a genetic distance (\code{distmat}-component, see \code{\link{alleleinit}}). The possible genetic distance measures between communities are as follows: \itemize{ \item \code{"chord"}: chord-distance (Cavalli-Sforza and Edwards, 1967) \item \code{"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). \item \code{"shared.average"}: average of between-community genetic distances. \item \code{"shared.chakraborty"}: between-community shared allele distance according to Chakraborty and Jin (1993). \item \code{"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 \item{comvector}{integer vector of length of the number of individuals, indicating their community membership.} \item{dist}{genetic distances between communities. Parameter \code{out.dist} determines whether this is a \code{dist}-object or a matrix.} \item{cgeodist}{if \code{compute.geodist}, geographical distance between communities defined as average distance of all pairs of individuals belonging to different ones of the two communities between which the distance is computed. Parameter \code{out.dist} determines whether this is a \code{dist}-object or a matrix.} \item{comgroup}{vector of length of the number of communities. If \code{grouping} was provided, this is a vector giving the group memberships of all communities, otherwise it is a vector of 1s.} } \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. \emph{Nature} 368, 455-457. Cavalli-Sforza, L. L. and Edwards, A. W. F. (1967) Phylogenetic Analysis - Models and Estimation Procedures. \emph{The American Journal of Human Genetics} 19, 233-257. Chakraborty, R. and Jin, L. (1993) Determination of relatedness between individuals using DNA fingerprinting. \emph{Human Biology} 65, 875-895. Meirmans, P. G. (2006) Using the AMOVA framework to estimate a standardized genetic differentiation measure. \emph{Evolution} 60, 2399-2402. Peakall, R. and Smouse P.E. (2012) GenAlEx Tutorial 2. \url{https://biology-assets.anu.edu.au/GenAlEx/Tutorials.html} } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{communities}}; refer to \code{\link{phipt}} for computation of distances between specific pairs of communities. \code{\link{diploidcomlist}} produces relative frequencies for all alles of all loci in all communities (on which the chord- and the \code{"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) } \keyword{spatial}% __ONLY ONE__ keyword per line \keyword{multivariate}% __ONLY ONE__ keyword per line prabclus/man/phipt.Rd0000644000176200001440000001201013475070552014255 0ustar liggesusers\name{phipt} \alias{phipt} \alias{cfchord} \alias{shared.problist} \alias{diploidcomlist} %- Also NEED an `\alias' for EACH other topic documented here. \title{Distances between communities, auxiliary functions} \description{ Auxiliary functions for \code{\link{communitydist}}. \code{phipt} computes phiPT/phiST (Peakall and Smouse, 2012, Meirmans, 2006) between two communities. \code{cfchord} computes the chord-distance (Cavalli-Sforza and Edwards, 1967) between two lists or locus-wise relative allele frequencies. \code{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. \code{diploidcomlist} constructs the input lists for \code{cfchord} and \code{shared.problist} from an \code{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{alleleobj}{if \code{diploid=TRUE}, an object of class \code{alleleobject} as produced by function \code{\link[prabclus]{alleleinit}}. This has the required information on the individuals that are grouped into communities. In case \code{diploid=FALSE}, a list that needs to have components \code{n.variables} (number of loci), \code{alevels} (vector of allele names, see \code{\link{alleleinit}}) and \code{charmatrix} (matrix of characters with one row for every individual and one column for every locus giving the alleles; see examples below for how this can be constructed for a \code{prabobject} with presence-absence data).} \item{comvector}{vector of integers indicating to which community an individual belongs.} \item{i}{integer. Number of community.} \item{j}{integer. Number of community. The phiPT-distance is computed between the communities numbered \code{i} and \code{j}} \item{p1}{list. Every list entry refers to a locus and is a vector of relative frequencies of the alleles present in that locus in a community.} \item{p2}{list. Every list entry refers to a locus and is a vector of relative frequencies of the alleles present in that locus in a community. The chord or shared allele distance is computed between the communities encoded by \code{p1} and \code{p2}.} \item{diploid}{logical, indicating whether loci are diploid, see \code{alleleobj}.} } \value{ \code{cfchord} gives out the value of the chord distance. \code{shared.problist} gives out the distance value. \code{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 \code{phipt} gives out a list with components \code{phipt, vap, n0, sst, ssg, msa, msw}. These refer to the notation on p.2.12 and 2.15 of Peakall and Smouse (2012). \item{phipt}{value of phiPT.} \item{vap}{variance among (between) populations (communities).} \item{n0}{standardisation factor N0, see p.2.12 of Peakall and Smouse (2012).} \item{sst}{total distances sum of squares.} \item{ssg}{vector with two non-\code{NA} entriesm, within community sums of squares for communities \code{i} and \code{j}.} \item{msa}{mean square between communities.} \item{msw}{mean square within communities.} } \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. \emph{Nature} 368, 455-457. Cavalli-Sforza, L. L. and Edwards, A. W. F. (1967) Phylogenetic Analysis - Models and Estimation Procedures. \emph{The American Journal of Human Genetics} 19, 233-257. Meirmans, P. G. (2006) Using the AMOVA framework to estimate a standardized genetic differentiation measure. \emph{Evolution} 60, 2399-2402. Peakall, R. and Smouse P.E. (2012) GenAlEx Tutorial 2. \url{https://biology-assets.anu.edu.au/GenAlEx/Tutorials.html} } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{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]]) } \keyword{spatial}% __ONLY ONE__ keyword per line \keyword{multivariate}% __ONLY ONE__ keyword per line prabclus/man/nb.Rd0000644000176200001440000000142213473260375013537 0ustar liggesusers\name{nb} \alias{nb} % \non_function{} \title{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 \code{numeric(0)} in case of no neighbors) indicating the neighboring islands. } \details{ Reads from example data file \code{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. \emph{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))) } \keyword{datasets} prabclus/man/build.ext.nblist.Rd0000644000176200001440000000251013473260375016327 0ustar liggesusers\name{build.ext.nblist} \alias{build.ext.nblist} %- Also NEED an `\alias' for EACH other topic documented here. \title{Internal: generates neighborhood list for diploid loci} \description{ This is for use in \code{\link{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)) } %- maybe also `usage' for other objects documented here. \arguments{ \item{neighbors}{list of integer vectors, where each vector contains the neighbors of an individual.} \item{n.individuals}{integer. Number of individuals.} } \value{ list with \code{2*n.inidividuals} vectors of integers as described above. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{\code{\link{alleleinit}}} \examples{ data(veronica) vnb <- coord2dist(coordmatrix=veronica.coord[1:20,], cut=20, file.format="decimal2",neighbors=TRUE) build.ext.nblist(vnb$nblist) } \keyword{cluster}% at least one, from doc/KEYWORDS prabclus/man/coord2dist.Rd0000644000176200001440000000704114515570774015224 0ustar liggesusers\name{coord2dist} \alias{coord2dist} %- Also NEED an `\alias' for EACH other topic documented here. \title{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{file}{string. A filename for the coordinate file. The file should have 2, 4 or 6 numeric columns and one row for each location. See \code{file.format}. One of \code{file} and \code{coordmatrix} needs to be specified (if \code{coordmatrix} is not specified, coordinates are read from \code{file}).} \item{coordmatrix}{something that can be coerced into a matrix with 2, 4 or 6 columns. Matrix of coordinates, one row for each location. See \code{file.format}. One of \code{file} and \code{coordmatrix} needs to be specified.} \item{cut}{numeric. Only active if \code{neighbors==TRUE}; see \code{neighbors}.} \item{file.format}{one of \code{"degminsec"}, \code{"decimal2"} or \code{"decimal4"}. The format of the required file or \code{coordmatrix} consists of the following columns: \describe{ \item{"degminsec"}{6 columns; the first three give degrees, minutes and seconds for latitude, columns 4-6 the same for longitude. Values in column 1 and 4 can be positive or negative (negative means "South", "West", respectively). Values in the other columns should be non-negative.} \item{"decimal2"}{2 columns; the first one gives latitude, the second one longitude in proper decimal notation. Values can be positive or negative (negative means "South", "West", respectively).} \item{"decimal4"}{4 columns; the first two give latitude, no. 3 and 4 give longitude. Values in column 1 and 3 can be positive or negative (negative means "South", "West", respectively). The give integer degrees. Values in the other columns should be non-negative. They give percentages (\code{<=100}).} }} \item{output.dist}{logical. If \code{TRUE}, the resulting distance matrix is given out as a \code{\link{dist}} object.} \item{radius}{numeric. Radius of the earth in km used in computation (the default is the equatorial radius but this is not the uniquely possible choice).} \item{fp}{flattening of the earth; the default is from WGS-84.} \item{neighbors}{logical. If \code{TRUE}, a neighborhood list is also computed, listing for every location all locations with distance \code{<=cut} as neighbors.} } \value{ If \code{neighbors==TRUE}, a list with components \item{distmatrix}{distance matrix between locations. See \code{output.dist} above. This is in km by default; the measurement unit is determined by the value used for \code{radius}.} \item{nblist}{list with a vector for every location containing the numbers of its neighbors, see \code{neighbors}.} If \code{neighbors==FALSE}, only the distance matrix. } \references{ German Wikipedia from 29 August 2010: \url{https://de.wikipedia.org/wiki/Orthodrome} } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{geo2neighbor}} } \examples{ options(digits=4) data(veronica) coord2dist(coordmatrix=veronica.coord[1:20,], cut=20, file.format="decimal2",neighbors=TRUE) } \keyword{math}% __ONLY ONE__ keyword per line prabclus/man/siskiyou.Rd0000644000176200001440000000236313473260375015024 0ustar liggesusers\name{siskiyou} \alias{siskiyou} \alias{siskiyou.nb} \alias{siskiyou.groups} \docType{data} % \non_function{} \title{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: \describe{ \item{siskiyou}{numeric matrix giving the 144*6 abundance values.} \item{siskiyou.nb}{neighborhood list for the 6 regions.} \item{siskiyou.groups}{integer vector of length 144, giving group memberships for the 144 species.}} } \source{ Whittaker, R. H. 1960. Vegetation of the Siskiyou Mountains, Oregon and California. \emph{Ecol. Monogr}. 30: 279-338 (table 14). } \details{ Reads from example data files \code{LeiMik1.dat, LeiMik1NB.dat, LeiMik1G.dat}. } \examples{ data(siskiyou) } \keyword{datasets} prabclus/man/distratio.Rd0000644000176200001440000000255513473260375015152 0ustar liggesusers\name{distratio} \alias{distratio} %- Also NEED an `\alias' for EACH other topic documented here. \title{Distance ratio test statistics for distance based clustering} \description{ Calculates the ratio between the \code{prop} smallest and largest distances of a distance matrix. } \usage{ distratio(distmat, prop = 0.25) } %- maybe also `usage' for other objects documented here. \arguments{ \item{distmat}{symmetric distance matrix.} \item{prop}{numerical. Proportion between 0 and 1.} } \details{ Rounding is by \code{floor} for small and \code{ceiling} for large distances. } \value{ A list with components \item{dr}{ratio of \code{prop} smallest to \code{prop} largest distances.} \item{lowmean}{mean of \code{prop} smallest distances.} \item{himean}{mean of \code{prop} smallest distances.} \item{prop}{see above.} } \references{ Hennig, C. and Hausdorf, B. (2004) Distance-based parametric bootstrap tests for clustering of species ranges. \emph{Computational Statistics and Data Analysis} 45, 875-896. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{\code{\link{prabtest}}} \examples{ options(digits=4) data(kykladspecreg) j <- jaccard(t(kykladspecreg)) distratio(j) } \keyword{cluster}% at least one, from doc/KEYWORDS % \keyword{ ~kwd2 }% __ONLY ONE__ keyword per line prabclus/man/pop.sim.Rd0000644000176200001440000001000713473260375014524 0ustar liggesusers\name{pop.sim} \alias{pop.sim} %- Also NEED an `\alias' for EACH other topic documented here. \title{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 \code{\link{prabtest}}. The null model is defined by \code{\link{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{regmat}{0-1-matrix. Columns are species, rows are regions.} \item{neighbors}{A list with a component for every region. The components are vectors of integers indicating neighboring regions. A region without neighbors (e.g., an island) should be assigned a list \code{numeric(0)}.} \item{h0c}{numerical. Parameter \code{p.nb} for use in \code{randpop.nb}.} \item{times}{integer. Number of simulation runs.} \item{dist}{"kulczynski", "jaccard" or "geco", see \code{kulczynski}, \code{geco} and \code{jaccard}.} \item{teststat}{"isovertice", "lcomponent", "distratio", "nn" or "inclusions". See the corresponding functions, \code{homogen.test} for "isovertice", \code{incmatrix} for "inclusions").} \item{testc}{numerical. Tuning constant for the test statistics.} \item{geodist}{matrix of non-negative reals. Geographical distances between regions. Only used if \code{dist="geco"}.} \item{gtf}{tuning constant for geco-distance if \code{dist="geco"}, see \code{"geco"}.} \item{n.species}{integer. Number of species.} \item{specperreg}{vector of integers. Numbers of species per region (is calculated from the data by default).} \item{regperspec}{vector of integers. Number of regions per species (is calculated from the data by default).} \item{species.fixed}{logical. If \code{TRUE}, the sizes of the species are taken directly from \code{regmat}. Otherwise, they are drawn by random from the empirical distribution of the values from \code{regmat}.} \item{pdfnb}{logical. Probability correction in \code{randpop.nb}.} \item{ignore.richness}{logical. If \code{TRUE}, there is no assumption of species richnesses to differ between regions in the null model. Regionwise probabilities don't differ in the generation of null data.} } \value{ List with components \item{results}{vector of teststatistic values for the simulated matrices.} \item{p.above}{p-value if large test statistic leads to rejection.} \item{p.below}{p-value if small test statistic leads to rejection.} \item{datac}{test statistic value for the original data.} \item{testc}{see above.} } \references{ Hennig, C. and Hausdorf, B. (2004) Distance-based parametric bootstrap tests for clustering of species ranges. \emph{Computational Statistics and Data Analysis} 45, 875-896. \url{http://stat.ethz.ch/Research-Reports/110.html}. Hausdorf, B. and Hennig, C. (2003) Biotic Element Analysis in Biogeography. \emph{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. \emph{Oecologia} 135, 102-109. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{prabtest}}, \code{\link{randpop.nb}}, \code{\link{jaccard}}, \code{\link{kulczynski}}, \code{\link{homogen.test}}, \code{\link{lcomponent}}, \code{\link{distratio}}, \code{\link{nn}}, \code{\link{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) } \keyword{cluster}% at least one, from doc/KEYWORDS \keyword{htest}% __ONLY ONE__ keyword per line prabclus/man/allele2zeroone.Rd0000644000176200001440000000162513473260375016067 0ustar liggesusers\name{allele2zeroone} \alias{allele2zeroone} %- Also NEED an `\alias' for EACH other topic documented here. \title{Converts alleleobject into binary matrix} \description{ Converts \code{\link{alleleobject}} with codominant markers into binary matrix with a column for each marker. } \usage{ allele2zeroone(alleleobject) } %- maybe also `usage' for other objects documented here. \arguments{ \item{alleleobject}{object of class \code{\link{alleleobject}} as generated by \code{\link{alleleinit}}.} } \value{ A 0-1-matrix with individuals as rows and markers (alleles) as columns. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \examples{ data(tetragonula) ta <- alleleconvert(strmatrix=tetragonula[21:50,]) tai <- alleleinit(allelematrix=ta) allele2zeroone(tai) } \keyword{manip}% at least one, from doc/KEYWORDS prabclus/man/autoconst.Rd0000644000176200001440000001156713473260375015172 0ustar liggesusers\name{autoconst} \alias{autoconst} \alias{autoreg} %- Also NEED an `\alias' for EACH other topic documented here. \title{Spatial autocorrelation parameter estimation} \description{ Monte Carlo estimation of the disjunction/spatial autocorrelation parameter \code{pd} for the simulation model used in \code{randpop.nb}, used for tests for clustering of presence-absence data. \code{autoconst} is the main function; \code{autoreg} performs the simulation and is executed within \code{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{object of class \code{prab} as generated by \code{prabinit}. Presence-absence data to be analyzed.} \item{prange}{numerical range vector, lower value not smaller than 0, larger value not larger than 1. Range where the parameter is to be found.} \item{twostep}{logical. If \code{TRUE}, a first estimation step is carried out in the whole \code{prange}, and then the final estimation is determined between the preliminary estimator \code{-5*step2} and \code{+5*step2}. Else, the first simulation determines the final estimator.} \item{step1}{numerical between 0 and 1. Interval length between subsequent choices of \code{pd} for the first simulation.} \item{step2}{numerical between 0 and 1. Interval length between subsequent choices of \code{pd} for the second simulation in case of \code{twostep=TRUE}.} \item{plot}{logical. If \code{TRUE}, a scatterplot of \code{pd}-values against resulting \code{ejprob} values (see below), with regression line and data value of \code{ejprob} is shown.} \item{nperp}{integer. Number of simulations per \code{pd}-value.} \item{ejprob}{numerical between 0 and 1. Observed disjunction probability for data \code{x}; if not specified in advance, it will be computed by \code{autoconst}.} \item{species.fixed}{logical. If \code{TRUE}, sizes of generated species match the species sizes in \code{x}, else they are generated from the empirical distribution of species sizes in \code{x}.} \item{probs}{vector of numericals between 0 and 1. \code{pd} values for the simulation.} \item{pdfnb}{logical. If \code{TRUE}, the probabilities of the regions are modified according to the number of neighboring regions in \code{randpop.nb}, see Hennig and Hausdorf (2002), p. 5.} \item{ignore.richness}{logical. If \code{TRUE}, there is no assumption of species richnesses to differ between regions in the null model. Regionwise probabilities don't differ in the generation of null data.} } \details{ The spatial autocorrelation parameter \code{pd} of the model for the generation of presence-absence data sets used by \code{randpop.nb} can be estimated by use of the observed disjuction probability \code{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 \code{x} and different \code{pd}-values, governed by \code{prange, step1, step2} and \code{nperp}. \code{ejprob} is then calculated for all simulated populations. A linear regression of \code{ejprob} on \code{pd} is performed and the estimator of \code{pd} is determined by computing the inverse of the regression function for the \code{ejprob}-value of \code{x}. } \value{ \code{autoconst} produces the same list as \code{autoreg} with additional component \code{ejprob}. The components are \item{pd}{(eventually) estimated parameter \code{pd}.} \item{coef}{(eventually) estimated regression coefficients.} \item{ejprob}{see above.} } \references{ Hausdorf, B. and Hennig, C. (2003) Biotic Element Analysis in Biogeography. To appear in \emph{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. \emph{Oecologia} 135, 102-109. Hennig, C. and Hausdorf, B. (2004) Distance-based parametric bootstrap tests for clustering of species ranges. \emph{Computational Statistics and Data Analysis} 45, 875-896. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{randpop.nb}}, \code{\link{prabinit}}, \code{\link{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) } \keyword{spatial}% at least one, from doc/KEYWORDS prabclus/man/specgroups.Rd0000644000176200001440000000304713473260375015337 0ustar liggesusers\name{specgroups} \alias{specgroups} %- Also NEED an `\alias' for EACH other topic documented here. \title{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{distmat}{dissimilarity matrix or \code{dist}-object.} \item{groupvector}{integer vector. For every row of \code{distmat}, a number indicating the group membership.} \item{groupinfo}{list with components \code{lg} (levels of \code{groupvector}), \code{ng} (number of groups), \code{nsg} (vector of group sizes).} } \value{ A list with parameters \item{overall}{overall average within-groups dissimilarity.} \item{gr}{vector of group-wise average within-group dissimilarities (this will be \code{NaN} if the group size is only 1).} } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{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$distmat,groupvector,groupinfo) } \keyword{cluster}% at least one, from doc/KEYWORDS prabclus/man/abundtest.Rd0000644000176200001440000002604713473260375015143 0ustar liggesusers\name{abundtest} \alias{abundtest} %- Also NEED an `\alias' for EACH other topic documented here. \title{Parametric bootstrap test for clustering in abundance matrices} \description{ Parametric bootstrap test of a null model of i.i.d., but spatially autocorrelated species against clustering of the species' population patterns. Note that most relevant functionality of \code{prabtest} (except of the use of the geco distance) is also included in \code{abundtest}, so that \code{abundtest} can also be used on binary presence-absence data. In spite of the lots of parameters, a standard execution (for the default test statistics, see parameter \code{teststat} below) will be \cr \code{prabmatrix <- prabinit(file="path/abundmatrixfile", neighborhood="path/neighborhoodfile")}\cr \code{test <- abundtest(prabmatrix)}\cr \code{summary(test)}\cr \bold{Note:} Data formats are described on the \code{prabinit} help page. You may also consider the example datasets \code{kykladspecreg.dat} and \code{nb.dat}. Take care of the parameter \code{rows.are.species} of \code{prabinit}.} \usage{ abundtest(prabobj, teststat = "distratio", tuning = 0.25, times = 1000, p.nb = NULL, prange = c(0, 1), nperp = 4, step = 0.1, step2 = 0.01, twostep = TRUE, species.fixed=TRUE, prab01=NULL, groupvector=NULL, sarestimate=prab.sarestimate(prabobj), dist = prabobj$distance, n.species = prabobj$n.species) } %- maybe also `usage' for other objects documented here. \arguments{ \item{prabobj}{an object of class \code{prab} (presence-absence data), as generated by \code{prabinit}.} \item{teststat}{string, indicating the test statistics. \code{"isovertice"}: number of isolated vertices in the graph of \code{tuning} smallest distances between species. \code{"lcomponent"}: size of largest connectivity component in this graph. \code{"distratio"}: ratio between \code{tuning} smallest and largest distances. \code{"nn"}: average distance of species to \code{tuning}th nearest neighbor. \code{"inclusions"}: number of inclusions between areas of different species (tests for nestedness structure, not for clustering, and treats abundance matrices as presence-absence-data). \code{"mean"}: mean of the distances between species (this is a rough measure of species co-occurrence). \code{"groups"}: this requires a specification of a vector defining different groups of species via parameter \code{groupvector}. The test statistic is then the mean of the distances between species of the same group. This is computed over all species, but also for every single group of species. It also includes the \code{"mean"}-test, so that the number of tests carried out is number of species groups with more than one element plus two.} \item{tuning}{integer or (if \code{teststat="distratio"}) numerical between 0 and 1. Tuning constant for test statistics, see \code{teststat}.} \item{times}{integer. Number of simulation runs.} \item{p.nb}{numerical between 0 and 1. The probability that a new region is drawn from the non-neighborhood of the previous regions belonging to a species under generation. If \code{NULL} (the default), and \code{prabobj$spatial}, \code{prabtest} estimates this by function \code{autoconst}. Otherwise the next five parameters have no effect. If \code{NULL}, and \code{!prabobj$spatial}, spatial structure is ignored.} \item{prange}{numerical range vector, lower value not smaller than 0, larger value not larger than 1. Range where \code{pd} is to be found. Used by function \code{autoconst}.} \item{nperp}{integer. Number of simulations per \code{pd}-value. Used by function \code{autoconst}.} \item{step}{numerical between 0 and 1. Interval length between subsequent choices of \code{pd} for the first simulation. Used by function \code{autoconst}.} \item{step2}{numerical between 0 and 1. Interval length between subsequent choices of \code{pd} for the second simulation (see parameter \code{twostep}). Used by function \code{autoconst}.} \item{twostep}{logical. If \code{TRUE}, a first estimation step for \code{pd} is carried out in the whole \code{prange}, and then the final estimation is determined between the preliminary estimator \code{-5*step2} and \code{+5*step2}. Else, the first simulation determines the final estimator. Used by function \code{autoconst}.} \item{species.fixed}{logical. Indicates if the range sizes of the species are held fixed in the test simulation (\code{TRUE}) or generated from their empirical distribution in \code{x} (\code{FALSE}) for presence-absence data. See function \code{randpop.nb}. Use always \code{TRUE} for abundance data (not necessary if \code{teststat="inclusions"}).} \item{prab01}{\code{prabinit}-object based on presence-absence matrix of same dimensions than the abundance matrix of \code{prabobj}. This specifies the presences and absences on which the presence/absence step of abundance-based tests is based (see details). If \code{NULL} (which is usually the only reasonable choice), \code{prab01} is computed in order to indicate the nonzeroes of \code{prabobj$prab}.} \item{groupvector}{integer vector. For every species, a number indicating the species' group membership. Needed only if \code{teststat="groups"}.} \item{sarestimate}{Estimator of the parameters of a simultaneous autoregression model corresponding to the null model for abundance data from Hausdorf and Hennig (2007) as generated by \code{prab.sarestimate}. This requires package \code{spdep}. Note that by explicitly specifying \code{sarestimate=NULL} simulation of 0-1 matrices can be enforced.} \item{dist}{One of \code{"jaccard"}, \code{"kulczynski"}, \code{"qkulczynski"} or \code{"logkulczynski"} specifying the distance measure on which the test is based. By default, this is taken from \code{prabobj}.} \item{n.species}{number of species. By default this is taken from \code{prabobj}. This should normally not be changed.} } \details{ For presence-absence data, the routine is described in \code{\link{prabtest}}. For abundance data, the first step under the null model is to simulated presence-absence patterns as in \code{prabtest}. The second step is to fit a simultaneous autoregression (SAR) model (Ripley 1981, section 5.2) to the log-abundances, see \code{\link{prab.sarestimate}}. The simulation from the null model is implemented in \code{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 \code{"mean"} and \code{"groups"}, which can also be applied to binary data. If \code{p.nb=NA} was specified, a diagnostic plot for the estimation of \code{pd} is plotted by \code{autoconst}. For details see Hennig and Hausdorf (2004) and the help pages of the cited functions. } \value{ An object of class \code{prabtest}, which is a list with components \item{results}{vector of test statistic values for all simulated populations. For \code{teststat="groups"} a list with components \code{overall} (means of within group-distances), \code{mean} (means of all distances), \code{gr} (matrix with a row for every group, giving the groupwise within-group distance means).} \item{p.above}{p-value against an alternative that generates large values of the test statistic (usually reasonable for \code{teststat="inclusions"}, \code{"groups"}, \code{"mean"}).} \item{p.below}{p-value against an alternative that generates small values of the test statistic (usually reasonable for \code{"lcomponent"}, \code{"nn"}, \code{"distratio"}; for \code{"isovertice"}, the two-sided p may make sense which is twice the smaller one of \code{p.above} and \code{p.below}). } \item{datac}{test statistic value for the original data. (\code{specgroups}-output for \code{teststat="groups"}).} \item{tuning}{see above.} \item{distance}{\code{dist} above.} \item{teststat}{see above.} \item{pd}{\code{p.nb} above.} \item{abund}{\code{TRUE} if simultaneous autoregression has been used (i.e., a \code{sarestimate} has been supplied or computed).} \item{sarlambda}{Estimator of the autocorrelation parameter \code{lambda} (see \code{\link[spdep]{errorsarlm}}) defined so that the average weight of neighbors (see \code{\link[spdep]{nb2listw}}) is standardized to 1.} \item{sarestimate}{the output object of \code{prab.sarestimate}.} \item{groupinfo}{list containing information from \code{"groups"} tests, with components \code{lg} (levels of \code{groupvector}), \code{ng} (number of groups), \code{nsg} (vector of group sizes), \code{testm} (value of \code{"means"} test statistic for input \code{prabobj}), \code{pa} (group-wise \code{p.above}), \code{pb} (group-wise \code{p.below}), \code{pma} (\code{p.above} of \code{"means"} test), \code{pmb} (\code{p.below} of \code{"means"} test).} } \references{ Hausdorf, B. and Hennig, C. (2007) Null model tests of clustering of species, negative co-occurrence patterns and nestedness in meta-communities. \emph{Oikos} 116, 818-828. Hennig, C. and Hausdorf, B. (2004) Distance-based parametric bootstrap tests for clustering of species ranges. \emph{Computational Statistics and Data Analysis} 45, 875-896. \url{http://stat.ethz.ch/Research-Reports/110.html}. Ripley, B. D. (1981) \emph{Spatial Statistics}. Wiley. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{prabinit}} generates objects of class \code{prab}. \code{\link{autoconst}} estimates \code{pd} from such objects. \code{\link{prabtest}} (analogous function for presence-absence data). \code{\link{regpop.sar}} generates populations from the null model. \code{\link{prab.sarestimate}} (parameter estimators for simultaneous autoregression model). This calls \code{\link[spdep]{errorsarlm}} (original estimation function from package \code{spdep}). Some more information on the test statistics is given in \code{\link{homogen.test}}, \code{\link{lcomponent}}, \code{\link{distratio}}, \code{\link{nn}}, \code{\link{incmatrix}}. Summary and print methods: \code{\link{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) } \keyword{cluster}% at least one, from doc/KEYWORDS \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/prabclus-package.Rd0000644000176200001440000001363513475242467016361 0ustar liggesusers\name{prabclus-package} \alias{prabclus-package} %- Also NEED an `\alias' for EACH other topic documented here. \docType{package} \title{prabclus package overview} \description{ Here is a list of the main functions in package prabclus. Most other functions are auxiliary functions for these. } \section{Initialisation}{ \describe{ \item{prabinit}{Initialises presence/absence-, abundance- and multilocus data with dominant markers for use with most other key prabclus-functions.} \item{alleleinit}{Initialises multilocus data with codominant markers for use with key prabclus-functions.} \item{alleleconvert}{Generates the input format required by \code{\link{alleleinit}}.} }} \section{Tests for clustering and nestedness}{ \describe{ \item{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).} \item{abundtest}{ Computes the test introduced in Hausdorf and Hennig (2007) for abundance data.} \item{homogen.test}{A classical distance-based test for homogeneity going back to Erdos and Renyi (1960) and Ling (1973).} }} \section{Clustering}{ \describe{ \item{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 \code{mclust}, Fraley and Raftery (1998), on output of multidimensional scaling from distances as computed by \code{\link{prabinit}} or \code{\link{alleleinit}}. See also \code{\link{stressvals}} for help with choosing the number of MDS-dimensions.} \item{hprabclust}{An unpublished alternative to \code{\link{prabclust}} using hierarchical clustering methods.} \item{lociplots}{Visualisation of clusters of genetic markers vs. clusters of species.} \item{NNclean}{Nearest neighbor based classification of observations as noise/outliers according to Byers and Raftery (1998).} }} \section{Dissimilarity matrices}{ \describe{ \item{alleledist}{Shared allele distance (see the corresponding help pages for references).} \item{dicedist}{Dice distance.} \item{geco}{geco coefficient, taking geographical distance into account.} \item{jaccard}{Jaccard distance.} \item{kulczynski}{Kulczynski dissimilarity.} \item{qkulczynski}{Quantitative Kulczynski dissimilarity for abundance data.} }} \section{Communities}{ \describe{ \item{communities}{Constructs communities from geographical distances between individuals.} \item{communitydist}{chord-, phiPT- and various versions of the shared allele distance between communities.} }} \section{Tests for equality of dissimilarity-based regression}{ \describe{ \item{regeqdist}{Jackknife-based test for equality of two independent regressions between distances (Hausdorf and Hennig 2019).} \item{regdistbetween}{Jackknife-based test for equality of regression involving all distances and regression involving within-group distances only (Hausdorf and Hennig 2019).} \item{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).} }} \section{Small conversion functions}{ \describe{ \item{coord2dist}{Computes geographical distances from geographical coordinates.} \item{geo2neighbor}{Computes a neighborhood list from geographical distances.} \item{alleleconvert}{A somewhat restricted function for conversion of different file formats used for genetic data with codominant markers.} }} \section{Data sets}{ \code{\link{kykladspecreg}}, \code{\link{siskiyou}}, \code{\link{veronica}}, \code{\link{tetragonula}}. } \references{ Byers, S. and Raftery, A. E. (1998) Nearest-Neighbor Clutter Removal for Estimating Features in Spatial Point Processes, \emph{Journal of the American Statistical Association}, 93, 577-584. Erdos, P. and Renyi, A. (1960) On the evolution of random graphs. \emph{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. \emph{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. \emph{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. \emph{Oikos} 116, 818-828. Hausdorf, B. and Hennig, C. (2010) Species Delimitation Using Dominant and Codominant Multilocus Markers. \emph{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. \emph{Computational Statistics and Data Analysis} 45, 875-896. Hennig, C. and Hausdorf, B. (2006) A robust distance coefficient between distribution areas incorporating geographic distances. \emph{Systematic Biology} 55, 170-175. Ling, R. F. (1973) A probability theory of cluster analysis. \emph{Journal of the American Statistical Association} 68, 159-164. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en/} } prabclus/man/hprabclust.Rd0000644000176200001440000000765013473260375015320 0ustar liggesusers\name{hprabclust} \alias{hprabclust} \alias{print.comprabclust} %- Also NEED an `\alias' for EACH other topic documented here. \title{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 \code{prabclust}-solutions is more convincing due to higher flexibility of that method. However, \code{hprabclust} may be more stable sometimes. \bold{Note:} Data formats are described on the \code{prabinit} help page. You may also consider the example datasets \code{kykladspecreg.dat} and \code{nb.dat}. Take care of the parameter \code{rows.are.species} of \code{prabinit}. } \usage{ hprabclust(prabobj, cutdist=0.4, cutout=1, method="average", nnout=2, mdsplot=TRUE, mdsmethod="classical") \method{print}{comprabclust}(x, ...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{prabobj}{object of class \code{prab} as generated by \code{prabinit}. Presence-absence data to be analyzed.} \item{cutdist}{non-negative integer. Cutoff distance to determine the partition, see \code{cutree}.} \item{cutout}{non-negative integer. Points that have at most \code{nnout} distances smaller or equal than \code{cutout} are treated as noise.} \item{method}{string. Clustering method, see \code{hclust}.} \item{nnout}{non-negative integer. Members of clusters with less or equal than \code{nnout} points or that have less or equal than \code{nnout} neighbors closer than \code{cutout} are treated as noise.} \item{mdsplot}{logical. If \code{TRUE}, the cluster solution is plotted on the first two MDS dimensions, see \code{mdsmethod}.} \item{mdsmethod}{\code{"classical"}, \code{"kruskal"}, or \code{"sammon"}. The MDS method to transform the distances to data points. \code{"classical"} indicates metric MDS by function \code{cmdscale}, \code{"kruskal"} is non-metric MDS. Note that if \code{mdsmethod!="classical"} zero distances between different objects are replaced by the minimum of the nonzero distances divided by 10 (otherwise the MDS method would produce an error). Note that \code{mdsmethod} is ignored if \code{mdsplot=FALSE}.} \item{x}{\code{comprabclust}-object as generated by \code{hprabclus}.} \item{...}{necessary for print method.} } \value{ \code{hprabclust} generates an object of class \code{comprabclust}. This is a list with components \item{clustering}{vector of integers indicating the cluster memberships of the species (\code{cutout}-outliers are noise, but small clusters are allowed). Noise is coded as 0.} \item{rclustering}{vector of integers indicating the cluster memberships of the species, noise as described under \code{nnout}. Noise is coded as 0.} \item{cutdist}{see above.} \item{method}{see above.} \item{cutout}{see above.} \item{nnout}{see above.} \item{noisen}{number of points minus \code{cutout}-outliers.} \item{symbols}{vector of characters corresponding to \code{rclustering}, but estimated noise by \code{"N"}.} \item{points}{numerical matrix. MDS configuration (if \code{mdsplot=TRUE}).} \item{call}{function call.} } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{hclust}}, \code{\link{cutree}}, \code{\link{prabclust}}. } \examples{ data(kykladspecreg) data(nb) data(waterdist) x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb, geodist=waterdist, distance="geco") hprabclust(x,mdsplot=FALSE) } \keyword{cluster}% at least one, from doc/KEYWORDS \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/stressvals.Rd0000644000176200001440000000341613473260375015356 0ustar liggesusers\name{stressvals} \alias{stressvals} %- Also NEED an `\alias' for EACH other topic documented here. \title{Stress values for different dimensions of Kruskal's MDS} \description{ Computes Kruskal's nonmetric multidimensional scaling \code{\link{isoMDS}} on \code{\link{alleleobject}} or \code{\link{prab}}-objects for different output dimensions in order to compare stress values. } \usage{ stressvals(x,mdsdim=1:12,trace=FALSE) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{object of class \code{\link{alleleobject}} or \code{link{prab}}. generated by \code{\link{alleleinit}} or \code{\link{prabinit}}.} \item{mdsdim}{integer vector of MDS numbers of dimensions to be tried.} \item{trace}{logical. \code{trace}-argument for \code{\link{isoMDS}} (should trace information be printed during execution?).} } \details{ Note that zero distances between non-identical objects are replaced by the smallest nonzero distance divided by 10 to prevent \code{\link{isoMDS}} from producing an error. } \value{ A list with components \item{MDSstress}{vector of stress values.} \item{mdsout}{list of full outputs of \code{\link{isoMDS}}.} } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{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 } \keyword{multivariate}% at least one, from doc/KEYWORDS prabclus/man/regdist.Rd0000644000176200001440000000353713473553641014613 0ustar liggesusers\name{regdist} \alias{regdist} %- Also NEED an `\alias' for EACH other topic documented here. \title{Regression between subsets of dissimilarity matrices} \description{ Given two dissimilarity matrices \code{dmx} and \code{dmy} and an indicator vector \code{x}, this computes a standard least squares regression between the dissimilarity between objects indicated in \code{x}. } \usage{ regdist(x,dmx,dmy,xcenter=0,param) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{vector of logicals of length of the number of objects on which dissimilarities \code{dmx} and \code{dmy} are based.} \item{dmx}{dissimilarity matrix or object of class \code{\link{dist}}. Explanatory dissimilarities.} \item{dmy}{dissimilarity matrix or object of class \code{\link{dist}}. Response dissimilarities.} \item{xcenter}{numeric. Dissimilarities \code{dmx} are centered by this, i.e., this value is subtracted from the dissimilarities before regression.} \item{param}{1 or 2 or \code{NULL}. If 1 or 2, only the first or second parameter (intercept or slope) of the regression is given out.} } \value{ If \code{param=NULL}, the output object of \code{\link{lm}}. If \code{param=1} the intercept. If \code{param=2} the slope. } \references{ Hausdorf, B. and Hennig, C. (2019) Species delimitation and geography. Submitted. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \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) } \keyword{regression}% __ONLY ONE__ keyword per line \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/comp.test.Rd0000644000176200001440000000221313473260375015053 0ustar liggesusers\name{comp.test} \alias{comp.test} %- Also NEED an `\alias' for EACH other topic documented here. \title{Compare species clustering and species groups} \description{ Tests for independence between a clustering and another grouping of species. This is simply an interface to \code{chisq.test}. } \usage{ comp.test(cl,spg) } %- maybe also `usage' for other objects documented here. \arguments{ \item{cl}{a vector of integers. Clustering of species (may be taken from \code{prabclust}).} \item{spg}{a vector of integers of the same length, groups of species.} } \details{ \code{chisq.test} with simulated p-value is used. } \value{ Output of \code{chisq.test}. } \references{ Hausdorf, B. and Hennig, C. (2003) Biotic Element Analysis in Biogeography. \emph{Systematic Biology} 52, 717-723. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{chisq.test}}, \code{\link{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) } \keyword{htest}% at least one, from doc/KEYWORDS prabclus/man/regdistbetweenone.Rd0000644000176200001440000001522013475070704016653 0ustar liggesusers\name{regdistbetweenone} \alias{regdistbetweenone} %- Also NEED an `\alias' for EACH other topic documented here. \title{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{dmx}{dissimilarity matrix or object of class \code{dist}. Explanatory dissimilarities (often these will be proper distances, but more general dissimilarities that do not necessarily fulfill the triangle inequality can be used, same for \code{dmy}).} \item{dmy}{dissimilarity matrix or object of class \code{dist}. Response dissimilarities.} \item{grouping}{something that can be coerced into a factor, defining the grouping of objects represented by the dissimilarities \code{dmx} and \code{dmy} (i.e., if \code{grouping} has length n, \code{dmx} and \code{dmy} must be dissimilarities between \code{n} objects).} \item{groups}{vector of two levels. The two groups defining the regressions to be compared in the test. These can be factor levels, integer numbers, or strings, depending on the entries of \code{grouping}.} \item{rgroup}{one of the levels in \code{groups}, denoting the group of which within-group dissimilarities are considered.} } \details{ The null hypothesis that the regressions based on the distances within group \code{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 \code{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 \code{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 \code{regeqdist} to test whether a joint regression for the within-group distances of both groups makes sense. If this is not rejected, \code{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 \code{regeqdist}, \code{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, \code{regdistbetweenone} needs to be run twice using both groups as \code{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 \code{"regdistbetween"} with components \item{pval}{p-value.} \item{coeffdiff}{difference between regression fits (within-group together with between-groups distances minus within-group distances only) at \code{xcenterbetween}, see below.} \item{condition}{condition numbers of regressions, see \code{\link{kappa}}.} \item{lmfit}{list. Output objects of \code{\link{lm}} within the two groups.} \item{jr}{output object of \code{\link[bootstrap]{jackknife}} for difference between regression fitted values at \code{xcenterbetween}.} \item{xcenter}{mean of within-group distances for group \code{species} of explanatory variable, used for centering.} \item{xcenterbetween}{mean of between-groups distances of explanatory variable (after centering by \code{xcenter}); at this point regression fitted values are computed.} \item{tstat}{t-statistic.} \item{tdf}{degrees of freedom of t-statistic according to Welch-Sattertwaithe approximation.} \item{jackest}{jackknife-estimator of difference between regression fitted values at \code{xcenterbetween}.} \item{jackse}{jackknife-standard error for \code{jackest}.} \item{jackpseudo}{vector of jacknife pseudovalues on which the test is based.} \item{groups}{see above.} \item{species}{see above.} \item{testname}{title to be printed out when using \code{print.regdistbetween}.} } \references{ Hausdorf, B. and Hennig, C. (2019) Species delimitation and geography. Submitted. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{regeqdist}}, \code{\link{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) } \keyword{htest}% __ONLY ONE__ keyword per line \keyword{regression}% __ONLY ONE__ keyword per line \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/alleledist.Rd0000644000176200001440000000404713473260375015270 0ustar liggesusers\name{alleledist} \alias{alleledist} %- Also NEED an `\alias' for EACH other topic documented here. \title{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{allelelist}{a list of lists. In the "outer" list, there are \code{np} lists, one for each locus. In the "inner" list, for every individual there is a vector of two codes (typically characters, see \code{\link{alleleinit}}) for the two alleles in that locus. Such a list can be constructed by \code{\link{unbuild.charmatrix}} out of the \code{charmatrix} component of an output object of \code{\link{alleleinit}}.} \item{ni}{integer. Number of individuals.} \item{np}{integer. Number of loci.} \item{count}{logical. If \code{TRUE}, the number of the individual to be processed is printed.} } \value{ A symmetrical matrix of shared allele distances between individuals. } \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. \emph{Nature} 368, 455-457. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ %- \code{\link{chorddist}},\code{\link{neidist}}, \code{\link{alleleinit}}, \code{\link{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)) } \keyword{cluster}% at least one, from doc/KEYWORDS prabclus/man/dicedist.Rd0000644000176200001440000000244013473260375014731 0ustar liggesusers\name{dicedist} \alias{dicedist} %- Also NEED an `\alias' for EACH other topic documented here. \title{Dice distance matrix} \description{ Computes a distance derived from Dice's coincidence index between the columns of a 0-1-matrix. } \usage{ dicedist(regmat) } %- maybe also `usage' for other objects documented here. \arguments{ \item{regmat}{0-1-matrix. Columns are species, rows are regions.} } \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. } \references{ Shi, G. R. (1993) Multivariate data analysis in palaeoecology and palaeobiogeography - a review. \emph{Palaeogeography, Palaeoclimatology, Palaeoecology} 105, 199-234. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{kulczynski}},\code{\link{jaccard}} } \examples{ options(digits=4) data(kykladspecreg) dicedist(t(kykladspecreg)) } \keyword{cluster}% at least one, from doc/KEYWORDS \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/cluspop.nb.Rd0000644000176200001440000001055513473260375015232 0ustar liggesusers\name{cluspop.nb} \alias{cluspop.nb} %- Also NEED an `\alias' for EACH other topic documented here. \title{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 \code{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{neighbors}{A list with a component for every region. The components are vectors of integers indicating neighboring regions. A region without neighbors (e.g., an island) should be assigned a list \code{numeric(0)}.} \item{p.nb}{numerical between 0 and 1. The probability that a new region is drawn from the non-neighborhood of the previous regions belonging to a species under generation. Note that for a given presence-absence matrix, this parameter can be estimated by \code{autoconst} (called \code{pd} there).} \item{n.species}{integer. Number of species.} \item{clus.specs}{integer not larger than \code{n.species}. Number of species restricted to one of the two groups of regions defined by \code{reg.group} (called "clustered species" because this leads to more similar species ranges).} \item{reg.group}{vector of pairwise distinct integers not larger than \code{n. regions}. Defines a group of regions to which a part of the \code{clus.specs} clustered species is restricted (more or less, see \code{grouppf}). The other clustered species are restricted to the complementary regions.} \item{grouppf}{numerical. The probability of the region of a clustered species to belong to the corresponding group of regions is up-weighted by factor \code{grouppf} compared to the generation of "non-clustered" species.} \item{n.regions}{integer. Number of regions.} \item{vector.species}{vector of integers. The sizes (i.e., numbers of regions) of the species are generated randomly from the empirical distribution of \code{vector.species}.} \item{pdf.regions}{numerical vector of length \code{n.species}. The entries must sum up to 1 and give probabilities for the regions to be drawn during the generation of a species. These probabilities are used conditional on the new region being a neighbor or a non-neighbor of the previous regions of the species, see \code{p.nb}, modified by \code{grouppf} for the clustered species.} \item{count}{logical. If \code{TRUE}, the number of the currently generated species is printed.} \item{pdfnb}{logical. If \code{TRUE}, the probabilities of the regions are modified according to the number of neighboring regions by dividing them relative to the others by min(1,number of neighbors).} } \details{ The non-clustered species are generated as explained on the help page for \code{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 \code{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. } \references{ Hennig, C. and Hausdorf, B. (2004) Distance-based parametric bootstrap tests for clustering of species ranges. \emph{Computational Statistics and Data Analysis} 45, 875-896. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{randpop.nb}}, \code{\link{autoconst}} estimates \code{p.nb} from matrices of class \code{prab}. These are generated by \code{\link{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)) } \keyword{spatial}% at least one, from doc/KEYWORDS prabclus/man/kulczynski.Rd0000644000176200001440000000255713473260375015360 0ustar liggesusers\name{kulczynski} \alias{kulczynski} %- Also NEED an `\alias' for EACH other topic documented here. \title{Kulczynski distance matrix} \description{ Computes Kulczynski distances between the columns of a 0-1-matrix. } \usage{ kulczynski(regmat) } %- maybe also `usage' for other objects documented here. \arguments{ \item{regmat}{0-1-matrix. Columns are species, rows are regions.} } \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. } \references{ Shi, G. R. (1993) Multivariate data analysis in palaeoecology and palaeobiogeography - a review. \emph{Palaeogeography, Palaeoclimatology, Palaeoecology} 105, 199-234. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{jaccard}}, \code{\link{geco}},\code{\link{qkulczynski}} , \code{\link{dicedist}} } \examples{ options(digits=4) data(kykladspecreg) kulczynski(t(kykladspecreg)) } \keyword{cluster}% at least one, from doc/KEYWORDS \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/kykladspecreg.Rd0000644000176200001440000000164313473260375015775 0ustar liggesusers\name{kykladspecreg} \alias{kykladspecreg} % \non_function{} \title{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.} \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. \emph{Biological Journal of the Linnean Society} 84, 785-795. } \details{ Reads from example data file \code{kykladspecreg.dat}. } \seealso{ \code{\link{nb}} provides neighborhood information about the 34 islands. \code{\link{waterdist}} provides a geographical distance matrix between the islands. } \examples{ data(kykladspecreg) } \keyword{datasets} % \keyword{spatial} prabclus/man/nbtest.Rd0000644000176200001440000000173213473260375014443 0ustar liggesusers\name{nbtest} \alias{nbtest} %- Also NEED an `\alias' for EACH other topic documented here. \title{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)) } %- maybe also `usage' for other objects documented here. \arguments{ \item{nblist}{A list with a component for every region. The components are vectors of integers indicating neighboring regions. A region without neighbors (e.g., an island) should be assigned a vector \code{numeric(0)}.} \item{n.regions}{Number of regions.} } \value{ \code{invisible{TRUE}}. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{\code{\link{prabinit}}.} \examples{ data(nb) nbtest(nb) nb[[1]][1] <- 1 try(nbtest(nb)) } \keyword{spatial}% at least one, from doc/KEYWORDS prabclus/man/lociplots.Rd0000644000176200001440000000636313473260375015161 0ustar liggesusers\name{lociplots} \alias{lociplots} %- Also NEED an `\alias' for EACH other topic documented here. \title{Visualises clusters of markers vs. species} \description{ Given a clustering of individuals from \code{\link{prabclust}} (as generated in species delimitation) and a clustering of markers (for example dominant markers of genetic loci), \code{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{indclust}{\code{\link{prabclust}}-object. Clustering of individuals.} \item{locclust}{vector of integers. Clustering of markers/loci.} \item{locprab}{\code{\link{prab}}-object in which the markers are what the help page of \code{\link{prabinit}} refers to as "species" (i.e., reverse of what is used for species delimitation clustering; for data sets with codominant markers, such an object can be constructed by use of \code{\link{allele2zeroone}} before \code{\link{prabinit}}.)} \item{lcluster}{integer. Number of cluster in \code{locclust} for which plot and statistics are produced.} \item{symbols}{vector of plot symbols. If \code{NULL}, \code{indclust$symbols} is used.} \item{brightest.grey}{numeric between 0 and 1. Brightest grey value used in plot for individuals with smallest marker percentage, see details.} \item{darkest.grey}{numeric between 0 and 1. Darkest grey value used in plot for individuals with highest marker percentage, see details.} \item{mdsdim}{vector of two integers. The two MDS variables taken from \code{indclust} used for visualisation.} } \details{ Plot and statistics are based on the individual marker percentage, which is the percentage of markers present in an individual of the markers belonging to cluster no. \code{lcluster}. In the plot, the grey value visualises the marker percentage. } \value{ list with components \item{locfreq}{vector of individual marker percentages.} \item{locfreqmin}{vector of minimum individual marker precentages for each cluster in \code{indclust}-clustering (the first value refers to the "noise component", if present).} \item{locfreqmax}{vector of maximum individual marker precentages for each cluster in \code{indclust}-clustering (the first value refers to the "noise component", if present).} \item{locfreqmean}{vector of average individual marker precentages for each cluster in \code{indclust}-clustering (the first value refers to the "noise component", if present).} } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{\code{\link{prabclust}}} \examples{ \donttest{ options(digits=4) data(veronica) vei <- prabinit(prabmatrix=veronica[1:50,],distance="jaccard") ppv <- prabclust(vei) veloci <- prabinit(prabmatrix=veronica[1:50,],rows.are.species=FALSE) velociclust <- prabclust(veloci,nnk=0) lociplots(ppv,velociclust$clustering,veloci,lcluster=3) } } \keyword{cluster}% at least one, from doc/KEYWORDS prabclus/man/concomp.Rd0000644000176200001440000000301213473260375014573 0ustar liggesusers\name{con.comp} \alias{con.comp} %- Also NEED an `\alias' for EACH other topic documented here. \title{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{comat}{a symmetric logical or 0-1 matrix, where \code{comat[i,j]=TRUE} means that there is an edge between vertices \code{i} and \code{j}. The diagonal is ignored.} } \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. } \references{ Cormen, T. H., Leiserson, C. E. and Rivest, R. L. (1990), \emph{Introduction to Algorithms}, Cambridge: MIT Press. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{hclust}}, \code{\link{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). } \keyword{array}% at least one, from doc/KEYWORDS \keyword{cluster}% __ONLY ONE__ keyword per line prabclus/man/jaccard.Rd0000644000176200001440000000237413473260375014536 0ustar liggesusers\name{jaccard} \alias{jaccard} %- Also NEED an `\alias' for EACH other topic documented here. \title{Jaccard distance matrix} \description{ Computes Jaccard distances between the columns of a 0-1-matrix. } \usage{ jaccard(regmat) } %- maybe also `usage' for other objects documented here. \arguments{ \item{regmat}{0-1-matrix. Columns are species, rows are regions.} } \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. } \references{ Shi, G. R. (1993) Multivariate data analysis in palaeoecology and palaeobiogeography - a review. \emph{Palaeogeography, Palaeoclimatology, Palaeoecology} 105, 199-234. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{kulczynski}}, \code{\link{dicedist}} } \examples{ options(digits=4) data(kykladspecreg) jaccard(t(kykladspecreg)) } \keyword{cluster}% at least one, from doc/KEYWORDS \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/crmatrix.Rd0000644000176200001440000000245613473260375015001 0ustar liggesusers\name{crmatrix} \alias{crmatrix} %- Also NEED an `\alias' for EACH other topic documented here. \title{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{object of class \code{prab} as generated by \code{prabinit}. Presence-absence data to be analyzed.} \item{xc}{object of class \code{prabclust} or \code{comprabclust} as generated by \code{prabclust} or \code{hprabclust}. The clustering.} \item{percentages}{logical. If \code{TRUE}, the output matrix will give the proportion of species from a certain region in the cluster.} } \value{ A clusters time regions matrix as explained above. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en} } \examples{ \donttest{ 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) } } \keyword{spatial}% at least one, from doc/KEYWORDS \keyword{cluster}% __ONLY ONE__ keyword per line prabclus/man/prab.sarestimate.Rd0000644000176200001440000000666713473260375016424 0ustar liggesusers\name{prab.sarestimate} \alias{prab.sarestimate} %- Also NEED an `\alias' for EACH other topic documented here. \title{Estimates SAR model from log-abundance matrix of prab-object.} \description{ This is either an interface for the function \code{\link[spdep]{errorsarlm}} for abundance data stored in an object of class \code{\link{prab}} implemented for use in \code{\link{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{abmat}{object of class \code{prab}.} \item{prab01}{presence-absence matrix of same dimensions than the abundance matrix of \code{prabobj}. This specifies the presences and absences on which the presence/absence step of abundance-based tests is based (see details). If \code{NULL} (which is usually the only reasonable choice), \code{prab01} is computed in order to indicate the nonzeroes of \code{prabobj$prab}.} \item{sarmethod}{this is passed on as parameter \code{method} to \code{\link[spdep]{errorsarlm}} and documented there. We don't have experience with any other choice than \code{"eigen"}.} \item{weightstyle}{can take values "W", "B", "C", "U", and "S" though tests suggest that "C" should be chosen. See \code{\link[spdep]{nb2listw}}.} \item{quiet}{this is passed on as parameter \code{quiet} to \code{\link[spdep]{errorsarlm}} and documented there.} \item{sar}{logical. If \code{TRUE}, a simultaneous autoregression model is fitted by calling \code{\link[spdep]{errorsarlm}}. If \code{FALSE}, a two-way additive unreplicated linear model for log-abundances on factors species and region is computed by \code{\link[stats]{lm}}, ignoring the spatial arrangement of the regions.} \item{add.lmobject}{logical. If \code{TRUE}, the whole output object of \code{\link[spdep]{errorsarlm}} (or \code{lm}) is given out.} } \value{ A list with the following components: \item{sar}{see above.} \item{intercept}{numeric. Estimator of the intercept.} \item{sigma}{numeric. Estimator of error standard deviation.} \item{regeffects}{numeric vector. Estimator for region effects.} \item{speceffects}{numeric vector. Estimator for species effects.} \item{lamda}{numeric. Governs the degree of spatial autocorrelation. See \code{\link[spdep]{errorsarlm}}.} \item{size}{integer. Length of neighborhood list generated by \code{\link[spdep]{nb2listw}} used by \code{\link[spdep]{errorsarlm}}.} \item{nbweight}{numeric. Average weight of neighbors.} \item{lmobject}{if \code{add.lmobject=TRUE}, output object of either \code{\link[stats]{lm}} or \code{\link[spdep]{errorsarlm}}.} } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link[spdep]{errorsarlm}}, \code{\link{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) } \keyword{spatial}% at least one, from doc/KEYWORDS prabclus/man/qkulczynski.Rd0000644000176200001440000000307313473260375015533 0ustar liggesusers\name{qkulczynski} \alias{qkulczynski} %- Also NEED an `\alias' for EACH other topic documented here. \title{Quantitative Kulczynski distance matrix} \description{ Computes quantitative Kulczynski distances between the columns of an abundance matrix. } \usage{ qkulczynski(regmat, log.distance=FALSE) } %- maybe also `usage' for other objects documented here. \arguments{ \item{regmat}{(non-negative) abundance matrix. Columns are species, rows are regions.} \item{log.distance}{logical. If \code{TRUE}, 1 is added to the abundance matrix and then the logs of the values are taken in order to compute the distance.} } \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. } \references{ D. P. Faith, P. R. Minchin and L. Belbin (1987) Compositional dissimilarity as a robust measure of ecological distance. \emph{Vegetation} 69, 57-68. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{kulczynski}} } \examples{ options(digits=4) data(kykladspecreg) qkulczynski(t(kykladspecreg)) } \keyword{cluster}% at least one, from doc/KEYWORDS \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/prabtest.Rd0000644000176200001440000002270513473260375014773 0ustar liggesusers\name{prabtest} \alias{prabtest} \alias{summary.prabtest} \alias{print.summary.prabtest} %- Also NEED an `\alias' for EACH other topic documented here. \title{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 \code{teststat} below) will be \cr \code{prabmatrix <- prabinit(file="path/prabmatrixfile", neighborhood="path/neighborhoodfile")}\cr \code{test <- prabtest(prabmatrix)}\cr \code{summary(test)}\cr \bold{Note:} Data formats are described on the \code{prabinit} help page. You may also consider the example datasets \code{kykladspecreg.dat} and \code{nb.dat}. Take care of the parameter \code{rows.are.species} of \code{prabinit}.} \usage{ prabtest(prabobject, teststat = "distratio", tuning = switch(teststat, distratio = 0.25, lcomponent = floor(3 * ncol(prabobject$distmat)/4), isovertice = ncol(prabobject$distmat), nn = 4, NA), times = 1000, pd = NULL, prange = c(0, 1), nperp = 4, step = 0.1, step2=0.01, twostep = TRUE, sf.sim = FALSE, sf.const = sf.sim, pdfnb = FALSE, ignore.richness=FALSE) \method{summary}{prabtest}(object, above.p=object$teststat \%in\% c("groups","inclusions","mean"), group.outmean=FALSE,...) \method{print}{summary.prabtest}(x, ...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{prabobject}{an object of class \code{prab} (presence-absence data), as generated by \code{prabinit}.} \item{teststat}{string, indicating the test statistics. \code{"isovertice"}: number of isolated vertices in the graph of \code{tuning} smallest distances between species. \code{"lcomponent"}: size of largest connectivity component in this graph. \code{"distratio"}: ratio between \code{tuning} smallest and largest distances. \code{"nn"}: average distance of species to \code{tuning}th nearest neighbor. \code{"inclusions"}: number of inclusions between areas of different species (tests for nestedness structure, not for clustering).} \item{tuning}{integer or (if \code{teststat="distratio"}) numerical between 0 and 1. Tuning constant for test statistics, see \code{teststat}.} \item{times}{integer. Number of simulation runs.} \item{pd}{numerical between 0 and 1. The probability that a new region is drawn from the non-neighborhood of the previous regions belonging to a species under generation. If \code{NA} (the default), \code{prabtest} estimates this by function \code{autoconst}. Otherwise the next five parameters have no effect.} \item{prange}{numerical range vector, lower value not smaller than 0, larger value not larger than 1. Range where \code{pd} is to be found. Used by function \code{autoconst}.} \item{nperp}{integer. Number of simulations per \code{pd}-value. Used by function \code{autoconst}.} \item{step}{numerical between 0 and 1. Interval length between subsequent choices of \code{pd} for the first simulation. Used by function \code{autoconst}.} \item{step2}{numerical between 0 and 1. Interval length between subsequent choices of \code{pd} for the second simulation (see parameter \code{twostep}). Used by function \code{autoconst}.} \item{twostep}{logical. If \code{TRUE}, a first estimation step for \code{pd} is carried out in the whole \code{prange}, and then the final estimation is determined between the preliminary estimator \code{-5*step2} and \code{+5*step2}. Else, the first simulation determines the final estimator. Used by function \code{autoconst}.} \item{sf.sim}{logical. Indicates if the range sizes of the species are held fixed in the test simulation (\code{TRUE}) or generated from their empirical distribution in \code{x} (\code{FALSE}). See function \code{randpop.nb}.} \item{sf.const}{logical. Same as \code{sf.sim}, but for estimation of \code{pd} by \code{autoconst}.} \item{pdfnb}{logical. If \code{TRUE}, the probabilities of the regions are modified according to the number of neighboring regions in \code{randpop.nb}, see Hennig and Hausdorf (2002), p. 5. This is usually no improvement.} \item{ignore.richness}{logical. If \code{TRUE}, there is no assumption of species richnesses to differ between regions in the null model. Regionwise probabilities don't differ in the generation of null data.} \item{object}{object of class \code{prabtest}.} \item{above.p}{logical. \code{TRUE} means that for output from \code{abundtest} the p-value is \code{p.above}, otherwise \code{p.below}.} \item{group.outmean}{logical. If \code{TRUE} and \code{object$teststat="groups"}, statistics concerning the mean of all dissimilarities are given out by \code{print.summary.prabtest}.} \item{x}{object of class \code{summary.prabtest}.} \item{\dots}{no meaning, necessary for print and summary methods.} } \details{ From the original data, the distribution of the range sizes of the species, the autocorrelation parameter \code{pd} (estimated by \code{autoconst}) and the distribution on the regions induced by the relative species numbers are taken. With these parameters, \code{times} populations according to the null model implemented in \code{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\code{+1} divided by \code{times+1}. "More extreme" means smaller for \code{"lcomponent"}, \code{"distratio"}, \code{"nn"}, larger for \code{"inclusions"}, and twice the smaller number between the original statistic value and the "border", i.e., a two-sided test for \code{"isovertice"}. If \code{pd=NA} was specified, a diagnostic plot for the estimation of \code{pd} is plotted by \code{autoconst}. For details see Hennig and Hausdorf (2004) and the help pages of the cited functions. } \value{ \code{prabtest} prodices an object of class \code{prabtest}, which is a list with components \item{results}{vector of test statistic values for all simulated populations.} \item{datac}{test statistic value for the original data.'} \item{p.value}{the p-value.} \item{tuning}{see above.} \item{pd}{see above.} \item{reg}{regression coefficients from \code{autoconst}.} \item{teststat}{see above.} \item{distance}{the distance measure chosen, see \code{prabinit}.} \item{gtf}{the geco-distance tuning parameter (only informative if \code{distance="geco"}), see \code{prabinit}.} \item{times}{see above.} \item{pdfnb}{see above.} \item{ignore.richness}{see above.} \code{summary.prabtest} produces an object of class \code{summary.prabtest}, which is a list with components \item{rrange}{range of the simulation results (test statistic values) of \code{object}.} \item{rmean}{mean of the simulation results (test statistic values) of \code{object}.} \item{datac, p.value, pd, tuning, teststat, distance, times, pdfnb, abund, sarlambda}{directly taken from \code{object}, see \code{prabtest} and \code{abundtest}.} \item{groupinfo}{if \code{object$teststat="groups"}, components \code{rrangeg} (matrix of group-wise ranges of test statistic value), \code{rmeang} (vector of group-wise means of test statistic value), \code{rrangem} (range over simulations of overall mean of within-group dissimilarities), \code{rmeanm} (mean over simulations of overall mean of within-group dissimilarities) are added to the list \code{object$groupinfo}, and this is given out.} } \references{ Hennig, C. and Hausdorf, B. (2004) Distance-based parametric bootstrap tests for clustering of species ranges. \emph{Computational Statistics and Data Analysis} 45, 875-896. \url{http://stat.ethz.ch/Research-Reports/110.html}. Hausdorf, B. and Hennig, C. (2003) Biotic Element Analysis in Biogeography. \emph{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. \emph{Oecologia} 135, 102-109. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{prabinit}} generates objects of class \code{prab}. \code{\link{autoconst}} estimates \code{pd} from such objects. \code{\link{randpop.nb}} generates populations from the null model. An alternative model is given by \code{\link{cluspop.nb}}. Some more information on the test statistics is given in \code{\link{homogen.test}}, \code{\link{lcomponent}}, \code{\link{distratio}}, \code{\link{nn}}, \code{\link{incmatrix}}. The simulations are computed by \code{\link{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) } \keyword{cluster}% at least one, from doc/KEYWORDS \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/veronica.Rd0000644000176200001440000000245313473260375014753 0ustar liggesusers\name{veronica} \alias{veronica} \alias{veronica.coord} \docType{data} % \non_function{} \title{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: \describe{ \item{veronica}{0-1 matrix with 207 individuals (rows) and 583 AFLP bands (columns).} \item{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).} } } \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.\emph{Syst. Bot.} 29, 965-986. } \details{ Reads from example data files \code{MartinezOrtega04AFLP.dat, MartinezKoord.dat}. } \examples{ data(veronica) } \keyword{datasets} prabclus/man/regeqdist.Rd0000644000176200001440000001152013604660061015117 0ustar liggesusers\name{regeqdist} \alias{regeqdist} \alias{print.regeqdist} %- Also NEED an `\alias' for EACH other topic documented here. \title{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]) \method{print}{regeqdist}(x,...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{dmx}{dissimilarity matrix or object of class \code{dist}. Explanatory dissimilarities (often these will be proper distances, but more general dissimilarities that do not necessarily fulfill the triangle inequality can be used, same for \code{dmy}).} \item{dmy}{dissimilarity matrix or object of class \code{dist}. Response dissimilarities.} \item{grouping}{something that can be coerced into a factor, defining the grouping of objects represented by the dissimilarities \code{dmx} and \code{dmy} (i.e., if \code{grouping} has length n, \code{dmx} and \code{dmy} must be dissimilarities between \code{n} objects).} \item{groups}{Vector of two, indicating the two groups defining the regressions to be compared in the test. These can be factor levels, integer numbers, or strings, depending on the entries of \code{grouping}.} \item{x}{object of class \code{"regeqdist"}.} \item{...}{optional arguments for print method.} } \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 \code{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 \code{regeqdist} to test whether a joint regression for the within-group distances of both groups makes sense. If this is not rejected, \code{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, \code{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 \code{"regeqdist"} with components \item{pval}{p-values for intercept and slope.} \item{coeffdiff}{vector of differences between groups (first minus second) for intercept and slope.} \item{condition}{condition numbers of regressions, see \code{\link{kappa}}.} \item{lmfit}{list. Output objects of \code{\link{lm}} within the two groups.} \item{jr}{list of two lists of two; output object of \code{\link[bootstrap]{jackknife}} within the two groups for intercept and slope.} \item{xcenter}{mean of \code{dmx} within the two groups used for centering.} \item{tstat}{t-statistic.} \item{tdf}{vector of degrees of freedom of t-statistic according to Welch-Sattertwaithe approximation for intercept and slope.} \item{jackest}{jackknife-estimator of difference between regressions; vector with intercept and slope difference.} \item{jackse}{vector with jackknife-standard errors for \code{jackest}, intercept and slope.} \item{jackpseudo}{list of two lists of vectors; jacknife pseudovalues within both groups for intercept and slope estimators.} \item{groups}{see above.} } \references{ Hausdorf, B. and Hennig, C. (2019) Species delimitation and geography. Submitted. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{regdistbetween}}, \code{\link{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) } \keyword{htest}% __ONLY ONE__ keyword per line \keyword{regression}% __ONLY ONE__ keyword per line \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/build.nblist.Rd0000644000176200001440000000355013473260375015535 0ustar liggesusers\name{build.nblist} \alias{build.nblist} %- Also NEED an `\alias' for EACH other topic documented here. \title{Generate spatial weights from prabclus neighborhood list} \description{ This generates a \code{listw}-object as needed for estimation of a simultaneous autoregression model in package \code{spdep} from a neighborhood list of the type generated in \code{prabinit}. } \usage{ build.nblist(prabobj,prab01=NULL,style="C") } %- maybe also `usage' for other objects documented here. \arguments{ \item{prabobj}{object of class \code{prab}.} \item{prab01}{presence-absence matrix of same dimensions than the abundance matrix of \code{prabobj}. This specifies the presences and absences on which the presence/absence step of abundance-based tests is based (see details). If \code{NULL} (which is usually the only reasonable choice), \code{prab01} is computed in order to indicate the nonzeroes of \code{prabobj$prab}.} \item{style}{can take values "W", "B", "C", "U", and "S" though tests suggest that "C" should be chosen. See \code{\link[spdep]{nb2listw}}.} } \value{ A 'listw' object with the following members: \item{style}{see above.} \item{neighbours}{the neighbours list in \code{spdep}-format.} \item{weights}{the weights for the neighbours and chosen style, with attributes set to report the type of relationships (binary or general, if general the form of the glist argument), and style as above.} } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link[spdep]{nb2listw}} (which is called) } \examples{ # Not run; requires package spdep # data(siskiyou) # x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb, # distance="logkulczynski") # build.nblist(x) } \keyword{spatial}% at least one, from doc/KEYWORDS prabclus/man/conregmat.Rd0000644000176200001440000000254613473260375015127 0ustar liggesusers\name{con.regmat} \alias{con.regmat} %- Also NEED an `\alias' for EACH other topic documented here. \title{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{regmat}{0-1-matrix. Columns are species, rows are regions.} \item{neighbors}{A list with a component for every region. The components are vectors of integers indicating neighboring regions. A region without neighbors (e.g., an island) should be assigned a list \code{numeric(0)}.} \item{count}{logical. If \code{TRUE}, the number of the currently processed species is printed.} } \details{ Uses \code{con.comp}. } \value{ Vector of numbers of connected regions per species. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \note{Designed for use in \code{prabtest}.} \seealso{\code{\link{con.comp}}, \code{\link{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) } \keyword{spatial}% at least one, from doc/KEYWORDS \keyword{cluster}% __ONLY ONE__ keyword per line prabclus/man/regdistbetween.Rd0000644000176200001440000001312613475071664016162 0ustar liggesusers\name{regdistbetween} \alias{regdistbetween} \alias{print.regdistbetween} %- Also NEED an `\alias' for EACH other topic documented here. \title{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]) \method{print}{regdistbetween}(x,...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{dmx}{dissimilarity matrix or object of class \code{dist}. Explanatory dissimilarities (often these will be proper distances, but more general dissimilarities that do not necessarily fulfill the triangle inequality can be used, same for \code{dmy}).} \item{dmy}{dissimilarity matrix or object of class \code{dist}. Response dissimilarities.} \item{grouping}{something that can be coerced into a factor, defining the grouping of objects represented by the dissimilarities \code{dmx} and \code{dmy} (i.e., if \code{grouping} has length n, \code{dmx} and \code{dmy} must be dissimilarities between \code{n} objects).} \item{groups}{Vector of two levels. The two groups defining the regressions to be compared in the test. These can be factor levels, integer numbers, or strings, depending on the entries of \code{grouping}.} \item{x}{object of class \code{"regdistbetween"}.} \item{...}{optional arguments for print method.} } \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 \code{regeqdist} to test whether a joint regression for the within-group distances of both groups makes sense. If this is not rejected, \code{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 \code{regeqdist}, \code{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 \code{"regdistbetween"} with components \item{pval}{p-value.} \item{coeffdiff}{difference between regression fits (all distances minus within-group distances only) at \code{xcenterbetween}, see below.} \item{condition}{condition numbers of regressions, see \code{\link{kappa}}.} \item{lmfit}{list. Output objects of \code{\link{lm}} within the two groups.} \item{jr}{output object of \code{\link[bootstrap]{jackknife}} for difference between regression fitted values at \code{xcenterbetween}.} \item{xcenter}{mean of within-groups distances of explanatory variable, used for centering.} \item{xcenterbetween}{mean of between-groups distances of explanatory variable (after centering by \code{xcenter}); at this point regression fitted values are computed.} \item{tstat}{t-statistic.} \item{tdf}{degrees of freedom of t-statistic.} \item{jackest}{jackknife-estimator of difference between regression fitted values at \code{xcenterbetween}.} \item{jackse}{jackknife-standard error for \code{jackest}.} \item{jackpseudo}{vector of jacknife pseudovalues on which the test is based.} \item{testname}{title to be printed out when using \code{print.regdistbetween}.} \item{groups}{see above.} } \references{ Hausdorf, B. and Hennig, C. (2019) Species delimitation and geography. Submitted. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{regeqdist}}, \code{\link{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) } \keyword{htest}% __ONLY ONE__ keyword per line \keyword{regression}% __ONLY ONE__ keyword per line \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/toprab.Rd0000644000176200001440000000146513473260375014436 0ustar liggesusers\name{toprab} \alias{toprab} %- Also NEED an `\alias' for EACH other topic documented here. \title{Convert abundance matrix into presence/absence matrix} \description{ Converts abundance matrix into binary (logical) presence/absence matrix (\code{TRUE} if abundance>0). } \usage{ toprab(prabobj) } %- maybe also `usage' for other objects documented here. \arguments{ \item{prabobj}{object of class \code{prab}.} } \value{ Logical matrix with same dimensions as \code{prabobj$prab} as described above. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \examples{ data(siskiyou) x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb, distance="none") toprab(x) } \keyword{manip}% at least one, from doc/KEYWORDS prabclus/man/plotdistreg.Rd0000644000176200001440000001205313475057234015502 0ustar liggesusers\name{plotdistreg} \alias{plotdistreg} %- Also NEED an `\alias' for EACH other topic documented here. \title{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",...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{dmx}{dissimilarity matrix or object of class \code{dist}. Explanatory dissimilarities (often these will be proper distances, but more general dissimilarities that do not necessarily fulfill the triangle inequality can be used, same for \code{dmy}).} \item{dmy}{dissimilarity matrix or object of class \code{dist}. Response dissimilarities.} \item{grouping}{something that can be coerced into a factor, defining the grouping of objects represented by the dissimilarities \code{dmx} and \code{dmy} (i.e., if \code{grouping} has length n, \code{dmx} and \code{dmy} must be dissimilarities between \code{n} objects).} \item{groups}{Vector of two levels. The two groups defining the regressions to be compared in the test. These can be factor levels, integer numbers, or strings, depending on the entries of \code{grouping}.} \item{cols}{vector of four colors (or color numbers) to be used for plotting distances and regression lines within the first group, within the second group, distances between groups, and a line marking the center of the between-groups explanatory distances, see \code{col}-argument of \code{\link{par}}.} \item{pchs}{vector of three plot symbols (or numbers) to be used for plotting distances within the first group, within the second group, and distances between groups, see \code{pch}-argument of \code{\link{par}}.} \item{ltys}{vector of line type numbers to be used for single group within-group regression, both groups combined within-group regression, regression with all distances, and regression combining within-groups distances of one group with between-groups distances, see \code{lty}-argument of \code{\link{par}}.} \item{individual}{if \code{TRUE}, within-groups distances regression lines are shown for both groups.} \item{jointwithin}{if \code{TRUE}, the within-groups distances regression line for both groups combined is shown.} \item{jointall}{if \code{TRUE}, the regression line based on all distances is shown.} \item{oneplusjoint}{if \code{TRUE}, the regression lines combining within-groups distances of one group with between-groups distances are shown (the colors of these are the colors of the individual groups, the first two components of the \code{cols}-argument).} \item{jittering}{if \code{TRUE}, points are jittered to avoid overplotting.} \item{bcenterline}{if \code{TRUE}, a line is plotted to mark the center of the between-groups distances on the explanatory variable.} \item{xlim}{to be passed on to \code{\link{plot}}; the default is determined from the involved distances.} \item{ylim}{to be passed on to \code{\link{plot}}; the default is determined from the involved distances.} \item{xlab}{to be passed on to \code{\link{plot}}.} \item{ylab}{to be passed on to \code{\link{plot}}.} \item{...}{optional arguments to be passed on to \code{\link{plot}}.} } \references{ Hausdorf, B. and Hennig, C. (2019) Species delimitation and geography. Submitted. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{regeqdist}}, \code{\link{regdistbetween}}, \code{\link{regdistbetweenone}}, \code{\link{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)) } \keyword{htest}% __ONLY ONE__ keyword per line \keyword{regression}% __ONLY ONE__ keyword per line \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/regdistdiff.Rd0000644000176200001440000000544213473554217015441 0ustar liggesusers\name{regdistdiff} \alias{regdistdiff} %- Also NEED an `\alias' for EACH other topic documented here. \title{Regression difference between within-group dissimilarities} \description{ Given two dissimilarity matrices \code{dmx} and \code{dmy}, an indicator vector \code{x} and a grouping, this computes the difference between standard least squares regression predictions at point \code{xcenterbetween}. The regressions are based on the dissimilarities in \code{dmx} vs. \code{dmy} for objects indicated in \code{x}. \code{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) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{vector of logicals of length of the number of objects on which dissimilarities \code{dmx} and \code{dmy} are based.} \item{dmx}{dissimilarity matrix or object of class \code{\link{dist}}. Explanatory dissimilarities.} \item{dmy}{dissimilarity matrix or object of class \code{\link{dist}}. Response dissimilarities.} \item{grouping}{vector of length of the number of objects on which dissimilarities \code{dmx} and \code{dmy} are based. Grouping vector. Regressions will be based on the first two values that appear in \code{unique(grouping[x])} (note that objects that are not assigned to one of these groups will be ignored); normally \code{grouping} should indicate only two groups on the objects with \code{x=TRUE}, and then these are used.} \item{xcenter}{numeric. Dissimilarities \code{dmx} are centered by this, i.e., this value is subtracted from the dissimilarities before regression.} \item{xcenterbetween}{numeric. This specifies the x- (dissimilarity) value at which predictions from the two regressions are compared. Note that this is interpreted as after centering by \code{xcenter}.} } \value{ Difference between standard least squares regression predictions for the two groups at point \code{xcenterbetween}. } \references{ Hausdorf, B. and Hennig, C. (2019) Species delimitation and geography. Submitted. } \seealso{ \code{\link{regdistbetween}} } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \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) } \keyword{regression}% __ONLY ONE__ keyword per line \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/alleleinit.Rd0000644000176200001440000001403213604657724015267 0ustar liggesusers\name{alleleinit} \alias{alleleinit} \alias{alleleobject} \alias{print.alleleobject} %- Also NEED an `\alias' for EACH other topic documented here. \title{Diploid loci matrix initialization} \description{ \code{alleleinit} converts genetic data with diploid loci as generated by \code{\link{alleleconvert}} into an object of class \code{alleleobject}. \code{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) \method{print}{alleleobject}(x, ...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{file}{string. File name. File must be in \code{"prabclus"} format, see details. Either \code{file} or \code{allelematrix} needs to be specified.} \item{allelematrix}{matrix in \code{"prabclus"}-format as generated by \code{\link{alleleconvert}}, see details. Either \code{file} or \code{allelematrix} needs to be specified. } \item{rows.are.individuals}{logical. If \code{TRUE}, rows are interpreted as individuals and columns are interpreted as loci.} \item{neighborhood}{A string or a list with a component for every individual. The components are vectors of integers indicating neighboring individuals. An individual without neighbors should be assigned a vector \code{numeric(0)}. If \code{neighborhood} is a filename, it is attempted to read such a list from a file, where every row should correspond to one region (such as example dataset \code{nb.dat}). If \code{neighborhood="none"}, all neighborhoods are set to \code{numeric(0)}. The neighborhood can be tested by \code{\link{nbtest}} for consistency.} \item{distance}{\code{"alleledist"} %- \code{"chord"}, \code{"nei"}, or \code{"none"}. The distance measure between individuals to compute by \code{alleleinit}.} \item{namode}{one of \code{"single"}, \code{"individuals"}, \code{"variables"} , or \code{"none"}. Determines whether a single probability for the entry to be missing is computed for a single locus of an individual (\code{"single"}), a vector of individual-wise probabilities for loci to be missing (\code{"individuals"}), a vector of loci-wise probabilities for individuals to be missing (\code{"variables"}) or no missingness probability at all.} \item{nachar}{character denoting missing values.} \item{distcount}{logical. If \code{TRUE}, during distance computation individuals are counted on the screen.} \item{x}{object of class \code{alleleobject}.} \item{...}{necessary for print method.} } \details{ The required input format is the output format \code{"prabclus"} of \code{\link{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 \code{read.table(file,stringsAsFactors=FALSE)}) } \value{ \code{alleleinit} produces an object of class \code{alleleobject} (note that this is similar to class \code{\link{prab}}; for example both can be used with \code{\link{prabclust}}), which is a list with components \item{distmat}{distance matrix between individuals.} \item{amatrix}{data frame of input data with string variables in the input format, see details. Note that in the output for an individual the whole locus is declared missing if at least one of its alleles is missing in the input.} \item{charmatrix}{matrix of characters in which there are two rows for every individual corresponding to the two alleles in every locus (column). Entries are allele codes but missing values are coded as \code{NA}.} \item{nb}{neighborhood list, see above.} \item{ext.nblist}{a neighborhood list in which for every row in \code{charmatrix} the second row number corresponding to the neighboring individuals is listed.} \item{n.variables}{number of loci.} \item{n.individuals}{number of individuals.} \item{n.levels}{maximum number of different alleles in a locus.} \item{n.species}{identical to \code{n.individuals} used for compatibility with \code{prabclust}.} \item{alevels}{character vector with all used allele codes not including missing values.} \item{leveldist}{matrix in which rows are loci, columns are alleles and entries are frequencies of alleles per locus.} \item{prab}{useless matrix of number of factor levels corresponding to \code{amatrix} added for compatibility with objects of class \code{prab}.} \item{regperspec}{vector of row-wise sums of \code{prab} added for compatibility with objects of class \code{prab}.} \item{specperreg}{vector of column-wise sums of \code{prab} added for compatibility with objects of class \code{prab}.} \item{distance}{string denoting the chosen distance measure, see above.} \item{namode}{see above.} \item{naprob}{probability of missing values, numeric or vector, see documentation of argument \code{namode}.} \item{nasum}{number of missing entries (individual/loci) in \code{amatrix}.} \item{nachar}{see above.} \item{spatial}{logical. \code{TRUE} if a neighborhood was submitted.} } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{alleleconvert}}, \code{\link{alleledist}}, \code{\link{prabinit}}. %- \code{\link{neidist}}, \code{\link{chorddist}} } \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) } \keyword{spatial}% at least one, from doc/KEYWORDS \keyword{cluster}% __ONLY ONE__ keyword per line \keyword{manip} prabclus/man/tetragonula.Rd0000644000176200001440000000315413473260375015471 0ustar liggesusers\name{tetragonula} \alias{tetragonula} \alias{tetragonula.coord} \docType{data} % \non_function{} \title{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: \describe{ \item{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 \code{"258260"} on variable V10 means that on locus 10 the two alleles have codes 258 and 260. \code{"000"} refers to missing values.} \item{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).} } } \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. \emph{Mol. Ecol.} 13, 2317-2331. Rousset, F. (2008) genepop'007: a complete re-implementation of the genepop software for Windows and Linux. \emph{Molecular Ecology Resources} 8, 103-106. } \details{ Reads from example data file \code{Heterotrigona_indoFO.dat}. } \examples{ data(tetragonula) } \keyword{datasets} prabclus/man/build.charmatrix.Rd0000644000176200001440000000301313473260375016376 0ustar liggesusers\name{build.charmatrix} \alias{build.charmatrix} %- Also NEED an `\alias' for EACH other topic documented here. \title{Internal: create character matrix out of allele list} \description{ For use in \code{\link{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 \code{\link{alleledist}}. } \usage{ build.charmatrix(allelelist,n.individuals,n.variables) } %- maybe also `usage' for other objects documented here. \arguments{ \item{allelelist}{A list of lists. In the "outer" list, there are \code{n.variables} lists, one for each locus. In the "inner" list, for every individual there is a vector of two codes (typically characters, see \code{\link{alleleinit}}) for the two alleles in that locus.} \item{n.individuals}{integer. Number of individuals.} \item{n.variables}{integer. Number of loci.} } \value{ A matrix of characters in which there are two rows for every individual corresponding to the two alleles in every locus (column). } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{\code{\link{alleleinit}}, \code{\link{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) } \keyword{manip}% at least one, from doc/KEYWORDS prabclus/man/nn.Rd0000644000176200001440000000164113473260375013556 0ustar liggesusers\name{nn} \alias{nn} %- Also NEED an `\alias' for EACH other topic documented here. \title{Mean distance to kth nearest neighbor} \description{ Computes the mean of the distances from each point to its \code{ne}th nearest neighbor. } \usage{ nn(distmat, ne = 1) } %- maybe also `usage' for other objects documented here. \arguments{ \item{distmat}{symmetric distance matrix (not a \code{dist}-object).} \item{ne}{integer.} } \value{ numerical. } \references{ Hennig, C. and Hausdorf, B. (2004) Distance-based parametric bootstrap tests for clustering of species ranges. \emph{Computational Statistics and Data Analysis} 45, 875-896. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{\code{\link{prabtest}}} \examples{ data(kykladspecreg) j <- jaccard(t(kykladspecreg)) nn(j,4) } \keyword{cluster}% at least one, from doc/KEYWORDS prabclus/man/homogen.test.Rd0000644000176200001440000000476413473260375015566 0ustar liggesusers\name{homogen.test} \alias{homogen.test} %- Also NEED an `\alias' for EACH other topic documented here. \title{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 \code{ne} edges are drawn from all possible edges. } \usage{ homogen.test(distmat, ne = ncol(distmat), testdist = "erdos") } %- maybe also `usage' for other objects documented here. \arguments{ \item{distmat}{numeric symmetric distance matrix.} \item{ne}{integer. Number of edges in the data graph, corresponding to smallest distances.} \item{testdist}{string. If \code{testdist="erdos"}, the test distribution is a Poisson asymptotic distibution as given by Erdos and Renyi (1960). If \code{testdist="ling"}, the test distribution is exact as given by Ling (1973), which needs much more computing time.} } \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 \item{p}{p-value for one-sided test.} \item{p.twoside}{p-value for two-sided test, only if \code{testdist="erdos"}.} \item{iv}{number of isolated vertices in the data.} \item{lambda}{parameter of the Poisson test distribution, only if \code{testdist="erdos"}.} \item{distcut}{largest distance value for which an edge has been drawn.} \item{ne}{see above.} } \references{ Erdos, P. and Renyi, A. (1960) On the evolution of random graphs. \emph{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.) \emph{From Data to Knowledge}, Springer, Berlin, 167-176. Ling, R. F. (1973) A probability theory of cluster analysis. \emph{Journal of the American Statistical Association} 68, 159-164. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{\code{\link{prabtest}}} \examples{ options(digits=4) data(kykladspecreg) j <- jaccard(t(kykladspecreg)) homogen.test(j, testdist="erdos") homogen.test(j, testdist="ling") } \keyword{cluster}% at least one, from doc/KEYWORDS \keyword{htest}% __ONLY ONE__ keyword per line prabclus/man/communities.Rd0000644000176200001440000000336513473260375015504 0ustar liggesusers\name{communities} \alias{communities} %- Also NEED an `\alias' for EACH other topic documented here. \title{Construct communities from individuals} \description{ Construct communities from individuals using geographical distance and hierarchical clustering. Communities are clusters of geographically close individuals, formed by \code{\link{hclust}} with specified distance cutoff. } \usage{ communities(geodist,grouping=NULL, cutoff=1e-5,method="single") } %- maybe also `usage' for other objects documented here. \arguments{ \item{geodist}{\code{dist}-object or matrix of geographical distances between individuals.} \item{grouping}{something that can be coerced into a factor. Different groups indicated by \code{grouping} cannot be together in the same community. (If \code{NULL}, there is no constraint.)} \item{cutoff}{numeric; clustering distance cutoff value, passed on as parameter \code{h} to \code{cutree}. Note that if this is smaller than the smallest nonzero geographical distance, communities will be all sets of individuals that have zero geographical distance to each other.} \item{method}{\code{method}-parameter for \code{\link{hclust}}.} } \value{ Vector of community memberships for the individuals (integer numbers from 1 to the number of communities without interruption. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{ \code{\link{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) } \keyword{spatial}% __ONLY ONE__ keyword per line prabclus/man/unbuild.charmatrix.Rd0000644000176200001440000000317013473260375016745 0ustar liggesusers\name{unbuild.charmatrix} \alias{unbuild.charmatrix} %- Also NEED an `\alias' for EACH other topic documented here. \title{Internal: create allele list out of character matrix} \description{ Creates a list of lists, such as required by \code{\link{alleledist}}, from the \code{charmatrix} component of an \code{\link{alleleobject}}. } \usage{ unbuild.charmatrix(charmatrix,n.individuals,n.variables) } %- maybe also `usage' for other objects documented here. \arguments{ \item{charmatrix}{matrix of characters in which there are two rows for every individual corresponding to the two alleles in every locus (column). Entries are allele codes but missing values are coded as \code{NA}.} \item{n.individuals}{integer. Number of individuals.} \item{n.variables}{integer. Number of loci.} } \value{ A list of lists. In the "outer" list, there are \code{n.variables} lists, one for each locus. In the "inner" list, for every individual there is a vector of two codes (typically characters, see \code{\link{alleleinit}}) for the two alleles in that locus. } \author{Christian Hennig \email{christian.hennig@unibo.it} \url{https://www.unibo.it/sitoweb/christian.hennig/en}} \seealso{\code{\link{alleleinit}}, \code{\link{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)) } \keyword{manip}% at least one, from doc/KEYWORDS prabclus/DESCRIPTION0000644000176200001440000000201714515610012013566 0ustar liggesusersPackage: prabclus Title: Functions for Clustering and Testing of Presence-Absence, Abundance and Multilocus Genetic Data Version: 2.3-3 Date: 2023-10-23 Author: Christian Hennig , Bernhard Hausdorf Depends: R (>= 2.10), MASS, mclust Suggests: spdep, spatialreg, bootstrap, foreign, mvtnorm Description: Distance-based parametric bootstrap tests for clustering with spatial neighborhood information. Some distance measures, Clustering of presence-absence, abundance and multilocus genetic data for species delimitation, nearest neighbor based noise detection. Genetic distances between communities. Tests whether various distance-based regressions are equal. Try package?prabclus for on overview. Maintainer: Christian Hennig License: GPL URL: https://www.unibo.it/sitoweb/christian.hennig/en/ NeedsCompilation: no Packaged: 2023-10-23 23:33:09 UTC; chrish Repository: CRAN Date/Publication: 2023-10-24 00:30:02 UTC prabclus/tests/0000755000176200001440000000000014515601214013226 5ustar liggesusersprabclus/tests/Examples/0000755000176200001440000000000014515601146015010 5ustar liggesusersprabclus/tests/Examples/prabclus-Ex.Rout.save0000644000176200001440000132462714515600727021030 0ustar liggesusers R Under development (unstable) (2023-10-23 r85395) -- "Unsuffered Consequences" Copyright (C) 2023 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > pkgname <- "prabclus" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('prabclus') Loading required package: MASS Loading required package: mclust Package 'mclust' version 6.0.0 Type 'citation("mclust")' for citing this R package in publications. > > base::assign(".oldSearch", base::search(), pos = 'CheckExEnv') > base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv') > cleanEx() > nameEx("NNclean") > ### * NNclean > > flush(stderr()); flush(stdout()) > > ### Name: NNclean > ### Title: Nearest neighbor based clutter/noise detection > ### Aliases: NNclean print.nnclean > ### Keywords: multivariate cluster > > ### ** Examples > > library(mclust) > data(chevron) > nnc <- NNclean(chevron[,2:3],15,plot=TRUE) > plot(chevron[,2:3],col=1+nnc$z) > > > > cleanEx() > nameEx("abundtest") > ### * abundtest > > flush(stderr()); flush(stdout()) > > ### Name: abundtest > ### Title: Parametric bootstrap test for clustering in abundance matrices > ### Aliases: abundtest > ### Keywords: cluster spatial > > ### ** 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) > > > > cleanEx() > nameEx("allele2zeroone") > ### * allele2zeroone > > flush(stderr()); flush(stdout()) > > ### Name: allele2zeroone > ### Title: Converts alleleobject into binary matrix > ### Aliases: allele2zeroone > ### Keywords: manip > > ### ** Examples > > data(tetragonula) > ta <- alleleconvert(strmatrix=tetragonula[21:50,]) > tai <- alleleinit(allelematrix=ta) > allele2zeroone(tai) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [1,] 0 0 1 0 1 1 0 0 0 0 0 0 1 [2,] 0 0 1 0 1 1 0 0 0 0 0 0 1 [3,] 0 0 1 0 1 1 0 0 0 0 0 0 1 [4,] 0 0 1 0 1 1 0 0 0 0 0 0 1 [5,] 0 0 1 0 1 1 0 0 0 0 0 0 1 [6,] 0 0 0 0 1 1 0 0 0 0 0 0 1 [7,] 0 0 1 1 0 1 0 0 0 0 0 0 1 [8,] 0 0 1 0 0 1 0 0 0 0 0 0 1 [9,] 0 0 0 1 1 1 0 0 0 0 0 0 0 [10,] 0 0 0 0 1 1 0 0 0 0 0 0 1 [11,] 0 0 1 0 1 1 0 0 0 0 0 0 1 [12,] 0 0 0 0 1 1 0 0 0 0 0 0 1 [13,] 0 0 1 0 1 1 0 0 0 0 0 0 1 [14,] 0 0 0 1 1 1 0 0 0 0 0 0 1 [15,] 0 0 1 1 0 1 0 0 0 0 0 0 0 [16,] 0 1 0 0 0 0 0 0 0 0 1 0 0 [17,] 0 1 0 0 0 0 1 1 0 0 0 0 0 [18,] 1 1 0 0 0 0 0 1 0 1 0 0 0 [19,] 0 1 0 0 0 0 0 1 0 0 0 0 0 [20,] 0 1 0 0 0 0 0 1 0 0 0 0 0 [21,] 0 1 0 0 0 0 0 1 0 1 0 0 0 [22,] 0 1 0 0 0 0 0 1 1 0 0 0 0 [23,] 1 1 0 0 0 0 0 0 0 0 1 0 0 [24,] 0 1 0 0 0 0 0 0 0 0 1 0 0 [25,] 1 1 0 0 0 0 0 0 0 1 1 0 0 [26,] 0 1 0 0 0 0 0 0 0 0 1 0 0 [27,] 0 1 0 0 0 0 0 1 0 0 0 1 0 [28,] 1 1 0 0 0 0 0 1 1 0 0 0 0 [29,] 0 1 0 0 0 0 0 0 0 0 1 1 0 [30,] 0 1 0 0 0 0 0 1 1 0 0 0 0 [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [1,] 1 0 0 0 0 0 1 0 0 0 1 0 [2,] 0 0 1 0 0 0 1 0 0 0 1 1 [3,] 0 0 0 0 0 0 1 0 1 0 0 1 [4,] 1 0 0 0 0 0 1 0 0 0 0 1 [5,] 1 0 0 0 0 0 1 0 0 1 1 0 [6,] 1 0 0 0 0 0 1 0 0 0 1 1 [7,] 0 0 0 0 0 0 1 0 0 0 1 1 [8,] 0 0 0 1 0 0 1 0 1 0 1 0 [9,] 1 0 1 0 0 0 1 0 0 0 1 0 [10,] 0 0 1 0 0 0 1 0 0 0 1 0 [11,] 1 0 0 0 0 0 1 0 0 0 1 1 [12,] 0 0 0 1 0 0 1 0 0 0 0 1 [13,] 0 0 0 0 0 0 1 0 0 0 0 1 [14,] 0 0 0 0 0 0 1 0 0 0 1 0 [15,] 1 1 0 0 0 0 1 0 0 0 1 0 [16,] 0 0 0 0 1 0 0 0 0 0 0 0 [17,] 0 0 0 0 0 0 0 1 0 0 0 0 [18,] 0 0 0 0 1 1 0 0 0 0 0 0 [19,] 0 0 0 0 1 0 0 0 0 0 0 0 [20,] 0 0 0 0 1 0 0 0 0 0 0 0 [21,] 0 0 0 0 0 0 1 0 0 0 0 0 [22,] 0 0 0 0 1 0 0 0 0 0 0 0 [23,] 0 0 0 0 1 1 0 0 0 0 0 0 [24,] 0 0 0 0 1 0 0 0 0 0 0 0 [25,] 0 0 0 0 1 0 0 0 0 0 0 0 [26,] 0 0 0 0 1 1 0 0 0 0 0 0 [27,] 0 0 0 0 1 0 0 0 0 0 0 0 [28,] 0 0 0 0 1 1 0 0 0 0 0 0 [29,] 0 0 0 0 1 1 0 0 0 0 0 0 [30,] 0 0 0 0 1 0 0 0 0 0 0 0 [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [1,] 0 0 0 0 1 0 1 0 0 0 0 0 [2,] 0 0 0 0 1 0 1 0 0 0 0 0 [3,] 0 0 0 1 0 0 1 0 0 0 0 0 [4,] 1 0 0 0 1 0 1 0 0 0 0 0 [5,] 0 0 0 1 1 0 1 0 0 0 0 0 [6,] 0 0 0 0 1 0 1 0 0 0 0 0 [7,] 0 0 0 0 1 0 1 0 0 0 0 0 [8,] 0 0 0 0 1 0 1 0 0 0 0 0 [9,] 0 0 0 1 1 0 1 0 0 0 0 0 [10,] 0 0 0 1 0 0 1 0 0 0 0 0 [11,] 0 0 0 1 1 0 1 0 0 0 0 0 [12,] 0 0 0 1 1 0 1 0 0 0 0 0 [13,] 1 0 0 0 1 0 1 0 0 0 0 0 [14,] 0 0 0 1 1 0 1 0 0 0 0 0 [15,] 0 0 0 0 1 0 1 0 0 0 0 0 [16,] 0 0 0 0 1 0 0 0 0 1 0 0 [17,] 0 0 1 0 0 0 0 0 0 0 1 1 [18,] 0 0 0 0 1 0 0 0 1 1 0 0 [19,] 0 0 0 0 1 1 0 0 0 1 0 0 [20,] 0 1 0 0 1 0 0 0 1 1 0 0 [21,] 0 0 1 1 0 0 0 1 0 0 0 0 [22,] 0 0 0 0 1 0 0 0 0 1 0 0 [23,] 0 0 0 0 1 0 0 0 1 1 0 0 [24,] 0 0 0 0 1 0 0 0 1 1 0 0 [25,] 0 0 0 0 1 0 0 0 1 1 0 0 [26,] 0 0 0 1 1 0 0 0 1 1 0 0 [27,] 0 0 0 0 1 0 0 0 1 0 0 0 [28,] 0 0 0 0 1 0 0 0 0 1 0 0 [29,] 0 0 0 0 1 0 0 0 0 1 0 0 [30,] 0 0 0 0 1 0 0 0 0 1 0 0 [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [1,] 0 0 1 0 1 0 0 0 0 0 0 1 [2,] 0 0 1 0 1 0 0 0 0 0 0 1 [3,] 0 0 1 1 0 0 0 0 0 0 0 1 [4,] 0 0 1 0 1 0 0 0 0 0 0 1 [5,] 0 0 1 0 1 0 0 0 0 0 0 1 [6,] 0 0 0 1 1 0 0 0 0 0 0 0 [7,] 0 0 1 0 1 0 0 0 0 0 0 1 [8,] 0 0 1 0 0 0 0 0 0 0 0 1 [9,] 0 0 1 0 1 0 0 0 0 0 0 1 [10,] 0 0 0 0 1 0 0 0 0 0 0 1 [11,] 0 0 1 1 0 0 0 0 0 0 0 1 [12,] 0 0 1 0 0 0 0 0 0 0 0 1 [13,] 0 0 1 0 1 0 0 0 0 0 0 1 [14,] 0 0 1 0 1 0 0 0 0 0 0 1 [15,] 0 0 1 0 1 0 0 0 0 0 0 1 [16,] 0 0 0 1 0 0 1 0 0 0 1 0 [17,] 0 1 0 0 0 0 0 1 0 1 1 0 [18,] 0 0 0 0 1 0 1 0 0 0 1 0 [19,] 0 0 0 1 0 0 1 0 0 0 1 0 [20,] 0 0 0 1 0 0 1 0 0 0 1 0 [21,] 1 1 0 0 0 1 0 1 0 0 1 0 [22,] 0 0 0 1 1 0 1 0 0 0 1 0 [23,] 0 0 0 1 1 0 1 0 0 0 1 0 [24,] 0 0 0 1 1 0 1 0 0 0 1 0 [25,] 0 1 0 1 0 0 1 0 1 0 1 0 [26,] 0 0 0 1 0 0 0 0 1 0 1 0 [27,] 0 0 0 1 1 0 1 0 1 0 1 0 [28,] 0 0 0 1 1 0 1 0 0 0 1 0 [29,] 0 1 0 1 0 0 1 0 1 0 1 0 [30,] 0 0 0 1 0 0 1 0 0 1 1 0 [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [1,] 1 0 1 0 0 0 0 0 0 0 1 1 [2,] 0 0 1 0 0 0 0 0 0 1 0 1 [3,] 0 0 1 0 0 0 0 0 1 0 0 0 [4,] 0 0 1 0 0 0 0 0 0 0 1 0 [5,] 1 0 1 0 0 0 0 0 0 0 0 1 [6,] 1 0 1 0 0 0 0 0 1 0 1 0 [7,] 0 0 1 0 0 0 0 0 1 1 0 0 [8,] 0 0 1 0 0 0 0 0 0 0 0 1 [9,] 0 0 1 0 0 0 0 0 0 0 0 1 [10,] 1 0 1 0 0 0 0 0 1 1 0 0 [11,] 0 0 1 0 0 0 0 0 0 0 0 1 [12,] 1 0 1 0 0 0 0 0 0 1 1 0 [13,] 0 0 1 0 0 0 0 0 1 0 0 0 [14,] 0 0 1 0 0 0 0 0 0 0 1 0 [15,] 0 0 1 0 0 0 0 0 0 0 0 0 [16,] 0 0 0 1 1 0 0 0 0 0 0 1 [17,] 0 1 0 1 1 0 0 0 0 0 0 0 [18,] 0 0 0 1 1 0 0 0 0 0 0 0 [19,] 0 0 0 1 1 1 0 0 0 0 0 0 [20,] 0 0 0 1 1 0 0 0 0 0 0 0 [21,] 0 0 0 1 0 0 1 1 0 0 0 0 [22,] 0 0 0 1 1 1 0 0 0 0 0 0 [23,] 0 0 0 1 1 0 0 0 0 0 0 0 [24,] 0 0 0 1 1 1 0 0 0 0 0 0 [25,] 0 0 0 1 1 1 0 0 0 0 0 0 [26,] 0 0 0 1 1 1 0 0 0 0 0 0 [27,] 0 0 0 1 0 1 0 0 0 0 0 0 [28,] 0 0 0 1 0 1 0 0 0 0 0 0 [29,] 0 0 0 1 1 0 0 0 0 0 0 0 [30,] 0 0 0 1 1 0 0 0 0 0 0 1 [,62] [,63] [,64] [,65] [,66] [,67] [,68] [1,] 0 0 0 1 0 0 0 [2,] 0 0 0 1 0 0 0 [3,] 1 0 0 1 0 0 0 [4,] 0 1 0 1 0 0 0 [5,] 1 0 0 1 0 0 0 [6,] 0 0 0 1 0 0 0 [7,] 0 0 0 1 0 0 0 [8,] 0 0 0 1 0 0 0 [9,] 1 0 0 1 0 0 0 [10,] 0 0 0 1 0 0 0 [11,] 0 1 0 1 0 0 0 [12,] 0 0 0 1 0 0 0 [13,] 0 1 0 1 0 0 0 [14,] 0 0 1 1 0 0 0 [15,] 1 0 0 1 0 0 0 [16,] 0 0 0 0 0 1 0 [17,] 0 0 0 0 0 1 1 [18,] 0 0 0 0 0 1 0 [19,] 0 0 0 0 0 1 0 [20,] 0 0 0 0 0 1 0 [21,] 0 0 0 0 1 0 1 [22,] 0 0 0 0 0 1 0 [23,] 0 0 0 0 0 1 0 [24,] 0 0 0 0 0 1 0 [25,] 0 0 0 0 0 1 0 [26,] 0 0 0 0 0 1 0 [27,] 0 0 0 0 0 1 0 [28,] 0 0 0 0 0 1 0 [29,] 0 0 0 0 0 1 0 [30,] 0 0 0 0 0 1 0 > > > > cleanEx() > nameEx("alleleconvert") > ### * alleleconvert > > flush(stderr()); flush(stdout()) > > ### Name: alleleconvert > ### Title: Format conversion for codominant marker data > ### Aliases: alleleconvert > ### Keywords: manip > > ### ** Examples > > data(tetragonula) > # This uses example data file Heterotrigona_indoFO.dat > str(alleleconvert(strmatrix=tetragonula)) chr [1:236, 1:13] "NO" "EO" "NQ" "OO" "OO" "LN" "OO" "EP" "NO" "EO" "NN" ... - attr(*, "alevels")= chr [1:28] "A" "B" "C" "D" ... > 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) [,1] [,2] [1,] "AB" "--" [2,] "AA" "AB" [3,] "BB" "CD" attr(,"alevels") [1] "A" "B" "C" "D" > alleleconvert(strmatrix=strucmatrix,format.in="structure", + format.out="structurama",orig.nachar="0",new.nachar="-9",firstcolname=TRUE) [,1] [,2] [1,] "(122,144)" "(-9,-9)" [2,] "(122,122)" "(21,33)" [3,] "(144,144)" "(35,44)" attr(,"alevels") [1] "21" "33" "35" "44" > > > > cleanEx() > nameEx("alleledist") > ### * alleledist > > flush(stderr()); flush(stdout()) > > ### Name: alleledist > ### Title: Shared allele distance for diploid loci > ### Aliases: alleledist > ### Keywords: cluster > > ### ** 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)) num [1:50, 1:50] 0 0.208 0.333 0.292 0.25 ... > > > > cleanEx() > nameEx("alleleinit") > ### * alleleinit > > flush(stderr()); flush(stdout()) > > ### Name: alleleinit > ### Title: Diploid loci matrix initialization > ### Aliases: alleleinit alleleobject print.alleleobject > ### Keywords: spatial cluster manip > > ### ** 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) Diploid allele/locus-data object Number of individuals: 50 Number of loci: 13 Alleles (all loci): A B C D E F G H I J K L M Object contains between-individuals dissimilarity matrix of type alleledist . Object contains neighborhood list nb between individuals. Object contains 65 missing loci data. Further potentially informative components: amatrix (individuals*loci matrix), leveldist (distribution of alleles per locus), naprob (probabilities for missing values, governed by namode= variables ), components charmatrix, ext.nblist, prab, regperspec, specperreg don't contain additional information and are only needed for efficient data handling. > > > > cleanEx() > nameEx("allelepaircomp") > ### * allelepaircomp > > flush(stderr()); flush(stdout()) > > ### Name: allelepaircomp > ### Title: Internal: compares two pairs of alleles > ### Aliases: allelepaircomp > ### Keywords: cluster > > ### ** Examples > > allelepaircomp(c("A","B"),c("A","C")) [1] 1 > > > > cleanEx() > nameEx("autoconst") > ### * autoconst > > flush(stderr()); flush(stdout()) > > ### Name: autoconst > ### Title: Spatial autocorrelation parameter estimation > ### Aliases: autoconst autoreg > ### Keywords: spatial > > ### ** 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) Calculating disjunction probability for original data 0.3307 Estimating disj. parameter: Simulations for p= 0 Estimating disj. parameter: Simulations for p= 0.3 Estimating disj. parameter: Simulations for p= 0.6 Estimating disj. parameter: Simulations for p= 0.9 Estimated disjunction parameter = 0.366 > > > > cleanEx() > nameEx("build.charmatrix") > ### * build.charmatrix > > flush(stderr()); flush(stdout()) > > ### Name: build.charmatrix > ### Title: Internal: create character matrix out of allele list > ### Aliases: build.charmatrix > ### Keywords: manip > > ### ** 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) [,1] [,2] [1,] "A" "A" [2,] "A" "C" [3,] "B" "B" [4,] "A" "B" [5,] NA "A" [6,] NA "D" > > > > cleanEx() > nameEx("build.ext.nblist") > ### * build.ext.nblist > > flush(stderr()); flush(stdout()) > > ### Name: build.ext.nblist > ### Title: Internal: generates neighborhood list for diploid loci > ### Aliases: build.ext.nblist > ### Keywords: cluster > > ### ** Examples > > data(veronica) > vnb <- coord2dist(coordmatrix=veronica.coord[1:20,], cut=20, + file.format="decimal2",neighbors=TRUE) > build.ext.nblist(vnb$nblist) [[1]] [1] 6 8 10 24 26 28 30 32 5 7 9 23 25 27 29 31 2 [[2]] [1] 6 8 10 24 26 28 30 32 5 7 9 23 25 27 29 31 1 [[3]] [1] 12 11 4 [[4]] [1] 12 11 3 [[5]] [1] 2 8 10 24 26 28 30 32 1 7 9 23 25 27 29 31 6 [[6]] [1] 2 8 10 24 26 28 30 32 1 7 9 23 25 27 29 31 5 [[7]] [1] 2 6 10 24 26 28 30 32 1 5 9 23 25 27 29 31 8 [[8]] [1] 2 6 10 24 26 28 30 32 1 5 9 23 25 27 29 31 7 [[9]] [1] 2 6 8 24 26 28 30 32 1 5 7 23 25 27 29 31 10 [[10]] [1] 2 6 8 24 26 28 30 32 1 5 7 23 25 27 29 31 9 [[11]] [1] 4 3 12 [[12]] [1] 4 3 11 [[13]] [1] 14 [[14]] [1] 13 [[15]] [1] 18 20 22 17 19 21 16 [[16]] [1] 18 20 22 17 19 21 15 [[17]] [1] 16 20 22 15 19 21 18 [[18]] [1] 16 20 22 15 19 21 17 [[19]] [1] 16 18 22 15 17 21 20 [[20]] [1] 16 18 22 15 17 21 19 [[21]] [1] 16 18 20 15 17 19 22 [[22]] [1] 16 18 20 15 17 19 21 [[23]] [1] 2 6 8 10 26 28 30 32 1 5 7 9 25 27 29 31 24 [[24]] [1] 2 6 8 10 26 28 30 32 1 5 7 9 25 27 29 31 23 [[25]] [1] 2 6 8 10 24 28 30 32 1 5 7 9 23 27 29 31 26 [[26]] [1] 2 6 8 10 24 28 30 32 1 5 7 9 23 27 29 31 25 [[27]] [1] 2 6 8 10 24 26 30 32 1 5 7 9 23 25 29 31 28 [[28]] [1] 2 6 8 10 24 26 30 32 1 5 7 9 23 25 29 31 27 [[29]] [1] 2 6 8 10 24 26 28 32 1 5 7 9 23 25 27 31 30 [[30]] [1] 2 6 8 10 24 26 28 32 1 5 7 9 23 25 27 31 29 [[31]] [1] 2 6 8 10 24 26 28 30 1 5 7 9 23 25 27 29 32 [[32]] [1] 2 6 8 10 24 26 28 30 1 5 7 9 23 25 27 29 31 [[33]] [1] 36 38 40 35 37 39 34 [[34]] [1] 36 38 40 35 37 39 33 [[35]] [1] 34 38 40 33 37 39 36 [[36]] [1] 34 38 40 33 37 39 35 [[37]] [1] 34 36 40 33 35 39 38 [[38]] [1] 34 36 40 33 35 39 37 [[39]] [1] 34 36 38 33 35 37 40 [[40]] [1] 34 36 38 33 35 37 39 > > > > cleanEx() > nameEx("build.nblist") > ### * build.nblist > > flush(stderr()); flush(stdout()) > > ### Name: build.nblist > ### Title: Generate spatial weights from prabclus neighborhood list > ### Aliases: build.nblist > ### Keywords: spatial > > ### ** Examples > > # Not run; requires package spdep > # data(siskiyou) > # x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb, > # distance="logkulczynski") > # build.nblist(x) > > > > cleanEx() > nameEx("cluspop.nb") > ### * cluspop.nb > > flush(stderr()); flush(stdout()) > > ### Name: cluspop.nb > ### Title: Simulation of presence-absence matrices (clustered) > ### Aliases: cluspop.nb > ### Keywords: spatial > > ### ** 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)) Species 1 Clustered species 2 Clustered species 3 Clustered species 4 Clustered species 5 Clustered species 6 Clustered species 7 Clustered species 8 Clustered species 9 Clustered species 10 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 0 0 0 1 0 0 0 0 0 0 [2,] 0 0 0 1 0 0 0 0 0 0 [3,] 0 0 0 1 0 0 0 0 0 0 [4,] 0 0 0 1 0 0 0 0 0 0 [5,] 0 0 1 0 0 0 0 0 0 0 [6,] 0 0 1 1 0 0 0 0 0 0 [7,] 0 0 1 1 0 0 0 0 0 0 [8,] 0 0 0 1 0 0 0 0 0 0 [9,] 1 0 1 1 0 0 0 0 0 0 [10,] 1 0 0 1 0 0 0 0 0 0 [11,] 1 0 1 0 0 0 0 0 0 0 [12,] 1 0 1 1 0 0 0 0 0 0 [13,] 0 0 1 0 0 0 0 0 0 0 [14,] 1 0 1 0 0 0 0 1 0 0 [15,] 0 0 1 0 0 0 0 1 0 0 [16,] 0 0 1 0 0 0 1 1 0 0 [17,] 1 0 0 0 1 0 0 0 0 0 [18,] 0 1 0 0 1 0 1 1 0 0 [19,] 0 1 0 0 1 0 1 1 0 1 [20,] 0 1 0 0 1 1 1 1 0 1 [21,] 0 0 0 0 1 1 1 1 0 0 [22,] 1 1 0 0 0 0 1 1 0 0 [23,] 0 0 0 0 1 1 1 1 0 0 [24,] 0 0 0 0 0 0 1 1 1 0 [25,] 1 0 0 0 0 1 0 0 0 1 [26,] 0 0 0 0 1 1 0 0 1 1 [27,] 0 0 0 0 0 0 0 0 1 1 [28,] 0 0 0 0 0 0 0 0 1 0 [29,] 0 1 0 0 0 0 1 0 1 0 [30,] 0 1 0 0 0 1 0 0 1 1 [31,] 0 1 0 0 0 1 0 0 1 1 [32,] 1 1 0 0 1 1 0 0 1 1 [33,] 1 1 0 0 1 1 0 0 1 1 [34,] 0 1 0 0 1 1 1 0 1 1 > > > > cleanEx() > nameEx("communities") > ### * communities > > flush(stderr()); flush(stdout()) > > ### Name: communities > ### Title: Construct communities from individuals > ### Aliases: communities > ### Keywords: spatial > > ### ** 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) [1] 1 2 3 4 4 2 5 6 6 6 6 4 4 3 3 3 7 7 7 7 7 8 8 9 10 [26] 11 11 5 5 12 12 11 8 8 8 13 13 13 13 1 1 13 14 15 15 15 15 16 16 16 [51] 16 16 9 9 9 9 17 18 19 19 19 20 20 21 22 23 23 23 23 24 24 24 22 22 22 [76] 22 25 25 25 25 25 26 27 27 27 27 27 28 28 28 > > > > > cleanEx() > nameEx("communitydist") > ### * communitydist > > flush(stderr()); flush(stdout()) > > ### Name: communitydist > ### Title: Distances between communities > ### Aliases: communitydist > ### Keywords: spatial multivariate > > ### ** 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) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [1,] 0 0 0 0 0 4291.1 4250.1 4209.2 4470.8 4380.9 4250.1 4209.2 [2,] 0 0 0 0 0 4291.1 4250.1 4209.2 4470.8 4380.9 4250.1 4209.2 [3,] 0 0 0 0 0 4291.1 4250.1 4209.2 4470.8 4380.9 4250.1 4209.2 [4,] 0 0 0 0 0 4291.1 4250.1 4209.2 4470.8 4380.9 4250.1 4209.2 [5,] 0 0 0 0 0 4291.1 4250.1 4209.2 4470.8 4380.9 4250.1 4209.2 [6,] 4291 4291 4291 4291 4291 0.0 214.9 429.8 406.2 203.1 214.9 429.8 [7,] 4250 4250 4250 4250 4250 214.9 0.0 214.9 338.2 276.6 214.9 214.9 [8,] 4209 4209 4209 4209 4209 429.8 214.9 0.0 270.3 350.0 214.9 0.0 [9,] 4471 4471 4471 4471 4471 406.2 338.2 270.3 0.0 203.1 338.2 270.3 [10,] 4381 4381 4381 4381 4381 203.1 276.6 350.0 203.1 0.0 276.6 350.0 [11,] 4250 4250 4250 4250 4250 214.9 214.9 214.9 338.2 276.6 0.0 214.9 [12,] 4209 4209 4209 4209 4209 429.8 214.9 0.0 270.3 350.0 214.9 0.0 [13,] 6600 6600 6600 6600 6600 2430.9 2417.6 2404.2 2156.3 2293.6 2417.6 2404.2 [14,] 6600 6600 6600 6600 6600 2430.9 2417.6 2404.2 2156.3 2293.6 2417.6 2404.2 [15,] 6600 6600 6600 6600 6600 2430.9 2417.6 2404.2 2156.3 2293.6 2417.6 2404.2 [16,] 6600 6600 6600 6600 6600 2430.9 2417.6 2404.2 2156.3 2293.6 2417.6 2404.2 [17,] 6600 6600 6600 6600 6600 2430.9 2417.6 2404.2 2156.3 2293.6 2417.6 2404.2 [18,] 6600 6600 6600 6600 6600 2430.9 2417.6 2404.2 2156.3 2293.6 2417.6 2404.2 [19,] 6600 6600 6600 6600 6600 2430.9 2417.6 2404.2 2156.3 2293.6 2417.6 2404.2 [,13] [,14] [,15] [,16] [,17] [,18] [,19] [1,] 6600 6600 6600 6600 6600 6600 6600 [2,] 6600 6600 6600 6600 6600 6600 6600 [3,] 6600 6600 6600 6600 6600 6600 6600 [4,] 6600 6600 6600 6600 6600 6600 6600 [5,] 6600 6600 6600 6600 6600 6600 6600 [6,] 2431 2431 2431 2431 2431 2431 2431 [7,] 2418 2418 2418 2418 2418 2418 2418 [8,] 2404 2404 2404 2404 2404 2404 2404 [9,] 2156 2156 2156 2156 2156 2156 2156 [10,] 2294 2294 2294 2294 2294 2294 2294 [11,] 2418 2418 2418 2418 2418 2418 2418 [12,] 2404 2404 2404 2404 2404 2404 2404 [13,] 0 0 0 0 0 0 0 [14,] 0 0 0 0 0 0 0 [15,] 0 0 0 0 0 0 0 [16,] 0 0 0 0 0 0 0 [17,] 0 0 0 0 0 0 0 [18,] 0 0 0 0 0 0 0 [19,] 0 0 0 0 0 0 0 > c1$comvector [1] 1 1 1 2 2 2 3 3 3 4 5 6 6 7 7 8 8 9 9 10 10 11 11 12 13 [26] 13 14 14 15 15 16 16 17 17 18 18 19 19 > c2$comvector [1] 1 1 1 2 2 2 3 3 3 4 5 6 6 7 7 8 8 9 9 10 10 11 11 12 13 [26] 13 14 14 15 15 16 16 17 17 18 18 19 19 > c3$comvector [1] 1 1 1 2 2 2 3 3 3 4 5 6 6 7 7 8 8 9 9 10 10 11 11 12 13 [26] 13 14 14 15 15 16 16 17 17 18 18 19 19 > c4$comvector [1] 1 1 1 2 2 2 3 3 3 4 5 6 6 7 7 8 8 9 9 10 10 11 11 12 13 [26] 13 14 14 15 15 16 16 17 17 18 18 19 19 > c5$comvector [1] 1 1 1 2 2 2 3 3 3 4 5 6 6 7 7 8 8 9 9 10 10 11 11 12 13 [26] 13 14 14 15 15 16 16 17 17 18 18 19 19 > round(c1$dist,digits=2) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [1,] 0.00 1.42 1.52 1.37 1.59 3.06 3.03 2.94 3.13 2.99 3.01 3.02 3.14 [2,] 1.42 0.00 1.28 1.43 1.73 2.77 3.02 2.97 3.08 2.98 2.96 2.95 3.08 [3,] 1.52 1.28 0.00 1.02 1.50 2.85 2.88 2.82 2.91 2.83 2.95 2.89 2.94 [4,] 1.37 1.43 1.02 0.00 1.56 3.11 3.11 3.04 3.15 3.04 3.13 3.08 3.16 [5,] 1.59 1.73 1.50 1.56 0.00 3.00 3.03 2.93 3.11 2.98 3.04 3.08 3.16 [6,] 3.06 2.77 2.85 3.11 3.00 0.00 1.99 2.04 2.02 1.85 1.92 2.04 2.48 [7,] 3.03 3.02 2.88 3.11 3.03 1.99 0.00 1.48 1.73 1.32 1.55 1.49 2.65 [8,] 2.94 2.97 2.82 3.04 2.93 2.04 1.48 0.00 1.62 1.30 1.61 1.44 2.48 [9,] 3.13 3.08 2.91 3.15 3.11 2.02 1.73 1.62 0.00 1.33 1.84 1.76 2.59 [10,] 2.99 2.98 2.83 3.04 2.98 1.85 1.32 1.30 1.33 0.00 1.43 1.45 2.55 [11,] 3.01 2.96 2.95 3.13 3.04 1.92 1.55 1.61 1.84 1.43 0.00 1.33 2.55 [12,] 3.02 2.95 2.89 3.08 3.08 2.04 1.49 1.44 1.76 1.45 1.33 0.00 2.53 [13,] 3.14 3.08 2.94 3.16 3.16 2.48 2.65 2.48 2.59 2.55 2.55 2.53 0.00 [14,] 3.15 3.02 2.95 3.18 3.18 2.55 2.59 2.62 2.65 2.63 2.60 2.61 1.03 [15,] 3.16 3.06 2.98 3.20 3.20 2.58 2.52 2.67 2.65 2.63 2.71 2.71 1.28 [16,] 3.15 3.09 2.95 3.18 3.18 2.53 2.55 2.52 2.66 2.60 2.51 2.49 0.70 [17,] 3.19 3.05 3.00 3.15 3.15 2.56 2.69 2.61 2.63 2.65 2.66 2.69 1.09 [18,] 3.13 2.99 2.88 3.15 3.10 2.46 2.53 2.49 2.62 2.56 2.44 2.45 1.23 [19,] 3.14 3.04 2.94 3.16 3.16 2.55 2.42 2.58 2.50 2.49 2.62 2.62 1.16 [,14] [,15] [,16] [,17] [,18] [,19] [1,] 3.15 3.16 3.15 3.19 3.13 3.14 [2,] 3.02 3.06 3.09 3.05 2.99 3.04 [3,] 2.95 2.98 2.95 3.00 2.88 2.94 [4,] 3.18 3.20 3.18 3.15 3.15 3.16 [5,] 3.18 3.20 3.18 3.15 3.10 3.16 [6,] 2.55 2.58 2.53 2.56 2.46 2.55 [7,] 2.59 2.52 2.55 2.69 2.53 2.42 [8,] 2.62 2.67 2.52 2.61 2.49 2.58 [9,] 2.65 2.65 2.66 2.63 2.62 2.50 [10,] 2.63 2.63 2.60 2.65 2.56 2.49 [11,] 2.60 2.71 2.51 2.66 2.44 2.62 [12,] 2.61 2.71 2.49 2.69 2.45 2.62 [13,] 1.03 1.28 0.70 1.09 1.23 1.16 [14,] 0.00 1.01 0.77 0.69 1.01 0.95 [15,] 1.01 0.00 1.05 1.31 1.38 0.74 [16,] 0.77 1.05 0.00 1.12 1.02 1.07 [17,] 0.69 1.31 1.12 0.00 1.15 1.03 [18,] 1.01 1.38 1.02 1.15 0.00 1.31 [19,] 0.95 0.74 1.07 1.03 1.31 0.00 > round(c2$dist,digits=2) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [1,] 0.00 0.06 0.10 -0.21 0.01 0.59 0.58 0.59 0.70 0.59 0.62 0.58 [2,] 0.06 0.00 -0.01 0.04 0.21 0.59 0.62 0.64 0.74 0.63 0.65 0.64 [3,] 0.10 -0.01 0.00 -0.61 -0.12 0.54 0.54 0.55 0.66 0.53 0.59 0.52 [4,] -0.21 0.04 -0.61 0.00 NA 0.58 0.56 0.62 0.88 0.58 0.68 NA [5,] 0.01 0.21 -0.12 NA 0.00 0.57 0.54 0.59 0.87 0.57 0.67 NA [6,] 0.59 0.59 0.54 0.58 0.57 0.00 0.28 0.30 0.53 0.18 0.28 0.20 [7,] 0.58 0.62 0.54 0.56 0.54 0.28 0.00 0.02 0.32 -0.14 0.07 -0.16 [8,] 0.59 0.64 0.55 0.62 0.59 0.30 0.02 0.00 0.33 -0.12 0.21 -0.06 [9,] 0.70 0.74 0.66 0.88 0.87 0.53 0.32 0.33 0.00 0.16 0.48 0.68 [10,] 0.59 0.63 0.53 0.58 0.57 0.18 -0.14 -0.12 0.16 0.00 0.00 -0.33 [11,] 0.62 0.65 0.59 0.68 0.67 0.28 0.07 0.21 0.48 0.00 0.00 -0.23 [12,] 0.58 0.64 0.52 NA NA 0.20 -0.16 -0.06 0.68 -0.33 -0.23 0.00 [13,] 0.71 0.75 0.67 0.91 0.91 0.65 0.63 0.64 0.84 0.64 0.70 0.87 [14,] 0.70 0.73 0.67 0.88 0.88 0.65 0.60 0.66 0.83 0.64 0.70 0.84 [15,] 0.69 0.72 0.66 0.84 0.84 0.63 0.55 0.64 0.80 0.61 0.68 0.79 [16,] 0.69 0.73 0.65 0.84 0.84 0.62 0.57 0.62 0.80 0.61 0.66 0.77 [17,] 0.64 0.67 0.60 0.69 0.69 0.53 0.52 0.55 0.70 0.54 0.59 0.62 [18,] 0.68 0.70 0.63 0.80 0.79 0.58 0.54 0.58 0.77 0.58 0.61 0.70 [19,] 0.69 0.72 0.65 0.84 0.84 0.62 0.52 0.63 0.79 0.59 0.68 0.78 [,13] [,14] [,15] [,16] [,17] [,18] [,19] [1,] 0.71 0.70 0.69 0.69 0.64 0.68 0.69 [2,] 0.75 0.73 0.72 0.73 0.67 0.70 0.72 [3,] 0.67 0.67 0.66 0.65 0.60 0.63 0.65 [4,] 0.91 0.88 0.84 0.84 0.69 0.80 0.84 [5,] 0.91 0.88 0.84 0.84 0.69 0.79 0.84 [6,] 0.65 0.65 0.63 0.62 0.53 0.58 0.62 [7,] 0.63 0.60 0.55 0.57 0.52 0.54 0.52 [8,] 0.64 0.66 0.64 0.62 0.55 0.58 0.63 [9,] 0.84 0.83 0.80 0.80 0.70 0.77 0.79 [10,] 0.64 0.64 0.61 0.61 0.54 0.58 0.59 [11,] 0.70 0.70 0.68 0.66 0.59 0.61 0.68 [12,] 0.87 0.84 0.79 0.77 0.62 0.70 0.78 [13,] 0.00 0.41 0.44 -0.08 0.09 0.35 0.38 [14,] 0.41 0.00 0.12 -0.08 -0.22 0.30 0.07 [15,] 0.44 0.12 0.00 0.06 0.18 0.40 -0.14 [16,] -0.08 -0.08 0.06 0.00 0.03 0.25 0.11 [17,] 0.09 -0.22 0.18 0.03 0.00 0.07 -0.07 [18,] 0.35 0.30 0.40 0.25 0.07 0.00 0.33 [19,] 0.38 0.07 -0.14 0.11 -0.07 0.33 0.00 > round(c3$dist,digits=2) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [1,] 0.00 0.37 0.43 0.31 0.38 0.92 0.94 0.91 0.97 0.92 0.93 0.92 0.97 [2,] 0.37 0.00 0.36 0.33 0.40 0.83 0.94 0.92 0.96 0.92 0.90 0.89 0.96 [3,] 0.43 0.36 0.00 0.25 0.36 0.85 0.89 0.85 0.88 0.85 0.90 0.85 0.89 [4,] 0.31 0.33 0.25 0.00 0.33 0.92 0.96 0.92 0.96 0.92 0.96 0.92 0.96 [5,] 0.38 0.40 0.36 0.33 0.00 0.90 0.92 0.85 0.92 0.90 0.94 0.92 0.96 [6,] 0.92 0.83 0.85 0.92 0.90 0.00 0.56 0.52 0.53 0.47 0.48 0.48 0.66 [7,] 0.94 0.94 0.89 0.96 0.92 0.56 0.00 0.39 0.39 0.36 0.39 0.37 0.68 [8,] 0.91 0.92 0.85 0.92 0.85 0.52 0.39 0.00 0.35 0.33 0.41 0.33 0.60 [9,] 0.97 0.96 0.88 0.96 0.92 0.53 0.39 0.35 0.00 0.30 0.40 0.37 0.64 [10,] 0.92 0.92 0.85 0.92 0.90 0.47 0.36 0.33 0.30 0.00 0.35 0.29 0.65 [11,] 0.93 0.90 0.90 0.96 0.94 0.48 0.39 0.41 0.40 0.35 0.00 0.25 0.66 [12,] 0.92 0.89 0.85 0.92 0.92 0.48 0.37 0.33 0.37 0.29 0.25 0.00 0.64 [13,] 0.97 0.96 0.89 0.96 0.96 0.66 0.68 0.60 0.64 0.65 0.66 0.64 0.00 [14,] 0.97 0.93 0.90 0.96 0.96 0.71 0.67 0.68 0.67 0.70 0.70 0.71 0.17 [15,] 0.98 0.95 0.92 0.98 0.98 0.72 0.63 0.69 0.67 0.68 0.73 0.73 0.21 [16,] 0.97 0.96 0.90 0.96 0.96 0.71 0.67 0.65 0.68 0.69 0.68 0.67 0.11 [17,] 0.98 0.94 0.92 0.95 0.95 0.72 0.74 0.71 0.69 0.74 0.74 0.77 0.21 [18,] 0.97 0.93 0.88 0.96 0.94 0.68 0.66 0.63 0.67 0.68 0.63 0.63 0.21 [19,] 0.97 0.94 0.90 0.96 0.96 0.71 0.61 0.67 0.63 0.65 0.71 0.71 0.19 [,14] [,15] [,16] [,17] [,18] [,19] [1,] 0.97 0.98 0.97 0.98 0.97 0.97 [2,] 0.93 0.95 0.96 0.94 0.93 0.94 [3,] 0.90 0.92 0.90 0.92 0.88 0.90 [4,] 0.96 0.98 0.96 0.95 0.96 0.96 [5,] 0.96 0.98 0.96 0.95 0.94 0.96 [6,] 0.71 0.72 0.71 0.72 0.68 0.71 [7,] 0.67 0.63 0.67 0.74 0.66 0.61 [8,] 0.68 0.69 0.65 0.71 0.63 0.67 [9,] 0.67 0.67 0.68 0.69 0.67 0.63 [10,] 0.70 0.68 0.69 0.74 0.68 0.65 [11,] 0.70 0.73 0.68 0.74 0.63 0.71 [12,] 0.71 0.73 0.67 0.77 0.63 0.71 [13,] 0.17 0.21 0.11 0.21 0.21 0.19 [14,] 0.00 0.15 0.12 0.17 0.22 0.14 [15,] 0.15 0.00 0.16 0.27 0.29 0.13 [16,] 0.12 0.16 0.00 0.23 0.23 0.17 [17,] 0.17 0.27 0.23 0.00 0.26 0.21 [18,] 0.22 0.29 0.23 0.26 0.00 0.26 [19,] 0.14 0.13 0.17 0.21 0.26 0.00 > round(c4$dist,digits=2) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [1,] 0.00 0.24 0.30 0.24 0.32 0.91 0.93 0.89 0.97 0.91 0.92 0.91 0.97 [2,] 0.24 0.00 0.21 0.28 0.35 0.81 0.93 0.91 0.95 0.90 0.89 0.88 0.95 [3,] 0.30 0.21 0.00 0.17 0.29 0.83 0.87 0.82 0.86 0.82 0.89 0.83 0.88 [4,] 0.24 0.28 0.17 0.00 0.33 0.91 0.96 0.91 0.96 0.91 0.96 0.92 0.96 [5,] 0.32 0.35 0.29 0.33 0.00 0.89 0.91 0.85 0.92 0.89 0.93 0.92 0.96 [6,] 0.91 0.81 0.83 0.91 0.89 0.00 0.49 0.45 0.49 0.39 0.41 0.45 0.63 [7,] 0.93 0.93 0.87 0.96 0.91 0.49 0.00 0.31 0.33 0.26 0.31 0.32 0.65 [8,] 0.89 0.91 0.82 0.91 0.85 0.45 0.31 0.00 0.29 0.23 0.34 0.29 0.57 [9,] 0.97 0.95 0.86 0.96 0.92 0.49 0.33 0.29 0.00 0.23 0.36 0.35 0.63 [10,] 0.91 0.90 0.82 0.91 0.89 0.39 0.26 0.23 0.23 0.00 0.26 0.24 0.62 [11,] 0.92 0.89 0.89 0.96 0.93 0.41 0.31 0.34 0.36 0.26 0.00 0.21 0.64 [12,] 0.91 0.88 0.83 0.92 0.92 0.45 0.32 0.29 0.35 0.24 0.21 0.00 0.63 [13,] 0.97 0.95 0.88 0.96 0.96 0.63 0.65 0.57 0.63 0.62 0.64 0.63 0.00 [14,] 0.97 0.92 0.89 0.96 0.96 0.69 0.64 0.66 0.66 0.67 0.68 0.71 0.14 [15,] 0.98 0.95 0.90 0.98 0.98 0.69 0.60 0.66 0.66 0.65 0.71 0.72 0.18 [16,] 0.97 0.95 0.89 0.96 0.96 0.68 0.64 0.62 0.67 0.66 0.66 0.66 0.07 [17,] 0.98 0.93 0.90 0.95 0.95 0.68 0.70 0.67 0.66 0.71 0.71 0.76 0.15 [18,] 0.97 0.92 0.86 0.96 0.94 0.65 0.62 0.60 0.66 0.65 0.60 0.62 0.17 [19,] 0.97 0.94 0.88 0.96 0.96 0.68 0.56 0.64 0.62 0.62 0.69 0.70 0.16 [,14] [,15] [,16] [,17] [,18] [,19] [1,] 0.97 0.98 0.97 0.98 0.97 0.97 [2,] 0.92 0.95 0.95 0.93 0.92 0.94 [3,] 0.89 0.90 0.89 0.90 0.86 0.88 [4,] 0.96 0.98 0.96 0.95 0.96 0.96 [5,] 0.96 0.98 0.96 0.95 0.94 0.96 [6,] 0.69 0.69 0.68 0.68 0.65 0.68 [7,] 0.64 0.60 0.64 0.70 0.62 0.56 [8,] 0.66 0.66 0.62 0.67 0.60 0.64 [9,] 0.66 0.66 0.67 0.66 0.66 0.62 [10,] 0.67 0.65 0.66 0.71 0.65 0.62 [11,] 0.68 0.71 0.66 0.71 0.60 0.69 [12,] 0.71 0.72 0.66 0.76 0.62 0.70 [13,] 0.14 0.18 0.07 0.15 0.17 0.16 [14,] 0.00 0.11 0.08 0.11 0.18 0.10 [15,] 0.11 0.00 0.12 0.21 0.24 0.09 [16,] 0.08 0.12 0.00 0.17 0.18 0.13 [17,] 0.11 0.21 0.17 0.00 0.20 0.14 [18,] 0.18 0.24 0.18 0.20 0.00 0.21 [19,] 0.10 0.09 0.13 0.14 0.21 0.00 > round(c5$dist,digits=2) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [1,] 0.00 0.29 0.36 0.31 0.38 0.92 0.92 0.90 0.97 0.92 0.92 0.92 0.97 [2,] 0.29 0.00 0.26 0.32 0.40 0.81 0.92 0.92 0.96 0.92 0.89 0.89 0.96 [3,] 0.36 0.26 0.00 0.21 0.32 0.82 0.85 0.83 0.88 0.85 0.89 0.85 0.89 [4,] 0.31 0.32 0.21 0.00 0.33 0.92 0.94 0.92 0.96 0.92 0.96 0.92 0.96 [5,] 0.38 0.40 0.32 0.33 0.00 0.90 0.92 0.85 0.92 0.90 0.92 0.92 0.96 [6,] 0.92 0.81 0.82 0.92 0.90 0.00 0.46 0.48 0.50 0.44 0.46 0.48 0.67 [7,] 0.92 0.92 0.85 0.94 0.92 0.46 0.00 0.33 0.38 0.23 0.33 0.31 0.69 [8,] 0.90 0.92 0.83 0.92 0.85 0.48 0.33 0.00 0.33 0.27 0.33 0.33 0.62 [9,] 0.97 0.96 0.88 0.96 0.92 0.50 0.38 0.33 0.00 0.27 0.38 0.37 0.65 [10,] 0.92 0.92 0.85 0.92 0.90 0.44 0.23 0.27 0.27 0.00 0.29 0.29 0.65 [11,] 0.92 0.89 0.89 0.96 0.92 0.46 0.33 0.33 0.38 0.29 0.00 0.25 0.67 [12,] 0.92 0.89 0.85 0.92 0.92 0.48 0.31 0.33 0.37 0.29 0.25 0.00 0.65 [13,] 0.97 0.96 0.89 0.96 0.96 0.67 0.69 0.62 0.65 0.65 0.67 0.65 0.00 [14,] 0.97 0.93 0.89 0.96 0.96 0.71 0.65 0.67 0.67 0.67 0.69 0.71 0.15 [15,] 0.97 0.94 0.91 0.98 0.98 0.71 0.63 0.69 0.67 0.67 0.73 0.73 0.19 [16,] 0.97 0.96 0.89 0.96 0.96 0.69 0.65 0.63 0.67 0.67 0.65 0.65 0.10 [17,] 0.98 0.94 0.91 0.95 0.95 0.69 0.73 0.69 0.69 0.73 0.71 0.75 0.19 [18,] 0.97 0.92 0.87 0.96 0.94 0.65 0.65 0.63 0.67 0.67 0.63 0.63 0.21 [19,] 0.97 0.94 0.89 0.96 0.96 0.69 0.60 0.67 0.63 0.63 0.71 0.71 0.15 [,14] [,15] [,16] [,17] [,18] [,19] [1,] 0.97 0.97 0.97 0.98 0.97 0.97 [2,] 0.93 0.94 0.96 0.94 0.92 0.94 [3,] 0.89 0.91 0.89 0.91 0.87 0.89 [4,] 0.96 0.98 0.96 0.95 0.96 0.96 [5,] 0.96 0.98 0.96 0.95 0.94 0.96 [6,] 0.71 0.71 0.69 0.69 0.65 0.69 [7,] 0.65 0.63 0.65 0.73 0.65 0.60 [8,] 0.67 0.69 0.63 0.69 0.63 0.67 [9,] 0.67 0.67 0.67 0.69 0.67 0.63 [10,] 0.67 0.67 0.67 0.73 0.67 0.63 [11,] 0.69 0.73 0.65 0.71 0.63 0.71 [12,] 0.71 0.73 0.65 0.75 0.63 0.71 [13,] 0.15 0.19 0.10 0.19 0.21 0.15 [14,] 0.00 0.15 0.10 0.12 0.19 0.13 [15,] 0.15 0.00 0.15 0.27 0.25 0.12 [16,] 0.10 0.15 0.00 0.19 0.17 0.17 [17,] 0.12 0.27 0.19 0.00 0.19 0.17 [18,] 0.19 0.25 0.17 0.19 0.00 0.21 [19,] 0.13 0.12 0.17 0.17 0.21 0.00 > > > > cleanEx() > nameEx("comp.test") > ### * comp.test > > flush(stderr()); flush(stdout()) > > ### Name: comp.test > ### Title: Compare species clustering and species groups > ### Aliases: comp.test > ### Keywords: htest > > ### ** 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) Pearson's Chi-squared test with simulated p-value (based on 10000 replicates) data: cl and spg X-squared = 2.7, df = NA, p-value = 0.6 > > > > cleanEx() > nameEx("concomp") > ### * concomp > > flush(stderr()); flush(stdout()) > > ### Name: con.comp > ### Title: Connectivity components of an undirected graph > ### Aliases: con.comp > ### Keywords: array cluster > > ### ** 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 [1] 6 > plot(x,cc) > # The same should be produced by > # cutree(hclust(as.dist(m),method="single"),h=0.2). > > > > cleanEx() > nameEx("conregmat") > ### * conregmat > > flush(stderr()); flush(stdout()) > > ### Name: con.regmat > ### Title: Connected regions per species > ### Aliases: con.regmat > ### Keywords: spatial cluster > > ### ** 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)) Species 1 Clustered species 2 Clustered species 3 Clustered species 4 Clustered species 5 Clustered species 6 Clustered species 7 Clustered species 8 Clustered species 9 Clustered species 10 > con.regmat(cp,nb) [1] 4 3 2 2 3 4 3 1 5 5 > > > > cleanEx() > nameEx("coord2dist") > ### * coord2dist > > flush(stderr()); flush(stdout()) > > ### Name: coord2dist > ### Title: Geographical coordinates to distances > ### Aliases: coord2dist > ### Keywords: math > > ### ** Examples > > options(digits=4) > data(veronica) > coord2dist(coordmatrix=veronica.coord[1:20,], cut=20, file.format="decimal2",neighbors=TRUE) $distmatrix [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 0.000 230.50 5.384 3.162 3.162 230.50 201.54 236.98 236.98 236.98 [2,] 230.495 0.00 225.260 231.942 231.942 0.00 100.10 66.94 66.94 66.94 [3,] 5.384 225.26 0.000 7.809 7.809 225.26 197.39 232.32 232.32 232.32 [4,] 3.162 231.94 7.809 0.000 0.000 231.94 201.63 237.57 237.57 237.57 [5,] 3.162 231.94 7.809 0.000 0.000 231.94 201.63 237.57 237.57 237.57 [6,] 230.495 0.00 225.260 231.942 231.942 0.00 100.10 66.94 66.94 66.94 [7,] 201.542 100.10 197.389 201.632 201.632 100.10 0.00 50.11 50.11 50.11 [8,] 236.977 66.94 232.318 237.574 237.574 66.94 50.11 0.00 0.00 0.00 [9,] 236.977 66.94 232.318 237.574 237.574 66.94 50.11 0.00 0.00 0.00 [10,] 236.977 66.94 232.318 237.574 237.574 66.94 50.11 0.00 0.00 0.00 [11,] 236.977 66.94 232.318 237.574 237.574 66.94 50.11 0.00 0.00 0.00 [12,] 3.162 231.94 7.809 0.000 0.000 231.94 201.63 237.57 237.57 237.57 [13,] 3.162 231.94 7.809 0.000 0.000 231.94 201.63 237.57 237.57 237.57 [14,] 5.384 225.26 0.000 7.809 7.809 225.26 197.39 232.32 232.32 232.32 [15,] 5.384 225.26 0.000 7.809 7.809 225.26 197.39 232.32 232.32 232.32 [16,] 5.384 225.26 0.000 7.809 7.809 225.26 197.39 232.32 232.32 232.32 [17,] 244.141 46.81 239.240 245.034 245.034 46.81 71.44 24.40 24.40 24.40 [18,] 244.141 46.81 239.240 245.034 245.034 46.81 71.44 24.40 24.40 24.40 [19,] 244.141 46.81 239.240 245.034 245.034 46.81 71.44 24.40 24.40 24.40 [20,] 244.141 46.81 239.240 245.034 245.034 46.81 71.44 24.40 24.40 24.40 [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [1,] 236.98 3.162 3.162 5.384 5.384 5.384 244.14 244.14 244.14 [2,] 66.94 231.942 231.942 225.260 225.260 225.260 46.81 46.81 46.81 [3,] 232.32 7.809 7.809 0.000 0.000 0.000 239.24 239.24 239.24 [4,] 237.57 0.000 0.000 7.809 7.809 7.809 245.03 245.03 245.03 [5,] 237.57 0.000 0.000 7.809 7.809 7.809 245.03 245.03 245.03 [6,] 66.94 231.942 231.942 225.260 225.260 225.260 46.81 46.81 46.81 [7,] 50.11 201.632 201.632 197.389 197.389 197.389 71.44 71.44 71.44 [8,] 0.00 237.574 237.574 232.318 232.318 232.318 24.40 24.40 24.40 [9,] 0.00 237.574 237.574 232.318 232.318 232.318 24.40 24.40 24.40 [10,] 0.00 237.574 237.574 232.318 232.318 232.318 24.40 24.40 24.40 [11,] 0.00 237.574 237.574 232.318 232.318 232.318 24.40 24.40 24.40 [12,] 237.57 0.000 0.000 7.809 7.809 7.809 245.03 245.03 245.03 [13,] 237.57 0.000 0.000 7.809 7.809 7.809 245.03 245.03 245.03 [14,] 232.32 7.809 7.809 0.000 0.000 0.000 239.24 239.24 239.24 [15,] 232.32 7.809 7.809 0.000 0.000 0.000 239.24 239.24 239.24 [16,] 232.32 7.809 7.809 0.000 0.000 0.000 239.24 239.24 239.24 [17,] 24.40 245.034 245.034 239.240 239.240 239.240 0.00 0.00 0.00 [18,] 24.40 245.034 245.034 239.240 239.240 239.240 0.00 0.00 0.00 [19,] 24.40 245.034 245.034 239.240 239.240 239.240 0.00 0.00 0.00 [20,] 24.40 245.034 245.034 239.240 239.240 239.240 0.00 0.00 0.00 [,20] [1,] 244.14 [2,] 46.81 [3,] 239.24 [4,] 245.03 [5,] 245.03 [6,] 46.81 [7,] 71.44 [8,] 24.40 [9,] 24.40 [10,] 24.40 [11,] 24.40 [12,] 245.03 [13,] 245.03 [14,] 239.24 [15,] 239.24 [16,] 239.24 [17,] 0.00 [18,] 0.00 [19,] 0.00 [20,] 0.00 $nblist $nblist[[1]] [1] 3 4 5 12 13 14 15 16 $nblist[[2]] [1] 6 $nblist[[3]] [1] 1 4 5 12 13 14 15 16 $nblist[[4]] [1] 1 3 5 12 13 14 15 16 $nblist[[5]] [1] 1 3 4 12 13 14 15 16 $nblist[[6]] [1] 2 $nblist[[7]] numeric(0) $nblist[[8]] [1] 9 10 11 $nblist[[9]] [1] 8 10 11 $nblist[[10]] [1] 8 9 11 $nblist[[11]] [1] 8 9 10 $nblist[[12]] [1] 1 3 4 5 13 14 15 16 $nblist[[13]] [1] 1 3 4 5 12 14 15 16 $nblist[[14]] [1] 1 3 4 5 12 13 15 16 $nblist[[15]] [1] 1 3 4 5 12 13 14 16 $nblist[[16]] [1] 1 3 4 5 12 13 14 15 $nblist[[17]] [1] 18 19 20 $nblist[[18]] [1] 17 19 20 $nblist[[19]] [1] 17 18 20 $nblist[[20]] [1] 17 18 19 > > > > cleanEx() > nameEx("crmatrix") > ### * crmatrix > > flush(stderr()); flush(stdout()) > > ### Name: crmatrix > ### Title: Region-wise cluster membership > ### Aliases: crmatrix > ### Keywords: spatial cluster > > ### ** Examples > > > > > cleanEx() > nameEx("dicedist") > ### * dicedist > > flush(stderr()); flush(stdout()) > > ### Name: dicedist > ### Title: Dice distance matrix > ### Aliases: dicedist > ### Keywords: cluster spatial > > ### ** Examples > > options(digits=4) > data(kykladspecreg) > dicedist(t(kykladspecreg)) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 0.0000 0.9355 0.8750 0.9310 0.9310 0.9355 0.8182 0.5000 0.8710 0.7297 [2,] 0.9355 0.0000 0.7143 1.0000 1.0000 0.5000 0.7143 1.0000 1.0000 1.0000 [3,] 0.8750 0.7143 0.0000 1.0000 1.0000 0.5000 0.7143 1.0000 1.0000 1.0000 [4,] 0.9310 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 0.9000 1.0000 1.0000 [5,] 0.9310 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 0.9000 1.0000 0.7143 [6,] 0.9355 0.5000 0.5000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [7,] 0.8182 0.7143 0.7143 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [8,] 0.5000 1.0000 1.0000 0.9000 0.9000 1.0000 1.0000 0.0000 0.8182 0.6429 [9,] 0.8710 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8182 0.0000 0.7500 [10,] 0.7297 1.0000 1.0000 1.0000 0.7143 1.0000 1.0000 0.6429 0.7500 0.0000 [11,] 0.8750 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 0.9091 1.0000 1.0000 [12,] 0.7778 1.0000 0.7778 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 [13,] 0.4412 0.8519 0.8519 0.9167 0.9167 0.7857 1.0000 0.5094 0.8462 0.6875 [14,] 0.8710 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9048 1.0000 1.0000 [15,] 1.0000 0.6000 0.6000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 [16,] 0.7714 0.7500 0.7500 1.0000 1.0000 1.0000 0.4000 1.0000 1.0000 1.0000 [17,] 0.9310 1.0000 0.6000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 [18,] 0.9355 0.5000 0.5000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [19,] 0.7000 1.0000 1.0000 0.7778 1.0000 1.0000 1.0000 0.6129 0.8000 0.8462 [20,] 0.4925 0.8571 0.9259 0.9200 0.9200 0.8571 1.0000 0.4643 0.8519 0.7500 [21,] 0.4688 0.7692 0.7692 0.9091 0.9091 0.7692 0.7692 0.5833 0.8333 0.6667 [22,] 0.9333 0.6667 0.4286 1.0000 1.0000 0.4286 1.0000 1.0000 1.0000 1.0000 [23,] 0.9310 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 1.0000 0.6000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 [25,] 0.9310 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9000 1.0000 1.0000 [26,] 0.8182 0.7143 0.7143 1.0000 1.0000 0.7143 0.5000 1.0000 1.0000 1.0000 [27,] 0.8182 0.7143 0.7143 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [28,] 0.9310 1.0000 0.6000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 [29,] 0.9310 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [30,] 1.0000 0.6000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 [31,] 0.9310 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9000 1.0000 1.0000 [32,] 0.9310 1.0000 0.6000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 [33,] 0.9310 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 0.9310 1.0000 0.6000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 [35,] 0.9355 0.3333 0.7143 1.0000 1.0000 0.5000 0.7143 1.0000 1.0000 1.0000 [36,] 0.3882 0.8421 0.8421 0.9412 0.9412 0.8421 0.8421 0.4925 0.8889 0.7619 [37,] 0.9333 0.6667 0.6667 1.0000 1.0000 0.6667 1.0000 0.9048 0.6000 0.7500 [38,] 1.0000 0.6000 0.6000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 [39,] 0.9310 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 0.9355 0.5000 0.5000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [41,] 0.9355 0.5000 0.7143 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 [42,] 0.8235 1.0000 1.0000 0.6667 0.6667 1.0000 1.0000 0.7600 1.0000 0.8000 [43,] 0.9310 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [44,] 0.8182 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 0.7778 [45,] 0.9310 1.0000 0.6000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 [46,] 0.6087 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.5556 0.7143 0.6667 [47,] 1.0000 0.6000 0.6000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 [48,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [49,] 0.7714 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 0.8333 1.0000 0.8000 [50,] 1.0000 0.6000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 [51,] 0.9310 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9000 0.5000 1.0000 [52,] 0.8710 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9048 0.6000 1.0000 [53,] 0.7714 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 0.6923 0.5000 0.5000 [54,] 0.8710 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9048 1.0000 1.0000 [55,] 0.9310 1.0000 0.6000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 1.0000 [57,] 0.8182 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 0.4286 0.6000 [58,] 0.9310 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 0.7714 1.0000 1.0000 0.6667 0.6667 1.0000 1.0000 0.6923 1.0000 0.8000 [60,] 0.8788 0.5556 0.4000 1.0000 1.0000 0.4000 0.7500 1.0000 1.0000 1.0000 [61,] 0.8182 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8261 1.0000 0.6000 [62,] 1.0000 0.6000 0.6000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 [63,] 0.9310 1.0000 0.6000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 [64,] 0.8235 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7600 0.5000 0.6364 [65,] 0.9333 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 0.9048 1.0000 0.7500 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 1.0000 [67,] 0.8710 1.0000 1.0000 0.5000 0.5000 1.0000 1.0000 0.8182 1.0000 0.7500 [68,] 0.9310 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9000 1.0000 0.7143 [69,] 0.6279 1.0000 1.0000 0.8000 0.8000 1.0000 1.0000 0.5758 1.0000 0.8571 [70,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [71,] 0.8182 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8261 1.0000 0.7778 [72,] 0.9355 0.3333 0.7143 1.0000 1.0000 0.5000 0.7143 1.0000 1.0000 1.0000 [73,] 0.8710 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8182 0.3333 0.7500 [74,] 0.8710 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9048 1.0000 1.0000 [75,] 0.7838 0.5000 0.5000 1.0000 1.0000 0.5000 0.5000 1.0000 1.0000 1.0000 [76,] 0.9333 0.6667 0.4286 1.0000 1.0000 0.4286 1.0000 1.0000 1.0000 1.0000 [77,] 0.5510 1.0000 1.0000 0.8462 0.8462 1.0000 1.0000 0.5676 1.0000 0.6842 [78,] 0.9310 1.0000 0.6000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 [79,] 0.9310 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 0.8182 1.0000 0.7143 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [1,] 0.8750 0.7778 0.4412 0.8710 1.0000 0.7714 0.9310 0.9355 0.7000 0.4925 [2,] 1.0000 1.0000 0.8519 1.0000 0.6000 0.7500 1.0000 0.5000 1.0000 0.8571 [3,] 1.0000 0.7778 0.8519 1.0000 0.6000 0.7500 0.6000 0.5000 1.0000 0.9259 [4,] 0.6000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 1.0000 0.7778 0.9200 [5,] 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9200 [6,] 1.0000 1.0000 0.7857 1.0000 0.6000 1.0000 1.0000 0.3333 1.0000 0.8571 [7,] 1.0000 0.6000 1.0000 1.0000 1.0000 0.4000 0.6000 1.0000 1.0000 1.0000 [8,] 0.9091 1.0000 0.5094 0.9048 1.0000 1.0000 1.0000 1.0000 0.6129 0.4643 [9,] 1.0000 1.0000 0.8462 1.0000 1.0000 1.0000 1.0000 1.0000 0.8000 0.8519 [10,] 1.0000 1.0000 0.6875 1.0000 1.0000 1.0000 1.0000 1.0000 0.8462 0.7500 [11,] 0.0000 0.6000 0.8519 1.0000 1.0000 1.0000 1.0000 1.0000 0.8182 0.7931 [12,] 0.6000 0.0000 0.9286 1.0000 1.0000 0.6364 0.7143 1.0000 1.0000 0.8667 [13,] 0.8519 0.9286 0.0000 0.9200 0.9167 1.0000 1.0000 0.7857 0.8125 0.4516 [14,] 1.0000 1.0000 0.9200 0.0000 1.0000 1.0000 1.0000 1.0000 0.8000 0.8519 [15,] 1.0000 1.0000 0.9167 1.0000 0.0000 1.0000 1.0000 0.6000 1.0000 0.9200 [16,] 1.0000 0.6364 1.0000 1.0000 1.0000 0.0000 0.6667 1.0000 1.0000 1.0000 [17,] 1.0000 0.7143 1.0000 1.0000 1.0000 0.6667 0.0000 1.0000 1.0000 1.0000 [18,] 1.0000 1.0000 0.7857 1.0000 0.6000 1.0000 1.0000 0.0000 1.0000 0.8571 [19,] 0.8182 1.0000 0.8125 0.8000 1.0000 1.0000 1.0000 1.0000 0.0000 0.6216 [20,] 0.7931 0.8667 0.4516 0.8519 0.9200 1.0000 1.0000 0.8571 0.6216 0.0000 [21,] 0.8400 0.7241 0.4737 0.9130 0.9091 0.7778 0.9091 0.7692 0.8000 0.5088 [22,] 1.0000 1.0000 0.8462 1.0000 0.5000 1.0000 1.0000 0.4286 1.0000 0.9231 [23,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.7778 0.9200 [24,] 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 0.9200 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7778 0.9200 [26,] 1.0000 0.7778 0.9231 1.0000 1.0000 0.5556 1.0000 0.7143 1.0000 1.0000 [27,] 1.0000 0.6000 1.0000 1.0000 1.0000 0.4000 0.6000 1.0000 1.0000 1.0000 [28,] 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 [29,] 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9200 [30,] 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 0.9200 [31,] 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9200 [32,] 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 0.7143 1.0000 1.0000 1.0000 0.6667 0.3333 1.0000 1.0000 1.0000 [35,] 1.0000 1.0000 0.8519 1.0000 0.6000 0.7500 1.0000 0.5000 1.0000 0.8571 [36,] 0.8421 0.7619 0.4211 0.8889 0.9412 0.8462 0.9412 0.8421 0.6957 0.4103 [37,] 1.0000 1.0000 0.8462 1.0000 0.5000 1.0000 1.0000 0.6667 1.0000 0.8519 [38,] 1.0000 1.0000 0.9167 1.0000 0.3333 1.0000 1.0000 0.6000 1.0000 0.9200 [39,] 0.6000 0.7143 0.9167 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9200 [40,] 1.0000 1.0000 0.7857 1.0000 0.6000 1.0000 1.0000 0.3333 1.0000 0.8571 [41,] 1.0000 1.0000 0.7857 1.0000 0.6000 1.0000 1.0000 0.5000 1.0000 0.8571 [42,] 0.5556 0.8000 0.7931 1.0000 1.0000 1.0000 1.0000 1.0000 0.8333 0.8000 [43,] 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9200 [44,] 1.0000 1.0000 0.7857 1.0000 1.0000 1.0000 1.0000 1.0000 0.8182 0.7931 [45,] 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 [46,] 1.0000 1.0000 0.5610 0.8462 1.0000 1.0000 1.0000 1.0000 0.7000 0.5714 [47,] 1.0000 1.0000 0.9167 1.0000 0.3333 1.0000 1.0000 0.6000 1.0000 0.9200 [48,] 0.6000 0.7143 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9200 [49,] 1.0000 1.0000 0.7333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7419 [50,] 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 0.9200 [51,] 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 1.0000 0.7778 0.9200 [52,] 1.0000 1.0000 0.9200 0.6000 1.0000 1.0000 1.0000 1.0000 0.6364 0.8519 [53,] 1.0000 1.0000 0.7333 1.0000 1.0000 1.0000 1.0000 1.0000 0.8333 0.7419 [54,] 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 0.6364 0.8519 [55,] 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8182 0.8571 [57,] 1.0000 1.0000 0.7857 1.0000 1.0000 1.0000 1.0000 1.0000 0.8182 0.8571 [58,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.7778 0.9200 [59,] 0.7500 1.0000 0.7333 0.7143 1.0000 1.0000 1.0000 1.0000 0.8333 0.7419 [60,] 1.0000 0.8000 0.7931 1.0000 0.6667 0.7778 0.6667 0.4000 1.0000 0.8621 [61,] 1.0000 1.0000 0.8519 0.6667 1.0000 1.0000 1.0000 1.0000 0.6667 0.8571 [62,] 1.0000 1.0000 0.9167 1.0000 0.3333 1.0000 1.0000 0.6000 1.0000 0.9200 [63,] 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 [64,] 1.0000 1.0000 0.7333 1.0000 1.0000 1.0000 1.0000 1.0000 0.8333 0.8000 [65,] 0.6667 0.7500 0.9200 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8519 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8182 0.8571 [67,] 0.6667 1.0000 0.8462 1.0000 1.0000 1.0000 1.0000 1.0000 0.8000 0.8519 [68,] 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9200 [69,] 0.8333 1.0000 0.5789 0.8182 1.0000 1.0000 1.0000 1.0000 0.8750 0.6316 [70,] 0.6000 0.7143 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9200 [71,] 0.7143 0.7778 0.7857 1.0000 1.0000 1.0000 1.0000 1.0000 0.8182 0.7931 [72,] 1.0000 1.0000 0.8519 1.0000 0.6000 0.7500 1.0000 0.5000 1.0000 0.8571 [73,] 1.0000 1.0000 0.8462 1.0000 1.0000 1.0000 1.0000 1.0000 0.8000 0.8519 [74,] 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 0.6364 0.8519 [75,] 1.0000 0.6923 0.8065 1.0000 0.7500 0.5385 0.7500 0.5000 1.0000 0.8710 [76,] 1.0000 1.0000 0.8462 1.0000 0.5000 1.0000 1.0000 0.4286 1.0000 0.9231 [77,] 0.7500 0.8824 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8000 0.5814 [78,] 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 [79,] 0.6000 0.7143 0.9167 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9200 [80,] 1.0000 0.6000 1.0000 1.0000 1.0000 0.4000 0.6000 1.0000 1.0000 1.0000 [,21] [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30] [1,] 0.4688 0.9333 0.9310 1.0000 0.9310 0.8182 0.8182 0.9310 0.9310 1.0000 [2,] 0.7692 0.6667 1.0000 0.6000 1.0000 0.7143 0.7143 1.0000 1.0000 0.6000 [3,] 0.7692 0.4286 1.0000 1.0000 1.0000 0.7143 0.7143 0.6000 1.0000 1.0000 [4,] 0.9091 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [5,] 0.9091 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [6,] 0.7692 0.4286 1.0000 0.6000 1.0000 0.7143 1.0000 0.6000 1.0000 0.6000 [7,] 0.7692 1.0000 1.0000 1.0000 1.0000 0.5000 0.3333 1.0000 1.0000 1.0000 [8,] 0.5833 1.0000 1.0000 1.0000 0.9000 1.0000 1.0000 1.0000 1.0000 1.0000 [9,] 0.8333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [10,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [11,] 0.8400 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [12,] 0.7241 1.0000 1.0000 1.0000 1.0000 0.7778 0.6000 1.0000 1.0000 1.0000 [13,] 0.4737 0.8462 1.0000 0.9167 1.0000 0.9231 1.0000 0.9167 0.9167 0.9167 [14,] 0.9130 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [15,] 0.9091 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [16,] 0.7778 1.0000 1.0000 1.0000 1.0000 0.5556 0.4000 1.0000 1.0000 1.0000 [17,] 0.9091 1.0000 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 [18,] 0.7692 0.4286 1.0000 0.6000 1.0000 0.7143 1.0000 0.6000 1.0000 0.6000 [19,] 0.8000 1.0000 0.7778 1.0000 0.7778 1.0000 1.0000 1.0000 1.0000 1.0000 [20,] 0.5088 0.9231 0.9200 0.9200 0.9200 1.0000 1.0000 1.0000 0.9200 0.9200 [21,] 0.0000 0.8333 1.0000 0.9091 1.0000 0.7692 0.7692 0.9091 0.9091 0.9091 [22,] 0.8333 0.0000 1.0000 1.0000 1.0000 0.6667 1.0000 0.5000 1.0000 1.0000 [23,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 0.9091 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 [25,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 0.7692 0.6667 1.0000 1.0000 1.0000 0.0000 0.5000 0.6000 1.0000 1.0000 [27,] 0.7692 1.0000 1.0000 1.0000 1.0000 0.5000 0.0000 1.0000 1.0000 1.0000 [28,] 0.9091 0.5000 1.0000 1.0000 1.0000 0.6000 1.0000 0.0000 1.0000 1.0000 [29,] 0.9091 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 [30,] 0.9091 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [32,] 0.9091 0.5000 1.0000 1.0000 1.0000 0.6000 1.0000 0.3333 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 0.9091 1.0000 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 [35,] 0.7692 0.6667 1.0000 0.6000 1.0000 0.7143 0.7143 1.0000 1.0000 0.6000 [36,] 0.4444 0.8889 0.9412 0.9412 0.9412 0.8421 0.8421 0.9412 0.9412 0.9412 [37,] 0.8333 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [38,] 0.9091 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 0.7692 0.4286 1.0000 0.6000 1.0000 0.7143 1.0000 0.6000 1.0000 0.6000 [41,] 0.8400 0.6667 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6000 [42,] 0.7778 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [43,] 0.9091 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 [44,] 0.7692 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [45,] 0.9091 0.5000 1.0000 1.0000 1.0000 0.6000 1.0000 0.3333 1.0000 1.0000 [46,] 0.5385 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8333 1.0000 [47,] 0.9091 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [48,] 0.9091 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [49,] 0.7143 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 [50,] 0.9091 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 [51,] 0.9091 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [52,] 0.9130 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [53,] 0.7143 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [54,] 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [55,] 0.9091 0.5000 1.0000 1.0000 1.0000 0.6000 1.0000 0.3333 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [57,] 0.7692 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [58,] 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 0.7778 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [60,] 0.7143 0.5000 1.0000 0.6667 1.0000 0.7500 0.7500 0.6667 1.0000 0.6667 [61,] 0.8400 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [62,] 0.9091 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [63,] 0.9091 0.5000 1.0000 1.0000 1.0000 0.6000 1.0000 0.3333 1.0000 1.0000 [64,] 0.7778 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [65,] 0.8333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [67,] 0.8333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [68,] 0.9091 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [69,] 0.6471 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [70,] 0.9091 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [71,] 0.8400 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [72,] 0.7692 0.6667 1.0000 0.6000 1.0000 0.7143 0.7143 1.0000 1.0000 0.6000 [73,] 0.8333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 0.5000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 [75,] 0.6250 0.6000 1.0000 0.7500 1.0000 0.5000 0.5000 0.7500 1.0000 0.7500 [76,] 0.8333 0.3333 1.0000 1.0000 1.0000 0.6667 1.0000 0.5000 1.0000 1.0000 [77,] 0.6316 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8462 1.0000 [78,] 0.9091 0.5000 1.0000 1.0000 1.0000 0.6000 1.0000 0.3333 1.0000 1.0000 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 0.8400 1.0000 1.0000 1.0000 1.0000 0.7143 0.5000 1.0000 1.0000 1.0000 [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] [,39] [,40] [1,] 0.9310 0.9310 0.9310 0.9310 0.9355 0.3882 0.9333 1.0000 0.9310 0.9355 [2,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.8421 0.6667 0.6000 1.0000 0.5000 [3,] 1.0000 0.6000 1.0000 0.6000 0.7143 0.8421 0.6667 0.6000 1.0000 0.5000 [4,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 1.0000 [5,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 1.0000 [6,] 1.0000 0.6000 1.0000 1.0000 0.5000 0.8421 0.6667 0.6000 1.0000 0.3333 [7,] 1.0000 1.0000 1.0000 0.6000 0.7143 0.8421 1.0000 1.0000 1.0000 1.0000 [8,] 0.9000 1.0000 1.0000 1.0000 1.0000 0.4925 0.9048 1.0000 1.0000 1.0000 [9,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 0.6000 1.0000 1.0000 1.0000 [10,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.7619 0.7500 1.0000 1.0000 1.0000 [11,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8421 1.0000 1.0000 0.6000 1.0000 [12,] 1.0000 1.0000 1.0000 0.7143 1.0000 0.7619 1.0000 1.0000 0.7143 1.0000 [13,] 0.9167 0.9167 1.0000 1.0000 0.8519 0.4211 0.8462 0.9167 0.9167 0.7857 [14,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 1.0000 1.0000 1.0000 1.0000 [15,] 1.0000 1.0000 1.0000 1.0000 0.6000 0.9412 0.5000 0.3333 1.0000 0.6000 [16,] 1.0000 1.0000 0.6667 0.6667 0.7500 0.8462 1.0000 1.0000 1.0000 1.0000 [17,] 1.0000 1.0000 1.0000 0.3333 1.0000 0.9412 1.0000 1.0000 1.0000 1.0000 [18,] 1.0000 0.6000 1.0000 1.0000 0.5000 0.8421 0.6667 0.6000 1.0000 0.3333 [19,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.6957 1.0000 1.0000 1.0000 1.0000 [20,] 0.9200 1.0000 1.0000 1.0000 0.8571 0.4103 0.8519 0.9200 0.9200 0.8571 [21,] 1.0000 0.9091 1.0000 0.9091 0.7692 0.4444 0.8333 0.9091 1.0000 0.7692 [22,] 1.0000 0.5000 1.0000 1.0000 0.6667 0.8889 0.6000 0.5000 1.0000 0.4286 [23,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 1.0000 [24,] 1.0000 1.0000 1.0000 1.0000 0.6000 0.9412 1.0000 1.0000 1.0000 0.6000 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 0.6000 1.0000 1.0000 0.7143 0.8421 1.0000 1.0000 1.0000 0.7143 [27,] 1.0000 1.0000 1.0000 0.6000 0.7143 0.8421 1.0000 1.0000 1.0000 1.0000 [28,] 1.0000 0.3333 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 0.6000 [29,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 1.0000 [30,] 1.0000 1.0000 1.0000 1.0000 0.6000 0.9412 1.0000 1.0000 1.0000 0.6000 [31,] 0.0000 1.0000 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 1.0000 [32,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 0.6000 [33,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 1.0000 1.0000 0.0000 1.0000 0.9412 1.0000 1.0000 1.0000 1.0000 [35,] 1.0000 1.0000 1.0000 1.0000 0.0000 0.8421 0.6667 0.6000 1.0000 0.5000 [36,] 0.9412 0.9412 1.0000 0.9412 0.8421 0.0000 0.8889 0.9412 0.9412 0.8421 [37,] 1.0000 1.0000 1.0000 1.0000 0.6667 0.8889 0.0000 0.5000 1.0000 0.6667 [38,] 1.0000 1.0000 1.0000 1.0000 0.6000 0.9412 0.5000 0.0000 1.0000 0.6000 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 0.0000 1.0000 [40,] 1.0000 0.6000 1.0000 1.0000 0.5000 0.8421 0.6667 0.6000 1.0000 0.0000 [41,] 1.0000 1.0000 1.0000 1.0000 0.5000 0.8421 0.6667 0.6000 1.0000 0.5000 [42,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8000 1.0000 1.0000 1.0000 1.0000 [43,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 1.0000 [44,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8421 1.0000 1.0000 1.0000 1.0000 [45,] 1.0000 0.3333 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 0.6000 [46,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.6154 0.8462 1.0000 1.0000 1.0000 [47,] 1.0000 1.0000 1.0000 1.0000 0.6000 0.9412 0.5000 0.3333 1.0000 0.6000 [48,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 1.0000 [49,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8000 1.0000 1.0000 1.0000 1.0000 [50,] 1.0000 1.0000 1.0000 1.0000 0.6000 0.9412 1.0000 1.0000 1.0000 0.6000 [51,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 1.0000 [52,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 1.0000 1.0000 1.0000 1.0000 [53,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8000 0.7143 1.0000 1.0000 1.0000 [54,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 1.0000 1.0000 1.0000 1.0000 [55,] 1.0000 0.3333 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 0.6000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8919 1.0000 1.0000 1.0000 1.0000 [57,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8421 0.6667 1.0000 1.0000 1.0000 [58,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 1.0000 [59,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8000 1.0000 1.0000 1.0000 1.0000 [60,] 1.0000 0.6667 1.0000 0.6667 0.5556 0.8000 0.7143 0.6667 1.0000 0.4000 [61,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8421 1.0000 1.0000 1.0000 1.0000 [62,] 1.0000 1.0000 1.0000 1.0000 0.6000 0.9412 0.5000 0.3333 1.0000 0.6000 [63,] 1.0000 0.3333 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 0.6000 [64,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8000 0.7143 1.0000 1.0000 1.0000 [65,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 1.0000 1.0000 1.0000 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8919 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 1.0000 1.0000 1.0000 1.0000 [68,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 1.0000 [69,] 0.8000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 [70,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 1.0000 [71,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8421 1.0000 1.0000 0.6000 1.0000 [72,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.8421 0.6667 0.6000 1.0000 0.5000 [73,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 0.6000 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 1.0000 1.0000 1.0000 1.0000 [75,] 1.0000 0.7500 1.0000 0.7500 0.5000 0.7273 0.7778 0.7500 1.0000 0.5000 [76,] 1.0000 0.5000 1.0000 1.0000 0.6667 0.8889 0.6000 0.5000 1.0000 0.4286 [77,] 0.8462 1.0000 1.0000 1.0000 1.0000 0.5926 1.0000 1.0000 0.8462 1.0000 [78,] 1.0000 0.3333 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 1.0000 0.6000 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9412 1.0000 1.0000 0.3333 1.0000 [80,] 1.0000 1.0000 0.6000 0.6000 1.0000 0.8919 1.0000 1.0000 1.0000 1.0000 [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50] [1,] 0.9355 0.8235 0.9310 0.8182 0.9310 0.6087 1.0000 1.0000 0.7714 1.0000 [2,] 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 0.6000 [3,] 0.7143 1.0000 1.0000 1.0000 0.6000 1.0000 0.6000 1.0000 1.0000 1.0000 [4,] 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [5,] 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 [6,] 0.5000 1.0000 1.0000 1.0000 0.6000 1.0000 0.6000 1.0000 1.0000 0.6000 [7,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [8,] 1.0000 0.7600 1.0000 0.7500 1.0000 0.5556 1.0000 1.0000 0.8333 1.0000 [9,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.7143 1.0000 1.0000 1.0000 1.0000 [10,] 1.0000 0.8000 1.0000 0.7778 1.0000 0.6667 1.0000 1.0000 0.8000 1.0000 [11,] 1.0000 0.5556 1.0000 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 [12,] 1.0000 0.8000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7143 1.0000 1.0000 [13,] 0.7857 0.7931 0.9167 0.7857 0.9167 0.5610 0.9167 1.0000 0.7333 0.9167 [14,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8462 1.0000 1.0000 1.0000 1.0000 [15,] 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [16,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [17,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [18,] 0.5000 1.0000 1.0000 1.0000 0.6000 1.0000 0.6000 1.0000 1.0000 0.6000 [19,] 1.0000 0.8333 1.0000 0.8182 1.0000 0.7000 1.0000 1.0000 1.0000 1.0000 [20,] 0.8571 0.8000 0.9200 0.7931 1.0000 0.5714 0.9200 0.9200 0.7419 0.9200 [21,] 0.8400 0.7778 0.9091 0.7692 0.9091 0.5385 0.9091 0.9091 0.7143 0.9091 [22,] 0.6667 1.0000 1.0000 1.0000 0.5000 1.0000 0.5000 1.0000 1.0000 1.0000 [23,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 [27,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [28,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 [29,] 1.0000 1.0000 0.3333 1.0000 1.0000 0.8333 1.0000 1.0000 0.6667 1.0000 [30,] 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [32,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [35,] 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 0.6000 [36,] 0.8421 0.8000 0.9412 0.8421 0.9412 0.6154 0.9412 0.9412 0.8000 0.9412 [37,] 0.6667 1.0000 1.0000 1.0000 1.0000 0.8462 0.5000 1.0000 1.0000 1.0000 [38,] 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 0.5000 1.0000 1.0000 1.0000 0.6000 1.0000 0.6000 1.0000 1.0000 0.6000 [41,] 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 0.6000 [42,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 0.7778 1.0000 [43,] 1.0000 1.0000 0.0000 1.0000 1.0000 0.8333 1.0000 1.0000 0.6667 1.0000 [44,] 1.0000 1.0000 1.0000 0.0000 1.0000 0.6250 1.0000 1.0000 0.7500 1.0000 [45,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [46,] 1.0000 1.0000 0.8333 0.6250 1.0000 0.0000 1.0000 1.0000 0.6471 1.0000 [47,] 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [48,] 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [49,] 1.0000 0.7778 0.6667 0.7500 1.0000 0.6471 1.0000 1.0000 0.0000 1.0000 [50,] 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [51,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8333 1.0000 1.0000 1.0000 1.0000 [52,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8462 1.0000 1.0000 1.0000 1.0000 [53,] 1.0000 0.7778 1.0000 1.0000 1.0000 0.7500 1.0000 1.0000 0.7778 1.0000 [54,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [55,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8571 1.0000 1.0000 1.0000 1.0000 [57,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.6250 1.0000 1.0000 1.0000 1.0000 [58,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 1.0000 0.6000 1.0000 1.0000 1.0000 0.8667 1.0000 1.0000 0.7778 1.0000 [60,] 0.5556 1.0000 1.0000 1.0000 0.6667 1.0000 0.6667 1.0000 1.0000 0.6667 [61,] 1.0000 1.0000 1.0000 0.7143 1.0000 0.7333 1.0000 1.0000 1.0000 1.0000 [62,] 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [63,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 [64,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.6471 1.0000 1.0000 1.0000 1.0000 [65,] 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 0.5000 0.7143 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8571 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7143 1.0000 [68,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [69,] 1.0000 0.6000 1.0000 0.6923 1.0000 0.6364 1.0000 1.0000 0.6000 1.0000 [70,] 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 [71,] 1.0000 1.0000 1.0000 0.5000 1.0000 0.7333 1.0000 1.0000 1.0000 1.0000 [72,] 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 0.6000 [73,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.7143 1.0000 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [75,] 0.6364 1.0000 1.0000 1.0000 0.7500 1.0000 0.7500 1.0000 1.0000 0.7500 [76,] 0.6667 1.0000 1.0000 1.0000 0.5000 1.0000 0.5000 1.0000 1.0000 1.0000 [77,] 1.0000 0.6667 0.8462 0.6471 1.0000 0.6800 1.0000 1.0000 0.6667 1.0000 [78,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [1,] 0.9310 0.8710 0.7714 0.8710 0.9310 1.0000 0.8182 0.9310 0.7714 0.8788 [2,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.5556 [3,] 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 0.4000 [4,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 [5,] 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 [6,] 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 0.4000 [7,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [8,] 0.9000 0.9048 0.6923 0.9048 1.0000 0.7500 0.7500 1.0000 0.6923 1.0000 [9,] 0.5000 0.6000 0.5000 1.0000 1.0000 1.0000 0.4286 1.0000 1.0000 1.0000 [10,] 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 0.6000 1.0000 0.8000 1.0000 [11,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 [12,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8000 [13,] 0.9167 0.9200 0.7333 1.0000 0.9167 1.0000 0.7857 1.0000 0.7333 0.7931 [14,] 1.0000 0.6000 1.0000 0.6000 1.0000 1.0000 1.0000 0.5000 0.7143 1.0000 [15,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 [16,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7778 [17,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 [18,] 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 0.4000 [19,] 0.7778 0.6364 0.8333 0.6364 1.0000 0.8182 0.8182 0.7778 0.8333 1.0000 [20,] 0.9200 0.8519 0.7419 0.8519 1.0000 0.8571 0.8571 0.9200 0.7419 0.8621 [21,] 0.9091 0.9130 0.7143 1.0000 0.9091 1.0000 0.7692 1.0000 0.7778 0.7143 [22,] 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.5000 [23,] 1.0000 0.5000 1.0000 0.5000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 [24,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 0.7500 [27,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [28,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 0.6667 [29,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [30,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [32,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 0.6667 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 [35,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.5556 [36,] 0.9412 0.8889 0.8000 0.8889 0.9412 0.8919 0.8421 0.9412 0.8000 0.8000 [37,] 1.0000 1.0000 0.7143 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 0.7143 [38,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 0.4000 [41,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.5556 [42,] 1.0000 1.0000 0.7778 1.0000 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 [43,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [44,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [45,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 0.6667 [46,] 0.8333 0.8462 0.7500 1.0000 1.0000 0.8571 0.6250 1.0000 0.8667 1.0000 [47,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 [48,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [49,] 1.0000 1.0000 0.7778 1.0000 1.0000 1.0000 1.0000 1.0000 0.7778 1.0000 [50,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 [51,] 0.0000 0.5000 0.6667 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 [52,] 0.5000 0.0000 0.7143 0.6000 1.0000 1.0000 0.6667 0.5000 1.0000 1.0000 [53,] 0.6667 0.7143 0.0000 1.0000 1.0000 1.0000 0.5556 1.0000 0.7778 1.0000 [54,] 1.0000 0.6000 1.0000 0.0000 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 [55,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.6667 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [57,] 0.6000 0.6667 0.5556 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [58,] 1.0000 0.5000 1.0000 0.5000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [59,] 1.0000 1.0000 0.7778 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 [60,] 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 0.0000 [61,] 1.0000 0.6667 1.0000 0.6667 1.0000 1.0000 0.7143 0.6000 1.0000 1.0000 [62,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 [63,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 0.6667 [64,] 0.6667 0.7143 0.6000 1.0000 1.0000 1.0000 0.4000 1.0000 1.0000 1.0000 [65,] 1.0000 1.0000 0.7143 1.0000 1.0000 1.0000 1.0000 1.0000 0.7143 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 1.0000 0.7143 1.0000 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 [68,] 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [69,] 1.0000 1.0000 0.8462 1.0000 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 [70,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [71,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [72,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.5556 [73,] 0.5000 0.6000 0.5000 1.0000 1.0000 1.0000 0.4286 1.0000 1.0000 1.0000 [74,] 1.0000 0.6000 1.0000 0.6000 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 [75,] 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 0.4286 [76,] 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.5000 [77,] 1.0000 1.0000 0.7647 1.0000 1.0000 1.0000 1.0000 1.0000 0.7647 1.0000 [78,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 0.6667 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [,61] [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [1,] 0.8182 1.0000 0.9310 0.8235 0.9333 1.0000 0.8710 0.9310 0.6279 1.0000 [2,] 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [3,] 1.0000 0.6000 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [4,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 0.8000 1.0000 [5,] 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 0.5000 1.0000 0.8000 1.0000 [6,] 1.0000 0.6000 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [7,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [8,] 0.8261 1.0000 1.0000 0.7600 0.9048 0.7500 0.8182 0.9000 0.5758 1.0000 [9,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [10,] 0.6000 1.0000 1.0000 0.6364 0.7500 1.0000 0.7500 0.7143 0.8571 1.0000 [11,] 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 0.6667 1.0000 0.8333 0.6000 [12,] 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 0.7143 [13,] 0.8519 0.9167 0.9167 0.7333 0.9200 1.0000 0.8462 0.9167 0.5789 1.0000 [14,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8182 1.0000 [15,] 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [16,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [17,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [18,] 1.0000 0.6000 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [19,] 0.6667 1.0000 1.0000 0.8333 1.0000 0.8182 0.8000 1.0000 0.8750 1.0000 [20,] 0.8571 0.9200 1.0000 0.8000 0.8519 0.8571 0.8519 0.9200 0.6316 0.9200 [21,] 0.8400 0.9091 0.9091 0.7778 0.8333 1.0000 0.8333 0.9091 0.6471 0.9091 [22,] 1.0000 0.5000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [23,] 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [27,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [28,] 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [29,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [30,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8000 1.0000 [32,] 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [35,] 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [36,] 0.8421 0.9412 0.9412 0.8000 0.8889 0.8919 0.8889 0.9412 0.6667 0.9412 [37,] 1.0000 0.5000 1.0000 0.7143 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [38,] 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 1.0000 0.6000 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [41,] 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [42,] 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 0.5000 1.0000 0.6000 0.6667 [43,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [44,] 0.7143 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6923 1.0000 [45,] 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [46,] 0.7333 1.0000 1.0000 0.6471 1.0000 0.8571 1.0000 1.0000 0.6364 1.0000 [47,] 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [48,] 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.3333 [49,] 1.0000 1.0000 1.0000 1.0000 0.7143 1.0000 0.7143 1.0000 0.6000 1.0000 [50,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [51,] 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [52,] 0.6667 1.0000 1.0000 0.7143 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [53,] 1.0000 1.0000 1.0000 0.6000 0.7143 1.0000 0.7143 0.6667 0.8462 1.0000 [54,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [55,] 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [57,] 0.7143 1.0000 1.0000 0.4000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [58,] 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 1.0000 1.0000 1.0000 1.0000 0.7143 1.0000 0.5000 1.0000 0.6000 1.0000 [60,] 1.0000 0.6667 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [61,] 0.0000 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [62,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [63,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [64,] 0.7500 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [65,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 0.6000 1.0000 0.8182 0.5000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 1.0000 1.0000 1.0000 0.6000 1.0000 0.0000 1.0000 0.6667 1.0000 [68,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [69,] 1.0000 1.0000 1.0000 1.0000 0.8182 1.0000 0.6667 1.0000 0.0000 1.0000 [70,] 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.0000 [71,] 0.7143 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8333 1.0000 [72,] 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [73,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [74,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [75,] 1.0000 0.7500 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [76,] 1.0000 0.5000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [77,] 0.8667 1.0000 1.0000 1.0000 0.8571 1.0000 0.7333 0.8462 0.5200 1.0000 [78,] 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [,71] [,72] [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [1,] 0.8182 0.9355 0.8710 0.8710 0.7838 0.9333 0.5510 0.9310 0.9310 0.8182 [2,] 1.0000 0.3333 1.0000 1.0000 0.5000 0.6667 1.0000 1.0000 1.0000 1.0000 [3,] 1.0000 0.7143 1.0000 1.0000 0.5000 0.4286 1.0000 0.6000 1.0000 0.7143 [4,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8462 1.0000 1.0000 1.0000 [5,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8462 1.0000 1.0000 1.0000 [6,] 1.0000 0.5000 1.0000 1.0000 0.5000 0.4286 1.0000 0.6000 1.0000 1.0000 [7,] 1.0000 0.7143 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.5000 [8,] 0.8261 1.0000 0.8182 0.9048 1.0000 1.0000 0.5676 1.0000 1.0000 1.0000 [9,] 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [10,] 0.7778 1.0000 0.7500 1.0000 1.0000 1.0000 0.6842 1.0000 1.0000 1.0000 [11,] 0.7143 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 0.6000 1.0000 [12,] 0.7778 1.0000 1.0000 1.0000 0.6923 1.0000 0.8824 1.0000 0.7143 0.6000 [13,] 0.7857 0.8519 0.8462 1.0000 0.8065 0.8462 0.5000 0.9167 0.9167 1.0000 [14,] 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [15,] 1.0000 0.6000 1.0000 1.0000 0.7500 0.5000 1.0000 1.0000 1.0000 1.0000 [16,] 1.0000 0.7500 1.0000 1.0000 0.5385 1.0000 1.0000 1.0000 1.0000 0.4000 [17,] 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 0.6000 [18,] 1.0000 0.5000 1.0000 1.0000 0.5000 0.4286 1.0000 0.6000 1.0000 1.0000 [19,] 0.8182 1.0000 0.8000 0.6364 1.0000 1.0000 0.8000 1.0000 1.0000 1.0000 [20,] 0.7931 0.8571 0.8519 0.8519 0.8710 0.9231 0.5814 1.0000 0.9200 1.0000 [21,] 0.8400 0.7692 0.8333 1.0000 0.6250 0.8333 0.6316 0.9091 1.0000 0.8400 [22,] 1.0000 0.6667 1.0000 1.0000 0.6000 0.3333 1.0000 0.5000 1.0000 1.0000 [23,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 1.0000 0.6000 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 [25,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 0.7143 1.0000 1.0000 0.5000 0.6667 1.0000 0.6000 1.0000 0.7143 [27,] 1.0000 0.7143 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.5000 [28,] 1.0000 1.0000 1.0000 1.0000 0.7500 0.5000 1.0000 0.3333 1.0000 1.0000 [29,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8462 1.0000 1.0000 1.0000 [30,] 1.0000 0.6000 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8462 1.0000 1.0000 1.0000 [32,] 1.0000 1.0000 1.0000 1.0000 0.7500 0.5000 1.0000 0.3333 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6000 [34,] 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 0.6000 [35,] 1.0000 0.3333 1.0000 1.0000 0.5000 0.6667 1.0000 1.0000 1.0000 1.0000 [36,] 0.8421 0.8421 0.8889 0.8889 0.7273 0.8889 0.5926 0.9412 0.9412 0.8919 [37,] 1.0000 0.6667 0.6000 1.0000 0.7778 0.6000 1.0000 1.0000 1.0000 1.0000 [38,] 1.0000 0.6000 1.0000 1.0000 0.7500 0.5000 1.0000 1.0000 1.0000 1.0000 [39,] 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8462 1.0000 0.3333 1.0000 [40,] 1.0000 0.5000 1.0000 1.0000 0.5000 0.4286 1.0000 0.6000 1.0000 1.0000 [41,] 1.0000 0.5000 1.0000 1.0000 0.6364 0.6667 1.0000 1.0000 1.0000 1.0000 [42,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 [43,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8462 1.0000 1.0000 1.0000 [44,] 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6471 1.0000 1.0000 1.0000 [45,] 1.0000 1.0000 1.0000 1.0000 0.7500 0.5000 1.0000 0.3333 1.0000 1.0000 [46,] 0.7333 1.0000 0.7143 1.0000 1.0000 1.0000 0.6800 1.0000 1.0000 1.0000 [47,] 1.0000 0.6000 1.0000 1.0000 0.7500 0.5000 1.0000 1.0000 1.0000 1.0000 [48,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [49,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 [50,] 1.0000 0.6000 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 [51,] 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [52,] 1.0000 1.0000 0.6000 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [53,] 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 0.7647 1.0000 1.0000 1.0000 [54,] 1.0000 1.0000 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [55,] 1.0000 1.0000 1.0000 1.0000 0.7500 0.5000 1.0000 0.3333 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [57,] 1.0000 1.0000 0.4286 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [58,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7647 1.0000 1.0000 1.0000 [60,] 1.0000 0.5556 1.0000 1.0000 0.4286 0.5000 1.0000 0.6667 1.0000 0.7500 [61,] 0.7143 1.0000 1.0000 0.6667 1.0000 1.0000 0.8667 1.0000 1.0000 1.0000 [62,] 1.0000 0.6000 1.0000 1.0000 0.7500 0.5000 1.0000 1.0000 1.0000 1.0000 [63,] 1.0000 1.0000 1.0000 1.0000 0.7500 0.5000 1.0000 0.3333 1.0000 1.0000 [64,] 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [65,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8571 1.0000 1.0000 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7333 1.0000 1.0000 1.0000 [68,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8462 1.0000 1.0000 1.0000 [69,] 0.8333 1.0000 1.0000 1.0000 1.0000 1.0000 0.5200 1.0000 1.0000 1.0000 [70,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [71,] 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6471 1.0000 0.6000 1.0000 [72,] 1.0000 0.0000 1.0000 1.0000 0.5000 0.6667 1.0000 1.0000 1.0000 1.0000 [73,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [75,] 1.0000 0.5000 1.0000 1.0000 0.0000 0.6000 1.0000 0.7500 1.0000 0.6364 [76,] 1.0000 0.6667 1.0000 1.0000 0.6000 0.0000 1.0000 0.5000 1.0000 1.0000 [77,] 0.6471 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 0.8462 1.0000 [78,] 1.0000 1.0000 1.0000 1.0000 0.7500 0.5000 1.0000 0.0000 1.0000 1.0000 [79,] 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8462 1.0000 0.0000 1.0000 [80,] 1.0000 1.0000 1.0000 1.0000 0.6364 1.0000 1.0000 1.0000 1.0000 0.0000 > > > > cleanEx() > nameEx("distratio") > ### * distratio > > flush(stderr()); flush(stdout()) > > ### Name: distratio > ### Title: Distance ratio test statistics for distance based clustering > ### Aliases: distratio > ### Keywords: cluster > > ### ** Examples > > options(digits=4) > data(kykladspecreg) > j <- jaccard(t(kykladspecreg)) > distratio(j) $dr [1] 0.7159 $lowmean [1] 0.7159 $himean [1] 1 $prop [1] 0.25 > > > > cleanEx() > nameEx("geco") > ### * geco > > flush(stderr()); flush(stdout()) > > ### Name: geco > ### Title: geco distance matrix > ### Aliases: geco > ### Keywords: cluster spatial > > ### ** Examples > > options(digits=4) > data(kykladspecreg) > data(waterdist) > geco(t(kykladspecreg),waterdist) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [1,] 0.00000 0.67310 0.50129 0.4740 0.4257 0.69805 0.42541 0.2620 0.45859 [2,] 0.67310 0.00000 0.44444 1.0000 1.0000 0.27083 0.32870 1.0000 1.00000 [3,] 0.50129 0.44444 0.00000 1.0000 1.0000 0.23611 0.38194 1.0000 1.00000 [4,] 0.47402 1.00000 1.00000 0.0000 1.0000 1.00000 1.00000 0.4637 1.00000 [5,] 0.42567 1.00000 1.00000 1.0000 0.0000 1.00000 1.00000 0.4093 1.00000 [6,] 0.69805 0.27083 0.23611 1.0000 1.0000 0.00000 0.75000 1.0000 1.00000 [7,] 0.42541 0.32870 0.38194 1.0000 1.0000 0.75000 0.00000 1.0000 1.00000 [8,] 0.26196 1.00000 1.00000 0.4637 0.4093 1.00000 1.00000 0.0000 0.44444 [9,] 0.45859 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 0.4444 0.00000 [10,] 0.32948 1.00000 1.00000 1.0000 0.4000 1.00000 1.00000 0.2635 0.43472 [11,] 0.62217 1.00000 1.00000 0.3333 1.0000 1.00000 1.00000 0.7971 1.00000 [12,] 0.49095 0.61481 0.54537 1.0000 1.0000 0.79537 0.32315 1.0000 1.00000 [13,] 0.16215 0.61269 0.52399 0.4681 0.4088 0.43182 0.86795 0.2859 0.44066 [14,] 0.41821 1.00000 1.00000 1.0000 0.5625 1.00000 1.00000 0.6046 1.00000 [15,] 0.68184 0.33333 0.17361 1.0000 1.0000 0.27083 0.59491 1.0000 1.00000 [16,] 0.42155 0.35764 0.36169 1.0000 1.0000 0.73148 0.02604 1.0000 1.00000 [17,] 0.44599 0.54861 0.19676 1.0000 1.0000 0.57176 0.18519 1.0000 1.00000 [18,] 0.69805 0.27083 0.23611 1.0000 1.0000 0.00000 0.75000 1.0000 1.00000 [19,] 0.42637 1.00000 1.00000 0.4286 1.0000 1.00000 1.00000 0.3049 0.46329 [20,] 0.21135 0.62319 0.65187 0.4783 0.4396 0.56069 0.87389 0.1889 0.44837 [21,] 0.17256 0.37951 0.40278 0.4750 0.4597 0.40799 0.40451 0.3868 0.45000 [22,] 0.56610 0.48958 0.06944 1.0000 1.0000 0.16667 0.68171 1.0000 1.00000 [23,] 0.47659 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 0.8746 1.00000 [24,] 1.00000 0.33333 1.00000 1.0000 1.0000 0.33333 1.00000 1.0000 1.00000 [25,] 0.47531 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 0.4695 1.00000 [26,] 0.42155 0.39815 0.37037 1.0000 1.0000 0.58796 0.20833 1.0000 1.00000 [27,] 0.42541 0.32870 0.38194 1.0000 1.0000 0.75000 0.00000 1.0000 1.00000 [28,] 0.47659 0.75000 0.23148 1.0000 1.0000 0.27083 0.84259 1.0000 1.00000 [29,] 0.48148 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 1.0000 1.00000 [30,] 1.00000 0.33333 1.00000 1.0000 1.0000 0.33333 1.00000 1.0000 1.00000 [31,] 0.44444 1.00000 1.00000 1.0000 0.3472 1.00000 1.00000 0.4375 1.00000 [32,] 0.47659 0.75000 0.23148 1.0000 1.0000 0.27083 0.84259 1.0000 1.00000 [33,] 0.44213 0.54861 0.40509 1.0000 1.0000 0.74537 0.25463 1.0000 1.00000 [34,] 0.44599 0.54861 0.19676 1.0000 1.0000 0.57176 0.18519 1.0000 1.00000 [35,] 0.67310 0.00000 0.44444 1.0000 1.0000 0.27083 0.32870 1.0000 1.00000 [36,] 0.07092 0.42470 0.43924 0.4781 0.4373 0.44249 0.44032 0.2024 0.45920 [37,] 0.53884 0.58333 0.42361 1.0000 1.0000 0.52083 0.74074 0.6983 0.28472 [38,] 0.68184 0.33333 0.17361 1.0000 1.0000 0.27083 0.59491 1.0000 1.00000 [39,] 0.48148 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 1.0000 1.00000 [40,] 0.69805 0.27083 0.23611 1.0000 1.0000 0.00000 0.75000 1.0000 1.00000 [41,] 0.68493 0.33333 0.50694 1.0000 1.0000 0.27083 0.78935 0.9514 0.87731 [42,] 0.52186 1.00000 1.00000 0.3368 0.3108 1.00000 1.00000 0.4931 1.00000 [43,] 0.48148 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 1.0000 1.00000 [44,] 0.43004 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 0.4051 1.00000 [45,] 0.47659 0.75000 0.23148 1.0000 1.0000 0.27083 0.84259 1.0000 1.00000 [46,] 0.32986 1.00000 1.00000 1.0000 0.6778 1.00000 1.00000 0.2394 0.40000 [47,] 0.68184 0.33333 0.17361 1.0000 1.0000 0.27083 0.59491 1.0000 1.00000 [48,] 1.00000 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 1.0000 1.00000 [49,] 0.36368 1.00000 1.00000 1.0000 0.3715 1.00000 1.00000 0.5395 1.00000 [50,] 1.00000 0.33333 1.00000 1.0000 1.0000 0.33333 1.00000 1.0000 1.00000 [51,] 0.46373 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 0.4483 0.03472 [52,] 0.44033 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 0.6319 0.28472 [53,] 0.36574 1.00000 1.00000 1.0000 0.3750 1.00000 1.00000 0.3260 0.25000 [54,] 0.45859 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 0.6566 1.00000 [55,] 0.47659 0.75000 0.23148 1.0000 1.0000 0.27083 0.84259 1.0000 1.00000 [56,] 0.94830 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 0.4120 1.00000 [57,] 0.43647 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 0.4113 0.16667 [58,] 0.47659 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 0.8746 1.00000 [59,] 0.39043 1.00000 1.00000 0.3750 0.2378 1.00000 1.00000 0.3565 1.00000 [60,] 0.60892 0.23438 0.12500 1.0000 1.0000 0.05208 0.45775 1.0000 1.00000 [61,] 0.43956 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 0.5563 1.00000 [62,] 0.68184 0.33333 0.17361 1.0000 1.0000 0.27083 0.59491 1.0000 1.00000 [63,] 0.47659 0.75000 0.23148 1.0000 1.0000 0.27083 0.84259 1.0000 1.00000 [64,] 0.44226 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 0.4894 0.20312 [65,] 0.67567 1.00000 1.00000 1.0000 0.2500 1.00000 1.00000 0.6593 1.00000 [66,] 0.94830 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 0.4120 1.00000 [67,] 0.40715 1.00000 1.00000 0.2500 0.2500 1.00000 1.00000 0.3816 1.00000 [68,] 0.48148 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 0.4722 1.00000 [69,] 0.31996 1.00000 1.00000 0.4184 0.3264 1.00000 1.00000 0.3014 1.00000 [70,] 1.00000 1.00000 1.00000 1.0000 1.0000 1.00000 1.00000 1.0000 1.00000 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0.33333 1.00000 1.0000 0.41667 1.00000 1.0000 1.0000 1.0000 [31,] 1.0000 1.0000 1.00000 1.00000 1.0000 1.00000 1.00000 0.3990 1.0000 1.0000 [32,] 1.0000 1.0000 0.75000 1.00000 1.0000 0.36574 0.15625 1.0000 0.0000 1.0000 [33,] 1.0000 1.0000 0.54861 1.00000 1.0000 0.39583 0.71181 1.0000 0.9722 1.0000 [34,] 1.0000 1.0000 0.54861 1.00000 1.0000 0.27431 0.50347 1.0000 0.7639 1.0000 [35,] 1.0000 1.0000 0.00000 1.00000 1.0000 0.09838 0.48958 1.0000 0.7500 1.0000 [36,] 0.4844 0.4412 0.42470 0.45920 0.4668 0.40625 0.45812 0.2951 0.4748 0.4844 [37,] 1.0000 1.0000 0.58333 0.28472 1.0000 0.57870 0.40625 1.0000 0.7188 1.0000 [38,] 1.0000 1.0000 0.33333 1.00000 1.0000 0.32870 0.15625 1.0000 0.6250 1.0000 [39,] 1.0000 0.3333 1.00000 1.00000 1.0000 1.00000 1.00000 0.4545 1.0000 0.0000 [40,] 1.0000 1.0000 0.27083 1.00000 1.0000 0.19329 0.16667 1.0000 0.2708 1.0000 [41,] 1.0000 1.0000 0.33333 0.87731 1.0000 0.41204 0.48958 1.0000 0.7500 1.0000 [42,] 0.3750 1.0000 1.00000 1.00000 1.0000 1.00000 1.00000 0.4230 1.0000 1.0000 [43,] 1.0000 1.0000 1.00000 1.00000 1.0000 1.00000 1.00000 0.4545 1.0000 1.0000 [44,] 1.0000 0.2708 1.00000 1.00000 1.0000 1.00000 1.00000 0.3624 1.0000 1.0000 [45,] 1.0000 1.0000 0.75000 1.00000 1.0000 0.36574 0.15625 1.0000 0.0000 1.0000 [46,] 1.0000 0.5368 1.00000 0.40000 0.9708 1.00000 1.00000 0.4848 1.0000 1.0000 [47,] 1.0000 1.0000 0.33333 1.00000 1.0000 0.32870 0.15625 1.0000 0.6250 1.0000 [48,] 0.0000 1.0000 1.00000 1.00000 1.0000 1.00000 1.00000 1.0000 1.0000 1.0000 [49,] 1.0000 0.8872 1.00000 1.00000 1.0000 1.00000 1.00000 0.3041 1.0000 1.0000 [50,] 1.0000 1.0000 0.33333 1.00000 1.0000 0.41667 1.00000 1.0000 1.0000 1.0000 [51,] 1.0000 1.0000 1.00000 0.03472 1.0000 1.00000 1.00000 1.0000 1.0000 1.0000 [52,] 1.0000 1.0000 1.00000 0.28472 0.4410 1.00000 1.00000 1.0000 1.0000 1.0000 [53,] 1.0000 1.0000 1.00000 0.25000 1.0000 1.00000 1.00000 0.5732 1.0000 1.0000 [54,] 1.0000 1.0000 1.00000 1.00000 0.4340 1.00000 1.00000 1.0000 1.0000 1.0000 [55,] 1.0000 1.0000 0.75000 1.00000 1.0000 0.36574 0.15625 1.0000 0.0000 1.0000 [56,] 1.0000 0.9236 1.00000 1.00000 1.0000 1.00000 1.00000 0.9438 1.0000 1.0000 [57,] 1.0000 0.9676 1.00000 0.16667 0.9595 1.00000 1.00000 0.9794 1.0000 1.0000 [58,] 1.0000 1.0000 1.00000 1.00000 0.1910 1.00000 1.00000 1.0000 1.0000 1.0000 [59,] 1.0000 1.0000 1.00000 1.00000 1.0000 1.00000 1.00000 0.4361 1.0000 1.0000 [60,] 1.0000 1.0000 0.23438 1.00000 1.0000 0.09259 0.17708 1.0000 0.2986 1.0000 [61,] 1.0000 0.6505 1.00000 1.00000 0.5081 1.00000 1.00000 0.7717 1.0000 1.0000 [62,] 1.0000 1.0000 0.33333 1.00000 1.0000 0.32870 0.15625 1.0000 0.6250 1.0000 [63,] 1.0000 1.0000 0.75000 1.00000 1.0000 0.36574 0.15625 1.0000 0.0000 1.0000 [64,] 1.0000 0.9716 1.00000 0.20312 0.9635 1.00000 1.00000 0.9834 1.0000 1.0000 [65,] 0.2500 1.0000 1.00000 1.00000 1.0000 1.00000 1.00000 0.6187 1.0000 1.0000 [66,] 1.0000 0.9236 1.00000 1.00000 1.0000 1.00000 1.00000 0.9438 1.0000 1.0000 [67,] 1.0000 1.0000 1.00000 1.00000 1.0000 1.00000 1.00000 0.3232 1.0000 1.0000 [68,] 1.0000 1.0000 1.00000 1.00000 1.0000 1.00000 1.00000 0.4545 1.0000 1.0000 [69,] 1.0000 0.7457 1.00000 1.00000 1.0000 1.00000 1.00000 0.2390 1.0000 1.0000 [70,] 0.0000 1.0000 1.00000 1.00000 1.0000 1.00000 1.00000 1.0000 1.0000 1.0000 [71,] 1.0000 0.0000 1.00000 1.00000 1.0000 1.00000 1.00000 0.3466 1.0000 0.3333 [72,] 1.0000 1.0000 0.00000 1.00000 1.0000 0.09838 0.48958 1.0000 0.7500 1.0000 [73,] 1.0000 1.0000 1.00000 0.00000 1.0000 1.00000 1.00000 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 1.00000 1.00000 0.0000 1.00000 1.00000 1.0000 1.0000 1.0000 [75,] 1.0000 1.0000 0.09838 1.00000 1.0000 0.00000 0.27662 1.0000 0.3657 1.0000 [76,] 1.0000 1.0000 0.48958 1.00000 1.0000 0.27662 0.00000 1.0000 0.1562 1.0000 [77,] 1.0000 0.3466 1.00000 1.00000 1.0000 1.00000 1.00000 0.0000 1.0000 0.4545 [78,] 1.0000 1.0000 0.75000 1.00000 1.0000 0.36574 0.15625 1.0000 0.0000 1.0000 [79,] 1.0000 0.3333 1.00000 1.00000 1.0000 1.00000 1.00000 0.4545 1.0000 0.0000 [80,] 1.0000 1.0000 0.42130 1.00000 1.0000 0.22801 0.61921 1.0000 0.8380 1.0000 [,80] [1,] 0.42413 [2,] 0.42130 [3,] 0.31250 [4,] 1.00000 [5,] 1.00000 [6,] 0.68750 [7,] 0.05787 [8,] 1.00000 [9,] 1.00000 [10,] 1.00000 [11,] 1.00000 [12,] 0.33472 [13,] 0.80545 [14,] 1.00000 [15,] 0.53241 [16,] 0.01736 [17,] 0.11574 [18,] 0.68750 [19,] 1.00000 [20,] 0.81139 [21,] 0.44271 [22,] 0.61921 [23,] 1.00000 [24,] 1.00000 [25,] 1.00000 [26,] 0.27778 [27,] 0.05787 [28,] 0.83796 [29,] 1.00000 [30,] 1.00000 [31,] 1.00000 [32,] 0.83796 [33,] 0.08102 [34,] 0.11574 [35,] 0.42130 [36,] 0.47721 [37,] 0.67824 [38,] 0.53241 [39,] 1.00000 [40,] 0.68750 [41,] 0.72685 [42,] 1.00000 [43,] 1.00000 [44,] 1.00000 [45,] 0.83796 [46,] 1.00000 [47,] 0.53241 [48,] 1.00000 [49,] 1.00000 [50,] 1.00000 [51,] 1.00000 [52,] 1.00000 [53,] 1.00000 [54,] 1.00000 [55,] 0.83796 [56,] 1.00000 [57,] 1.00000 [58,] 1.00000 [59,] 1.00000 [60,] 0.38831 [61,] 1.00000 [62,] 0.53241 [63,] 0.83796 [64,] 1.00000 [65,] 1.00000 [66,] 1.00000 [67,] 1.00000 [68,] 1.00000 [69,] 1.00000 [70,] 1.00000 [71,] 1.00000 [72,] 0.42130 [73,] 1.00000 [74,] 1.00000 [75,] 0.22801 [76,] 0.61921 [77,] 1.00000 [78,] 0.83796 [79,] 1.00000 [80,] 0.00000 > > > > cleanEx() > nameEx("geo2neighbor") > ### * geo2neighbor > > flush(stderr()); flush(stdout()) > > ### Name: geo2neighbor > ### Title: Neighborhood list from geographical distance > ### Aliases: geo2neighbor > ### Keywords: cluster spatial > > ### ** Examples > > data(waterdist) > geo2neighbor(waterdist) [[1]] [1] 2 3 4 [[2]] [1] 1 3 4 [[3]] [1] 1 2 4 [[4]] [1] 1 2 3 [[5]] [1] 6 [[6]] [1] 5 7 [[7]] [1] 6 9 [[8]] numeric(0) [[9]] [1] 7 10 [[10]] [1] 9 11 12 13 [[11]] [1] 10 12 13 [[12]] [1] 10 11 13 [[13]] [1] 10 11 12 [[14]] [1] 15 16 [[15]] [1] 14 16 17 [[16]] [1] 14 15 17 20 [[17]] [1] 15 16 18 19 20 21 22 [[18]] [1] 17 19 20 21 22 [[19]] [1] 17 18 20 21 22 [[20]] [1] 16 17 18 19 21 22 [[21]] [1] 17 18 19 20 22 23 [[22]] [1] 17 18 19 20 21 23 [[23]] [1] 21 22 [[24]] numeric(0) [[25]] numeric(0) [[26]] numeric(0) [[27]] [1] 29 32 33 [[28]] numeric(0) [[29]] [1] 27 31 32 33 [[30]] [1] 31 32 33 [[31]] [1] 29 30 32 33 [[32]] [1] 27 29 30 31 33 [[33]] [1] 27 29 30 31 32 [[34]] numeric(0) > > > > cleanEx() > nameEx("homogen.test") > ### * homogen.test > > flush(stderr()); flush(stdout()) > > ### Name: homogen.test > ### Title: Classical distance-based test for homogeneity against clustering > ### Aliases: homogen.test > ### Keywords: cluster htest > > ### ** Examples > > options(digits=4) > data(kykladspecreg) > j <- jaccard(t(kykladspecreg)) > homogen.test(j, testdist="erdos") $p [1] 0.6095 $p.twoside [1] 0.8641 $iv [1] 10 $lambda [1] 10.56 $distcut [1] 0.5 $ne [1] 80 > homogen.test(j, testdist="ling") [1] "Computation of Ling-probabilities..." [1] "finished." $p [1] 0.6164 $iv [1] 10 $distcut [1] 0.5 $ne [1] 80 > > > > cleanEx() > nameEx("hprabclust") > ### * hprabclust > > flush(stderr()); flush(stdout()) > > ### Name: hprabclust > ### Title: Clustering of species ranges from presence-absence matrices > ### (hierarchical methods) > ### Aliases: hprabclust print.comprabclust > ### Keywords: cluster spatial > > ### ** Examples > > data(kykladspecreg) > data(nb) > data(waterdist) > x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb, + geodist=waterdist, distance="geco") > hprabclust(x,mdsplot=FALSE) * Clustered presence-absence matrix * Clustered by hclust with noise detection, method=: average Distance value to cut tree: 0.4 Minimum cluster size (below is noise): 3 Call: hprabclust(prabobj = x, mdsplot = FALSE) Cluster memberships: [1] 2 1 1 0 9 1 3 2 5 10 0 3 2 6 1 3 3 1 0 2 2 1 6 7 0 [26] 4 3 4 0 7 0 4 3 3 1 2 5 1 0 1 7 8 0 0 4 2 1 8 2 7 [51] 5 0 10 6 4 0 5 6 9 1 0 1 4 5 8 0 9 10 2 8 0 1 5 6 1 [76] 1 2 4 0 3 > > > > cleanEx() > nameEx("incmatrix") > ### * incmatrix > > flush(stderr()); flush(stdout()) > > ### Name: incmatrix > ### Title: Nestedness matrix > ### Aliases: incmatrix > ### Keywords: spatial array > > ### ** Examples > > data(kykladspecreg) > incmatrix(t(kykladspecreg))$ninc [1] 602 > > > > cleanEx() > nameEx("jaccard") > ### * jaccard > > flush(stderr()); flush(stdout()) > > ### Name: jaccard > ### Title: Jaccard distance matrix > ### Aliases: jaccard > ### Keywords: cluster spatial > > ### ** Examples > > options(digits=4) > data(kykladspecreg) > jaccard(t(kykladspecreg)) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 0.0000 0.9655 0.9286 0.9630 0.9630 0.9655 0.8889 0.5000 0.9259 0.8148 [2,] 0.9655 0.0000 0.8000 1.0000 1.0000 0.5000 0.8000 1.0000 1.0000 1.0000 [3,] 0.9286 0.8000 0.0000 1.0000 1.0000 0.5000 0.8000 1.0000 1.0000 1.0000 [4,] 0.9630 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 0.9444 1.0000 1.0000 [5,] 0.9630 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 0.9444 1.0000 0.8000 [6,] 0.9655 0.5000 0.5000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [7,] 0.8889 0.8000 0.8000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [8,] 0.5000 1.0000 1.0000 0.9444 0.9444 1.0000 1.0000 0.0000 0.8889 0.7222 [9,] 0.9259 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 0.0000 0.8333 [10,] 0.8148 1.0000 1.0000 1.0000 0.8000 1.0000 1.0000 0.7222 0.8333 0.0000 [11,] 0.9286 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 0.9500 1.0000 1.0000 [12,] 0.8571 1.0000 0.8571 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 [13,] 0.3667 0.9130 0.9130 0.9545 0.9545 0.8636 1.0000 0.5185 0.9091 0.7727 [14,] 0.9259 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9474 1.0000 1.0000 [15,] 1.0000 0.6667 0.6667 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 [16,] 0.8519 0.8333 0.8333 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 [17,] 0.9630 1.0000 0.6667 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 [18,] 0.9655 0.5000 0.5000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [19,] 0.7857 1.0000 1.0000 0.8571 1.0000 1.0000 1.0000 0.6842 0.8750 0.9091 [20,] 0.4848 0.9167 0.9600 0.9565 0.9565 0.9167 1.0000 0.4231 0.9130 0.8333 [21,] 0.4333 0.8500 0.8500 0.9500 0.9500 0.8500 0.8500 0.6429 0.9000 0.7500 [22,] 0.9643 0.7500 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [23,] 0.9630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 1.0000 0.6667 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 [25,] 0.9630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9444 1.0000 1.0000 [26,] 0.8889 0.8000 0.8000 1.0000 1.0000 0.8000 0.5000 1.0000 1.0000 1.0000 [27,] 0.8889 0.8000 0.8000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [28,] 0.9630 1.0000 0.6667 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 [29,] 0.9630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [30,] 1.0000 0.6667 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 [31,] 0.9630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9444 1.0000 1.0000 [32,] 0.9630 1.0000 0.6667 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 [33,] 0.9630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 0.9630 1.0000 0.6667 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 [35,] 0.9655 0.0000 0.8000 1.0000 1.0000 0.5000 0.8000 1.0000 1.0000 1.0000 [36,] 0.2121 0.9062 0.9062 0.9688 0.9688 0.9062 0.9062 0.4848 0.9375 0.8438 [37,] 0.9643 0.7500 0.7500 1.0000 1.0000 0.7500 1.0000 0.9474 0.6667 0.8333 [38,] 1.0000 0.6667 0.6667 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 [39,] 0.9630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 0.9655 0.5000 0.5000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [41,] 0.9655 0.5000 0.8000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 [42,] 0.8929 1.0000 1.0000 0.7500 0.7500 1.0000 1.0000 0.8421 1.0000 0.8750 [43,] 0.9630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [44,] 0.8889 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8333 1.0000 0.8571 [45,] 0.9630 1.0000 0.6667 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 [46,] 0.6786 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6000 0.8000 0.7500 [47,] 1.0000 0.6667 0.6667 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 [48,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [49,] 0.8519 1.0000 1.0000 1.0000 0.7500 1.0000 1.0000 0.9000 1.0000 0.8750 [50,] 1.0000 0.6667 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 [51,] 0.9630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9444 0.5000 1.0000 [52,] 0.9259 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9474 0.6667 1.0000 [53,] 0.8519 1.0000 1.0000 1.0000 0.7500 1.0000 1.0000 0.7778 0.5000 0.5000 [54,] 0.9259 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9474 1.0000 1.0000 [55,] 0.9630 1.0000 0.6667 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8333 1.0000 1.0000 [57,] 0.8889 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8333 0.3333 0.6667 [58,] 0.9630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 0.8519 1.0000 1.0000 0.7500 0.7500 1.0000 1.0000 0.7778 1.0000 0.8750 [60,] 0.9310 0.6000 0.2500 1.0000 1.0000 0.2500 0.8333 1.0000 1.0000 1.0000 [61,] 0.8889 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8947 1.0000 0.6667 [62,] 1.0000 0.6667 0.6667 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 [63,] 0.9630 1.0000 0.6667 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 [64,] 0.8929 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8421 0.5000 0.7143 [65,] 0.9643 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 0.9474 1.0000 0.8333 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8333 1.0000 1.0000 [67,] 0.9259 1.0000 1.0000 0.5000 0.5000 1.0000 1.0000 0.8889 1.0000 0.8333 [68,] 0.9630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9444 1.0000 0.8000 [69,] 0.7037 1.0000 1.0000 0.8750 0.8750 1.0000 1.0000 0.6316 1.0000 0.9167 [70,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [71,] 0.8889 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8947 1.0000 0.8571 [72,] 0.9655 0.0000 0.8000 1.0000 1.0000 0.5000 0.8000 1.0000 1.0000 1.0000 [73,] 0.9259 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 0.0000 0.8333 [74,] 0.9259 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9474 1.0000 1.0000 [75,] 0.8621 0.5000 0.5000 1.0000 1.0000 0.5000 0.5000 1.0000 1.0000 1.0000 [76,] 0.9643 0.7500 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [77,] 0.5926 1.0000 1.0000 0.9091 0.9091 1.0000 1.0000 0.6190 1.0000 0.7692 [78,] 0.9630 1.0000 0.6667 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 [79,] 0.9630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 0.8889 1.0000 0.8000 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [1,] 0.9286 0.8571 0.3667 0.9259 1.0000 0.8519 0.9630 0.9655 0.7857 0.4848 [2,] 1.0000 1.0000 0.9130 1.0000 0.6667 0.8333 1.0000 0.5000 1.0000 0.9167 [3,] 1.0000 0.8571 0.9130 1.0000 0.6667 0.8333 0.6667 0.5000 1.0000 0.9600 [4,] 0.6667 1.0000 0.9545 1.0000 1.0000 1.0000 1.0000 1.0000 0.8571 0.9565 [5,] 1.0000 1.0000 0.9545 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9565 [6,] 1.0000 1.0000 0.8636 1.0000 0.6667 1.0000 1.0000 0.0000 1.0000 0.9167 [7,] 1.0000 0.6667 1.0000 1.0000 1.0000 0.2500 0.6667 1.0000 1.0000 1.0000 [8,] 0.9500 1.0000 0.5185 0.9474 1.0000 1.0000 1.0000 1.0000 0.6842 0.4231 [9,] 1.0000 1.0000 0.9091 1.0000 1.0000 1.0000 1.0000 1.0000 0.8750 0.9130 [10,] 1.0000 1.0000 0.7727 1.0000 1.0000 1.0000 1.0000 1.0000 0.9091 0.8333 [11,] 0.0000 0.6667 0.9130 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 0.8696 [12,] 0.6667 0.0000 0.9615 1.0000 1.0000 0.7143 0.8000 1.0000 1.0000 0.9231 [13,] 0.9130 0.9615 0.0000 0.9565 0.9545 1.0000 1.0000 0.8636 0.8846 0.3929 [14,] 1.0000 1.0000 0.9565 0.0000 1.0000 1.0000 1.0000 1.0000 0.8750 0.9130 [15,] 1.0000 1.0000 0.9545 1.0000 0.0000 1.0000 1.0000 0.6667 1.0000 0.9565 [16,] 1.0000 0.7143 1.0000 1.0000 1.0000 0.0000 0.7500 1.0000 1.0000 1.0000 [17,] 1.0000 0.8000 1.0000 1.0000 1.0000 0.7500 0.0000 1.0000 1.0000 1.0000 [18,] 1.0000 1.0000 0.8636 1.0000 0.6667 1.0000 1.0000 0.0000 1.0000 0.9167 [19,] 0.8889 1.0000 0.8846 0.8750 1.0000 1.0000 1.0000 1.0000 0.0000 0.6957 [20,] 0.8696 0.9231 0.3929 0.9130 0.9565 1.0000 1.0000 0.9167 0.6957 0.0000 [21,] 0.9048 0.8095 0.4444 0.9524 0.9500 0.8571 0.9500 0.8500 0.8750 0.5172 [22,] 1.0000 1.0000 0.9091 1.0000 0.5000 1.0000 1.0000 0.3333 1.0000 0.9583 [23,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.8571 0.9565 [24,] 1.0000 1.0000 0.9545 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 0.9565 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8571 0.9565 [26,] 1.0000 0.8571 0.9583 1.0000 1.0000 0.6000 1.0000 0.8000 1.0000 1.0000 [27,] 1.0000 0.6667 1.0000 1.0000 1.0000 0.2500 0.6667 1.0000 1.0000 1.0000 [28,] 1.0000 1.0000 0.9545 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 [29,] 1.0000 1.0000 0.9545 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9565 [30,] 1.0000 1.0000 0.9545 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 0.9565 [31,] 1.0000 1.0000 0.9545 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9565 [32,] 1.0000 1.0000 0.9545 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 0.8000 1.0000 1.0000 1.0000 0.7500 0.0000 1.0000 1.0000 1.0000 [35,] 1.0000 1.0000 0.9130 1.0000 0.6667 0.8333 1.0000 0.5000 1.0000 0.9167 [36,] 0.9062 0.8438 0.3125 0.9375 0.9688 0.9091 0.9688 0.9062 0.7812 0.2812 [37,] 1.0000 1.0000 0.9091 1.0000 0.5000 1.0000 1.0000 0.7500 1.0000 0.9130 [38,] 1.0000 1.0000 0.9545 1.0000 0.0000 1.0000 1.0000 0.6667 1.0000 0.9565 [39,] 0.6667 0.8000 0.9545 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9565 [40,] 1.0000 1.0000 0.8636 1.0000 0.6667 1.0000 1.0000 0.0000 1.0000 0.9167 [41,] 1.0000 1.0000 0.8636 1.0000 0.6667 1.0000 1.0000 0.5000 1.0000 0.9167 [42,] 0.6000 0.8750 0.8696 1.0000 1.0000 1.0000 1.0000 1.0000 0.9000 0.8750 [43,] 1.0000 1.0000 0.9545 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9565 [44,] 1.0000 1.0000 0.8636 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 0.8696 [45,] 1.0000 1.0000 0.9545 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 [46,] 1.0000 1.0000 0.6087 0.9091 1.0000 1.0000 1.0000 1.0000 0.7857 0.6250 [47,] 1.0000 1.0000 0.9545 1.0000 0.0000 1.0000 1.0000 0.6667 1.0000 0.9565 [48,] 0.6667 0.8000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9565 [49,] 1.0000 1.0000 0.8182 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8261 [50,] 1.0000 1.0000 0.9545 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 0.9565 [51,] 1.0000 1.0000 0.9545 1.0000 1.0000 1.0000 1.0000 1.0000 0.8571 0.9565 [52,] 1.0000 1.0000 0.9565 0.6667 1.0000 1.0000 1.0000 1.0000 0.7143 0.9130 [53,] 1.0000 1.0000 0.8182 1.0000 1.0000 1.0000 1.0000 1.0000 0.9000 0.8261 [54,] 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 0.7143 0.9130 [55,] 1.0000 1.0000 0.9545 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 0.9167 [57,] 1.0000 1.0000 0.8636 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 0.9167 [58,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.8571 0.9565 [59,] 0.8333 1.0000 0.8182 0.8000 1.0000 1.0000 1.0000 1.0000 0.9000 0.8261 [60,] 1.0000 0.8750 0.8696 1.0000 0.7500 0.8571 0.7500 0.2500 1.0000 0.9200 [61,] 1.0000 1.0000 0.9130 0.7500 1.0000 1.0000 1.0000 1.0000 0.7500 0.9167 [62,] 1.0000 1.0000 0.9545 1.0000 0.0000 1.0000 1.0000 0.6667 1.0000 0.9565 [63,] 1.0000 1.0000 0.9545 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 [64,] 1.0000 1.0000 0.8182 1.0000 1.0000 1.0000 1.0000 1.0000 0.9000 0.8750 [65,] 0.7500 0.8333 0.9565 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9130 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 0.9167 [67,] 0.7500 1.0000 0.9091 1.0000 1.0000 1.0000 1.0000 1.0000 0.8750 0.9130 [68,] 1.0000 1.0000 0.9545 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9565 [69,] 0.9000 1.0000 0.6364 0.8889 1.0000 1.0000 1.0000 1.0000 0.9286 0.7083 [70,] 0.6667 0.8000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9565 [71,] 0.8000 0.8571 0.8636 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 0.8696 [72,] 1.0000 1.0000 0.9130 1.0000 0.6667 0.8333 1.0000 0.5000 1.0000 0.9167 [73,] 1.0000 1.0000 0.9091 1.0000 1.0000 1.0000 1.0000 1.0000 0.8750 0.9130 [74,] 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 0.7143 0.9130 [75,] 1.0000 0.7778 0.8800 1.0000 0.8333 0.5714 0.8333 0.5000 1.0000 0.9259 [76,] 1.0000 1.0000 0.9091 1.0000 0.5000 1.0000 1.0000 0.3333 1.0000 0.9583 [77,] 0.8333 0.9333 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8750 0.6400 [78,] 1.0000 1.0000 0.9545 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 [79,] 0.6667 0.8000 0.9545 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9565 [80,] 1.0000 0.6667 1.0000 1.0000 1.0000 0.2500 0.6667 1.0000 1.0000 1.0000 [,21] [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30] [1,] 0.4333 0.9643 0.9630 1.0000 0.9630 0.8889 0.8889 0.9630 0.9630 1.0000 [2,] 0.8500 0.7500 1.0000 0.6667 1.0000 0.8000 0.8000 1.0000 1.0000 0.6667 [3,] 0.8500 0.3333 1.0000 1.0000 1.0000 0.8000 0.8000 0.6667 1.0000 1.0000 [4,] 0.9500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [5,] 0.9500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [6,] 0.8500 0.3333 1.0000 0.6667 1.0000 0.8000 1.0000 0.6667 1.0000 0.6667 [7,] 0.8500 1.0000 1.0000 1.0000 1.0000 0.5000 0.0000 1.0000 1.0000 1.0000 [8,] 0.6429 1.0000 1.0000 1.0000 0.9444 1.0000 1.0000 1.0000 1.0000 1.0000 [9,] 0.9000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [10,] 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [11,] 0.9048 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [12,] 0.8095 1.0000 1.0000 1.0000 1.0000 0.8571 0.6667 1.0000 1.0000 1.0000 [13,] 0.4444 0.9091 1.0000 0.9545 1.0000 0.9583 1.0000 0.9545 0.9545 0.9545 [14,] 0.9524 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [15,] 0.9500 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [16,] 0.8571 1.0000 1.0000 1.0000 1.0000 0.6000 0.2500 1.0000 1.0000 1.0000 [17,] 0.9500 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 [18,] 0.8500 0.3333 1.0000 0.6667 1.0000 0.8000 1.0000 0.6667 1.0000 0.6667 [19,] 0.8750 1.0000 0.8571 1.0000 0.8571 1.0000 1.0000 1.0000 1.0000 1.0000 [20,] 0.5172 0.9583 0.9565 0.9565 0.9565 1.0000 1.0000 1.0000 0.9565 0.9565 [21,] 0.0000 0.9000 1.0000 0.9500 1.0000 0.8500 0.8500 0.9500 0.9500 0.9500 [22,] 0.9000 0.0000 1.0000 1.0000 1.0000 0.7500 1.0000 0.5000 1.0000 1.0000 [23,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 0.9500 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [25,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 0.8500 0.7500 1.0000 1.0000 1.0000 0.0000 0.5000 0.6667 1.0000 1.0000 [27,] 0.8500 1.0000 1.0000 1.0000 1.0000 0.5000 0.0000 1.0000 1.0000 1.0000 [28,] 0.9500 0.5000 1.0000 1.0000 1.0000 0.6667 1.0000 0.0000 1.0000 1.0000 [29,] 0.9500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 [30,] 0.9500 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [32,] 0.9500 0.5000 1.0000 1.0000 1.0000 0.6667 1.0000 0.0000 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 0.9500 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 [35,] 0.8500 0.7500 1.0000 0.6667 1.0000 0.8000 0.8000 1.0000 1.0000 0.6667 [36,] 0.3750 0.9375 0.9688 0.9688 0.9688 0.9062 0.9062 0.9688 0.9688 0.9688 [37,] 0.9000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [38,] 0.9500 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 0.8500 0.3333 1.0000 0.6667 1.0000 0.8000 1.0000 0.6667 1.0000 0.6667 [41,] 0.9048 0.7500 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 [42,] 0.8571 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [43,] 0.9500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 [44,] 0.8500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [45,] 0.9500 0.5000 1.0000 1.0000 1.0000 0.6667 1.0000 0.0000 1.0000 1.0000 [46,] 0.5714 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9000 1.0000 [47,] 0.9500 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [48,] 0.9500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [49,] 0.8000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 [50,] 0.9500 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [51,] 0.9500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [52,] 0.9524 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [53,] 0.8000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [54,] 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [55,] 0.9500 0.5000 1.0000 1.0000 1.0000 0.6667 1.0000 0.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [57,] 0.8500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [58,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 0.8571 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [60,] 0.8000 0.5000 1.0000 0.7500 1.0000 0.8333 0.8333 0.7500 1.0000 0.7500 [61,] 0.9048 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [62,] 0.9500 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [63,] 0.9500 0.5000 1.0000 1.0000 1.0000 0.6667 1.0000 0.0000 1.0000 1.0000 [64,] 0.8571 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [65,] 0.9000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [67,] 0.9000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [68,] 0.9500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [69,] 0.7273 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [70,] 0.9500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [71,] 0.9048 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [72,] 0.8500 0.7500 1.0000 0.6667 1.0000 0.8000 0.8000 1.0000 1.0000 0.6667 [73,] 0.9000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 0.5000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 [75,] 0.7000 0.6667 1.0000 0.8333 1.0000 0.5000 0.5000 0.8333 1.0000 0.8333 [76,] 0.9000 0.0000 1.0000 1.0000 1.0000 0.7500 1.0000 0.5000 1.0000 1.0000 [77,] 0.7083 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9091 1.0000 [78,] 0.9500 0.5000 1.0000 1.0000 1.0000 0.6667 1.0000 0.0000 1.0000 1.0000 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 0.9048 1.0000 1.0000 1.0000 1.0000 0.8000 0.5000 1.0000 1.0000 1.0000 [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] [,39] [,40] [1,] 0.9630 0.9630 0.9630 0.9630 0.9655 0.2121 0.9643 1.0000 0.9630 0.9655 [2,] 1.0000 1.0000 1.0000 1.0000 0.0000 0.9062 0.7500 0.6667 1.0000 0.5000 [3,] 1.0000 0.6667 1.0000 0.6667 0.8000 0.9062 0.7500 0.6667 1.0000 0.5000 [4,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 1.0000 [5,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 1.0000 [6,] 1.0000 0.6667 1.0000 1.0000 0.5000 0.9062 0.7500 0.6667 1.0000 0.0000 [7,] 1.0000 1.0000 1.0000 0.6667 0.8000 0.9062 1.0000 1.0000 1.0000 1.0000 [8,] 0.9444 1.0000 1.0000 1.0000 1.0000 0.4848 0.9474 1.0000 1.0000 1.0000 [9,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9375 0.6667 1.0000 1.0000 1.0000 [10,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8438 0.8333 1.0000 1.0000 1.0000 [11,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9062 1.0000 1.0000 0.6667 1.0000 [12,] 1.0000 1.0000 1.0000 0.8000 1.0000 0.8438 1.0000 1.0000 0.8000 1.0000 [13,] 0.9545 0.9545 1.0000 1.0000 0.9130 0.3125 0.9091 0.9545 0.9545 0.8636 [14,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9375 1.0000 1.0000 1.0000 1.0000 [15,] 1.0000 1.0000 1.0000 1.0000 0.6667 0.9688 0.5000 0.0000 1.0000 0.6667 [16,] 1.0000 1.0000 0.7500 0.7500 0.8333 0.9091 1.0000 1.0000 1.0000 1.0000 [17,] 1.0000 1.0000 1.0000 0.0000 1.0000 0.9688 1.0000 1.0000 1.0000 1.0000 [18,] 1.0000 0.6667 1.0000 1.0000 0.5000 0.9062 0.7500 0.6667 1.0000 0.0000 [19,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.7812 1.0000 1.0000 1.0000 1.0000 [20,] 0.9565 1.0000 1.0000 1.0000 0.9167 0.2812 0.9130 0.9565 0.9565 0.9167 [21,] 1.0000 0.9500 1.0000 0.9500 0.8500 0.3750 0.9000 0.9500 1.0000 0.8500 [22,] 1.0000 0.5000 1.0000 1.0000 0.7500 0.9375 0.6667 0.5000 1.0000 0.3333 [23,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 1.0000 [24,] 1.0000 1.0000 1.0000 1.0000 0.6667 0.9688 1.0000 1.0000 1.0000 0.6667 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 0.6667 1.0000 1.0000 0.8000 0.9062 1.0000 1.0000 1.0000 0.8000 [27,] 1.0000 1.0000 1.0000 0.6667 0.8000 0.9062 1.0000 1.0000 1.0000 1.0000 [28,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 0.6667 [29,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 1.0000 [30,] 1.0000 1.0000 1.0000 1.0000 0.6667 0.9688 1.0000 1.0000 1.0000 0.6667 [31,] 0.0000 1.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 1.0000 [32,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 0.6667 [33,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 1.0000 1.0000 0.0000 1.0000 0.9688 1.0000 1.0000 1.0000 1.0000 [35,] 1.0000 1.0000 1.0000 1.0000 0.0000 0.9062 0.7500 0.6667 1.0000 0.5000 [36,] 0.9688 0.9688 1.0000 0.9688 0.9062 0.0000 0.9375 0.9688 0.9688 0.9062 [37,] 1.0000 1.0000 1.0000 1.0000 0.7500 0.9375 0.0000 0.5000 1.0000 0.7500 [38,] 1.0000 1.0000 1.0000 1.0000 0.6667 0.9688 0.5000 0.0000 1.0000 0.6667 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 0.0000 1.0000 [40,] 1.0000 0.6667 1.0000 1.0000 0.5000 0.9062 0.7500 0.6667 1.0000 0.0000 [41,] 1.0000 1.0000 1.0000 1.0000 0.5000 0.9062 0.7500 0.6667 1.0000 0.5000 [42,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8750 1.0000 1.0000 1.0000 1.0000 [43,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 1.0000 [44,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9062 1.0000 1.0000 1.0000 1.0000 [45,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 0.6667 [46,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.6875 0.9091 1.0000 1.0000 1.0000 [47,] 1.0000 1.0000 1.0000 1.0000 0.6667 0.9688 0.5000 0.0000 1.0000 0.6667 [48,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 1.0000 [49,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8750 1.0000 1.0000 1.0000 1.0000 [50,] 1.0000 1.0000 1.0000 1.0000 0.6667 0.9688 1.0000 1.0000 1.0000 0.6667 [51,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 1.0000 [52,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9375 1.0000 1.0000 1.0000 1.0000 [53,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8750 0.8000 1.0000 1.0000 1.0000 [54,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9375 1.0000 1.0000 1.0000 1.0000 [55,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 0.6667 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9394 1.0000 1.0000 1.0000 1.0000 [57,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9062 0.7500 1.0000 1.0000 1.0000 [58,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 1.0000 [59,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8750 1.0000 1.0000 1.0000 1.0000 [60,] 1.0000 0.7500 1.0000 0.7500 0.6000 0.8750 0.8000 0.7500 1.0000 0.2500 [61,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9062 1.0000 1.0000 1.0000 1.0000 [62,] 1.0000 1.0000 1.0000 1.0000 0.6667 0.9688 0.5000 0.0000 1.0000 0.6667 [63,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 0.6667 [64,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8750 0.8000 1.0000 1.0000 1.0000 [65,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9375 1.0000 1.0000 1.0000 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9394 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9375 1.0000 1.0000 1.0000 1.0000 [68,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 1.0000 [69,] 0.8750 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 [70,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 1.0000 [71,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9062 1.0000 1.0000 0.6667 1.0000 [72,] 1.0000 1.0000 1.0000 1.0000 0.0000 0.9062 0.7500 0.6667 1.0000 0.5000 [73,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9375 0.6667 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9375 1.0000 1.0000 1.0000 1.0000 [75,] 1.0000 0.8333 1.0000 0.8333 0.5000 0.8125 0.8571 0.8333 1.0000 0.5000 [76,] 1.0000 0.5000 1.0000 1.0000 0.7500 0.9375 0.6667 0.5000 1.0000 0.3333 [77,] 0.9091 1.0000 1.0000 1.0000 1.0000 0.6562 1.0000 1.0000 0.9091 1.0000 [78,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 1.0000 0.6667 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9688 1.0000 1.0000 0.0000 1.0000 [80,] 1.0000 1.0000 0.6667 0.6667 1.0000 0.9394 1.0000 1.0000 1.0000 1.0000 [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50] [1,] 0.9655 0.8929 0.9630 0.8889 0.9630 0.6786 1.0000 1.0000 0.8519 1.0000 [2,] 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 0.6667 [3,] 0.8000 1.0000 1.0000 1.0000 0.6667 1.0000 0.6667 1.0000 1.0000 1.0000 [4,] 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [5,] 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 [6,] 0.5000 1.0000 1.0000 1.0000 0.6667 1.0000 0.6667 1.0000 1.0000 0.6667 [7,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [8,] 1.0000 0.8421 1.0000 0.8333 1.0000 0.6000 1.0000 1.0000 0.9000 1.0000 [9,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8000 1.0000 1.0000 1.0000 1.0000 [10,] 1.0000 0.8750 1.0000 0.8571 1.0000 0.7500 1.0000 1.0000 0.8750 1.0000 [11,] 1.0000 0.6000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 [12,] 1.0000 0.8750 1.0000 1.0000 1.0000 1.0000 1.0000 0.8000 1.0000 1.0000 [13,] 0.8636 0.8696 0.9545 0.8636 0.9545 0.6087 0.9545 1.0000 0.8182 0.9545 [14,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9091 1.0000 1.0000 1.0000 1.0000 [15,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [16,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [17,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [18,] 0.5000 1.0000 1.0000 1.0000 0.6667 1.0000 0.6667 1.0000 1.0000 0.6667 [19,] 1.0000 0.9000 1.0000 0.8889 1.0000 0.7857 1.0000 1.0000 1.0000 1.0000 [20,] 0.9167 0.8750 0.9565 0.8696 1.0000 0.6250 0.9565 0.9565 0.8261 0.9565 [21,] 0.9048 0.8571 0.9500 0.8500 0.9500 0.5714 0.9500 0.9500 0.8000 0.9500 [22,] 0.7500 1.0000 1.0000 1.0000 0.5000 1.0000 0.5000 1.0000 1.0000 1.0000 [23,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 [27,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [28,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [29,] 1.0000 1.0000 0.0000 1.0000 1.0000 0.9000 1.0000 1.0000 0.7500 1.0000 [30,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [32,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [35,] 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 0.6667 [36,] 0.9062 0.8750 0.9688 0.9062 0.9688 0.6875 0.9688 0.9688 0.8750 0.9688 [37,] 0.7500 1.0000 1.0000 1.0000 1.0000 0.9091 0.5000 1.0000 1.0000 1.0000 [38,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 0.5000 1.0000 1.0000 1.0000 0.6667 1.0000 0.6667 1.0000 1.0000 0.6667 [41,] 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 0.6667 [42,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 0.8571 1.0000 [43,] 1.0000 1.0000 0.0000 1.0000 1.0000 0.9000 1.0000 1.0000 0.7500 1.0000 [44,] 1.0000 1.0000 1.0000 0.0000 1.0000 0.7000 1.0000 1.0000 0.8333 1.0000 [45,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [46,] 1.0000 1.0000 0.9000 0.7000 1.0000 0.0000 1.0000 1.0000 0.7273 1.0000 [47,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [48,] 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [49,] 1.0000 0.8571 0.7500 0.8333 1.0000 0.7273 1.0000 1.0000 0.0000 1.0000 [50,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [51,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9000 1.0000 1.0000 1.0000 1.0000 [52,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9091 1.0000 1.0000 1.0000 1.0000 [53,] 1.0000 0.8571 1.0000 1.0000 1.0000 0.8333 1.0000 1.0000 0.8571 1.0000 [54,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [55,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 [57,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.7000 1.0000 1.0000 1.0000 1.0000 [58,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 1.0000 0.6667 1.0000 1.0000 1.0000 0.9231 1.0000 1.0000 0.8571 1.0000 [60,] 0.6000 1.0000 1.0000 1.0000 0.7500 1.0000 0.7500 1.0000 1.0000 0.7500 [61,] 1.0000 1.0000 1.0000 0.8000 1.0000 0.8182 1.0000 1.0000 1.0000 1.0000 [62,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [63,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [64,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.7273 1.0000 1.0000 1.0000 1.0000 [65,] 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 0.5000 0.8000 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8000 1.0000 [68,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [69,] 1.0000 0.6667 1.0000 0.7778 1.0000 0.7143 1.0000 1.0000 0.6667 1.0000 [70,] 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [71,] 1.0000 1.0000 1.0000 0.5000 1.0000 0.8182 1.0000 1.0000 1.0000 1.0000 [72,] 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 0.6667 [73,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.8000 1.0000 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [75,] 0.7143 1.0000 1.0000 1.0000 0.8333 1.0000 0.8333 1.0000 1.0000 0.8333 [76,] 0.7500 1.0000 1.0000 1.0000 0.5000 1.0000 0.5000 1.0000 1.0000 1.0000 [77,] 1.0000 0.7500 0.9091 0.7273 1.0000 0.7647 1.0000 1.0000 0.7500 1.0000 [78,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [1,] 0.9630 0.9259 0.8519 0.9259 0.9630 1.0000 0.8889 0.9630 0.8519 0.9310 [2,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6000 [3,] 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 0.2500 [4,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 [5,] 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 [6,] 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 0.2500 [7,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8333 [8,] 0.9444 0.9474 0.7778 0.9474 1.0000 0.8333 0.8333 1.0000 0.7778 1.0000 [9,] 0.5000 0.6667 0.5000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [10,] 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 0.6667 1.0000 0.8750 1.0000 [11,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8333 1.0000 [12,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8750 [13,] 0.9545 0.9565 0.8182 1.0000 0.9545 1.0000 0.8636 1.0000 0.8182 0.8696 [14,] 1.0000 0.6667 1.0000 0.6667 1.0000 1.0000 1.0000 0.5000 0.8000 1.0000 [15,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [16,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8571 [17,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [18,] 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 0.2500 [19,] 0.8571 0.7143 0.9000 0.7143 1.0000 0.8889 0.8889 0.8571 0.9000 1.0000 [20,] 0.9565 0.9130 0.8261 0.9130 1.0000 0.9167 0.9167 0.9565 0.8261 0.9200 [21,] 0.9500 0.9524 0.8000 1.0000 0.9500 1.0000 0.8500 1.0000 0.8571 0.8000 [22,] 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.5000 [23,] 1.0000 0.5000 1.0000 0.5000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [24,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 0.8333 [27,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8333 [28,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [29,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [30,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [32,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [35,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6000 [36,] 0.9688 0.9375 0.8750 0.9375 0.9688 0.9394 0.9062 0.9688 0.8750 0.8750 [37,] 1.0000 1.0000 0.8000 1.0000 1.0000 1.0000 0.7500 1.0000 1.0000 0.8000 [38,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 0.2500 [41,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6000 [42,] 1.0000 1.0000 0.8571 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 [43,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [44,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [45,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [46,] 0.9000 0.9091 0.8333 1.0000 1.0000 0.9167 0.7000 1.0000 0.9231 1.0000 [47,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [48,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [49,] 1.0000 1.0000 0.8571 1.0000 1.0000 1.0000 1.0000 1.0000 0.8571 1.0000 [50,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [51,] 0.0000 0.5000 0.7500 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 [52,] 0.5000 0.0000 0.8000 0.6667 1.0000 1.0000 0.7500 0.5000 1.0000 1.0000 [53,] 0.7500 0.8000 0.0000 1.0000 1.0000 1.0000 0.6000 1.0000 0.8571 1.0000 [54,] 1.0000 0.6667 1.0000 0.0000 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 [55,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [57,] 0.6667 0.7500 0.6000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [58,] 1.0000 0.5000 1.0000 0.5000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [59,] 1.0000 1.0000 0.8571 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 [60,] 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 0.0000 [61,] 1.0000 0.7500 1.0000 0.7500 1.0000 1.0000 0.8000 0.6667 1.0000 1.0000 [62,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [63,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [64,] 0.7500 0.8000 0.6667 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 [65,] 1.0000 1.0000 0.8000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8000 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 1.0000 0.8000 1.0000 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 [68,] 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [69,] 1.0000 1.0000 0.9091 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 [70,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [71,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [72,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6000 [73,] 0.5000 0.6667 0.5000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [74,] 1.0000 0.6667 1.0000 0.6667 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 [75,] 1.0000 1.0000 1.0000 1.0000 0.8333 1.0000 1.0000 1.0000 1.0000 0.3333 [76,] 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.5000 [77,] 1.0000 1.0000 0.8462 1.0000 1.0000 1.0000 1.0000 1.0000 0.8462 1.0000 [78,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8333 [,61] [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [1,] 0.8889 1.0000 0.9630 0.8929 0.9643 1.0000 0.9259 0.9630 0.7037 1.0000 [2,] 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [3,] 1.0000 0.6667 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [4,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 0.8750 1.0000 [5,] 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 0.5000 1.0000 0.8750 1.0000 [6,] 1.0000 0.6667 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [7,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [8,] 0.8947 1.0000 1.0000 0.8421 0.9474 0.8333 0.8889 0.9444 0.6316 1.0000 [9,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [10,] 0.6667 1.0000 1.0000 0.7143 0.8333 1.0000 0.8333 0.8000 0.9167 1.0000 [11,] 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 0.7500 1.0000 0.9000 0.6667 [12,] 1.0000 1.0000 1.0000 1.0000 0.8333 1.0000 1.0000 1.0000 1.0000 0.8000 [13,] 0.9130 0.9545 0.9545 0.8182 0.9565 1.0000 0.9091 0.9545 0.6364 1.0000 [14,] 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8889 1.0000 [15,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [16,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [17,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [18,] 1.0000 0.6667 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [19,] 0.7500 1.0000 1.0000 0.9000 1.0000 0.8889 0.8750 1.0000 0.9286 1.0000 [20,] 0.9167 0.9565 1.0000 0.8750 0.9130 0.9167 0.9130 0.9565 0.7083 0.9565 [21,] 0.9048 0.9500 0.9500 0.8571 0.9000 1.0000 0.9000 0.9500 0.7273 0.9500 [22,] 1.0000 0.5000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [23,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [27,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [28,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [29,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [30,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8750 1.0000 [32,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [35,] 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [36,] 0.9062 0.9688 0.9688 0.8750 0.9375 0.9394 0.9375 0.9688 0.7500 0.9688 [37,] 1.0000 0.5000 1.0000 0.8000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [38,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 1.0000 0.6667 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [41,] 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [42,] 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 0.5000 1.0000 0.6667 0.7500 [43,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [44,] 0.8000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7778 1.0000 [45,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [46,] 0.8182 1.0000 1.0000 0.7273 1.0000 0.9167 1.0000 1.0000 0.7143 1.0000 [47,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [48,] 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.0000 [49,] 1.0000 1.0000 1.0000 1.0000 0.8000 1.0000 0.8000 1.0000 0.6667 1.0000 [50,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [51,] 1.0000 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [52,] 0.7500 1.0000 1.0000 0.8000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [53,] 1.0000 1.0000 1.0000 0.6667 0.8000 1.0000 0.8000 0.7500 0.9091 1.0000 [54,] 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [55,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [57,] 0.8000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [58,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 1.0000 1.0000 1.0000 1.0000 0.8000 1.0000 0.5000 1.0000 0.6667 1.0000 [60,] 1.0000 0.7500 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [61,] 0.0000 1.0000 1.0000 0.8333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [62,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [63,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [64,] 0.8333 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [65,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 0.6667 1.0000 0.8889 0.5000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 1.0000 1.0000 1.0000 0.6667 1.0000 0.0000 1.0000 0.7500 1.0000 [68,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [69,] 1.0000 1.0000 1.0000 1.0000 0.8889 1.0000 0.7500 1.0000 0.0000 1.0000 [70,] 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.0000 [71,] 0.8000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9000 1.0000 [72,] 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [73,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [74,] 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [75,] 1.0000 0.8333 0.8333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [76,] 1.0000 0.5000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [77,] 0.9231 1.0000 1.0000 1.0000 0.9167 1.0000 0.8182 0.9091 0.5385 1.0000 [78,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [,71] [,72] [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [1,] 0.8889 0.9655 0.9259 0.9259 0.8621 0.9643 0.5926 0.9630 0.9630 0.8889 [2,] 1.0000 0.0000 1.0000 1.0000 0.5000 0.7500 1.0000 1.0000 1.0000 1.0000 [3,] 1.0000 0.8000 1.0000 1.0000 0.5000 0.3333 1.0000 0.6667 1.0000 0.8000 [4,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9091 1.0000 1.0000 1.0000 [5,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9091 1.0000 1.0000 1.0000 [6,] 1.0000 0.5000 1.0000 1.0000 0.5000 0.3333 1.0000 0.6667 1.0000 1.0000 [7,] 1.0000 0.8000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.5000 [8,] 0.8947 1.0000 0.8889 0.9474 1.0000 1.0000 0.6190 1.0000 1.0000 1.0000 [9,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [10,] 0.8571 1.0000 0.8333 1.0000 1.0000 1.0000 0.7692 1.0000 1.0000 1.0000 [11,] 0.8000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8333 1.0000 0.6667 1.0000 [12,] 0.8571 1.0000 1.0000 1.0000 0.7778 1.0000 0.9333 1.0000 0.8000 0.6667 [13,] 0.8636 0.9130 0.9091 1.0000 0.8800 0.9091 0.5000 0.9545 0.9545 1.0000 [14,] 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [15,] 1.0000 0.6667 1.0000 1.0000 0.8333 0.5000 1.0000 1.0000 1.0000 1.0000 [16,] 1.0000 0.8333 1.0000 1.0000 0.5714 1.0000 1.0000 1.0000 1.0000 0.2500 [17,] 1.0000 1.0000 1.0000 1.0000 0.8333 1.0000 1.0000 1.0000 1.0000 0.6667 [18,] 1.0000 0.5000 1.0000 1.0000 0.5000 0.3333 1.0000 0.6667 1.0000 1.0000 [19,] 0.8889 1.0000 0.8750 0.7143 1.0000 1.0000 0.8750 1.0000 1.0000 1.0000 [20,] 0.8696 0.9167 0.9130 0.9130 0.9259 0.9583 0.6400 1.0000 0.9565 1.0000 [21,] 0.9048 0.8500 0.9000 1.0000 0.7000 0.9000 0.7083 0.9500 1.0000 0.9048 [22,] 1.0000 0.7500 1.0000 1.0000 0.6667 0.0000 1.0000 0.5000 1.0000 1.0000 [23,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 1.0000 0.6667 1.0000 1.0000 0.8333 1.0000 1.0000 1.0000 1.0000 1.0000 [25,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 0.8000 1.0000 1.0000 0.5000 0.7500 1.0000 0.6667 1.0000 0.8000 [27,] 1.0000 0.8000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.5000 [28,] 1.0000 1.0000 1.0000 1.0000 0.8333 0.5000 1.0000 0.0000 1.0000 1.0000 [29,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9091 1.0000 1.0000 1.0000 [30,] 1.0000 0.6667 1.0000 1.0000 0.8333 1.0000 1.0000 1.0000 1.0000 1.0000 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9091 1.0000 1.0000 1.0000 [32,] 1.0000 1.0000 1.0000 1.0000 0.8333 0.5000 1.0000 0.0000 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6667 [34,] 1.0000 1.0000 1.0000 1.0000 0.8333 1.0000 1.0000 1.0000 1.0000 0.6667 [35,] 1.0000 0.0000 1.0000 1.0000 0.5000 0.7500 1.0000 1.0000 1.0000 1.0000 [36,] 0.9062 0.9062 0.9375 0.9375 0.8125 0.9375 0.6562 0.9688 0.9688 0.9394 [37,] 1.0000 0.7500 0.6667 1.0000 0.8571 0.6667 1.0000 1.0000 1.0000 1.0000 [38,] 1.0000 0.6667 1.0000 1.0000 0.8333 0.5000 1.0000 1.0000 1.0000 1.0000 [39,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 0.9091 1.0000 0.0000 1.0000 [40,] 1.0000 0.5000 1.0000 1.0000 0.5000 0.3333 1.0000 0.6667 1.0000 1.0000 [41,] 1.0000 0.5000 1.0000 1.0000 0.7143 0.7500 1.0000 1.0000 1.0000 1.0000 [42,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 [43,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9091 1.0000 1.0000 1.0000 [44,] 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7273 1.0000 1.0000 1.0000 [45,] 1.0000 1.0000 1.0000 1.0000 0.8333 0.5000 1.0000 0.0000 1.0000 1.0000 [46,] 0.8182 1.0000 0.8000 1.0000 1.0000 1.0000 0.7647 1.0000 1.0000 1.0000 [47,] 1.0000 0.6667 1.0000 1.0000 0.8333 0.5000 1.0000 1.0000 1.0000 1.0000 [48,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [49,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 [50,] 1.0000 0.6667 1.0000 1.0000 0.8333 1.0000 1.0000 1.0000 1.0000 1.0000 [51,] 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [52,] 1.0000 1.0000 0.6667 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [53,] 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 0.8462 1.0000 1.0000 1.0000 [54,] 1.0000 1.0000 1.0000 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [55,] 1.0000 1.0000 1.0000 1.0000 0.8333 0.5000 1.0000 0.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [57,] 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [58,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8462 1.0000 1.0000 1.0000 [60,] 1.0000 0.6000 1.0000 1.0000 0.3333 0.5000 1.0000 0.7500 1.0000 0.8333 [61,] 0.8000 1.0000 1.0000 0.7500 1.0000 1.0000 0.9231 1.0000 1.0000 1.0000 [62,] 1.0000 0.6667 1.0000 1.0000 0.8333 0.5000 1.0000 1.0000 1.0000 1.0000 [63,] 1.0000 1.0000 1.0000 1.0000 0.8333 0.5000 1.0000 0.0000 1.0000 1.0000 [64,] 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [65,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9167 1.0000 1.0000 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8182 1.0000 1.0000 1.0000 [68,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9091 1.0000 1.0000 1.0000 [69,] 0.9000 1.0000 1.0000 1.0000 1.0000 1.0000 0.5385 1.0000 1.0000 1.0000 [70,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [71,] 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7273 1.0000 0.6667 1.0000 [72,] 1.0000 0.0000 1.0000 1.0000 0.5000 0.7500 1.0000 1.0000 1.0000 1.0000 [73,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [75,] 1.0000 0.5000 1.0000 1.0000 0.0000 0.6667 1.0000 0.8333 1.0000 0.7143 [76,] 1.0000 0.7500 1.0000 1.0000 0.6667 0.0000 1.0000 0.5000 1.0000 1.0000 [77,] 0.7273 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 0.9091 1.0000 [78,] 1.0000 1.0000 1.0000 1.0000 0.8333 0.5000 1.0000 0.0000 1.0000 1.0000 [79,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 0.9091 1.0000 0.0000 1.0000 [80,] 1.0000 1.0000 1.0000 1.0000 0.7143 1.0000 1.0000 1.0000 1.0000 0.0000 > > > > cleanEx() > nameEx("kulczynski") > ### * kulczynski > > flush(stderr()); flush(stdout()) > > ### Name: kulczynski > ### Title: Kulczynski distance matrix > ### Aliases: kulczynski > ### Keywords: cluster spatial > > ### ** Examples > > options(digits=4) > data(kykladspecreg) > kulczynski(t(kykladspecreg)) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 0.0000 0.8148 0.6296 0.4815 0.4815 0.8148 0.4444 0.3056 0.4630 0.4074 [2,] 0.8148 0.0000 0.6667 1.0000 1.0000 0.3333 0.6667 1.0000 1.0000 1.0000 [3,] 0.6296 0.6667 0.0000 1.0000 1.0000 0.3333 0.6667 1.0000 1.0000 1.0000 [4,] 0.4815 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 0.4722 1.0000 1.0000 [5,] 0.4815 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 0.4722 1.0000 0.4000 [6,] 0.8148 0.3333 0.3333 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [7,] 0.4444 0.6667 0.6667 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [8,] 0.3056 1.0000 1.0000 0.4722 0.4722 1.0000 1.0000 0.0000 0.4444 0.3611 [9,] 0.4630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4444 0.0000 0.6500 [10,] 0.4074 1.0000 1.0000 1.0000 0.4000 1.0000 1.0000 0.3611 0.6500 0.0000 [11,] 0.6296 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 0.8056 1.0000 1.0000 [12,] 0.5259 1.0000 0.7333 1.0000 1.0000 1.0000 0.4667 1.0000 1.0000 1.0000 [13,] 0.2163 0.6212 0.6212 0.4773 0.4773 0.4318 1.0000 0.3434 0.4545 0.3864 [14,] 0.4630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7222 1.0000 1.0000 [15,] 1.0000 0.3333 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [16,] 0.4259 0.7083 0.7083 1.0000 1.0000 1.0000 0.1250 1.0000 1.0000 1.0000 [17,] 0.4815 1.0000 0.3333 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [18,] 0.8148 0.3333 0.3333 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [19,] 0.4603 1.0000 1.0000 0.4286 1.0000 1.0000 1.0000 0.4048 0.6786 0.8286 [20,] 0.3156 0.6232 0.8116 0.4783 0.4783 0.6232 1.0000 0.2572 0.4565 0.5130 [21,] 0.2602 0.4250 0.4250 0.4750 0.4750 0.4250 0.4250 0.4722 0.4500 0.3750 [22,] 0.7315 0.5833 0.1667 1.0000 1.0000 0.1667 1.0000 1.0000 1.0000 1.0000 [23,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 1.0000 0.3333 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [25,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4722 1.0000 1.0000 [26,] 0.4444 0.6667 0.6667 1.0000 1.0000 0.6667 0.3333 1.0000 1.0000 1.0000 [27,] 0.4444 0.6667 0.6667 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [28,] 0.4815 1.0000 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [29,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [30,] 1.0000 0.3333 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [31,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4722 1.0000 1.0000 [32,] 0.4815 1.0000 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [33,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 0.4815 1.0000 0.3333 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [35,] 0.8148 0.0000 0.6667 1.0000 1.0000 0.3333 0.6667 1.0000 1.0000 1.0000 [36,] 0.1123 0.4531 0.4531 0.4844 0.4844 0.4531 0.4531 0.2622 0.4688 0.4219 [37,] 0.7315 0.5833 0.5833 1.0000 1.0000 0.5833 1.0000 0.7222 0.5000 0.6500 [38,] 1.0000 0.3333 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [39,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 0.8148 0.3333 0.3333 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [41,] 0.8148 0.3333 0.6667 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [42,] 0.5694 1.0000 1.0000 0.3750 0.3750 1.0000 1.0000 0.5417 1.0000 0.7750 [43,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [44,] 0.4444 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4167 1.0000 0.7333 [45,] 0.4815 1.0000 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [46,] 0.3833 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3778 0.4000 0.5500 [47,] 1.0000 0.3333 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [48,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [49,] 0.4259 1.0000 1.0000 1.0000 0.3750 1.0000 1.0000 0.6944 1.0000 0.7750 [50,] 1.0000 0.3333 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [51,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4722 0.2500 1.0000 [52,] 0.4630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7222 0.5000 1.0000 [53,] 0.4259 1.0000 1.0000 1.0000 0.3750 1.0000 1.0000 0.3889 0.2500 0.3250 [54,] 0.4630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7222 1.0000 1.0000 [55,] 0.4815 1.0000 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4167 1.0000 1.0000 [57,] 0.4444 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4167 0.1667 0.4667 [58,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 0.4259 1.0000 1.0000 0.3750 0.3750 1.0000 1.0000 0.3889 1.0000 0.7750 [60,] 0.7130 0.4167 0.1250 1.0000 1.0000 0.1250 0.7083 1.0000 1.0000 1.0000 [61,] 0.4444 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6111 1.0000 0.4667 [62,] 1.0000 0.3333 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [63,] 0.4815 1.0000 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [64,] 0.5694 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.5417 0.2500 0.5500 [65,] 0.7315 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 0.7222 1.0000 0.6500 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4167 1.0000 1.0000 [67,] 0.4630 1.0000 1.0000 0.2500 0.2500 1.0000 1.0000 0.4444 1.0000 0.6500 [68,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4722 1.0000 0.4000 [69,] 0.3519 1.0000 1.0000 0.4375 0.4375 1.0000 1.0000 0.3681 1.0000 0.8375 [70,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [71,] 0.4444 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6111 1.0000 0.7333 [72,] 0.8148 0.0000 0.6667 1.0000 1.0000 0.3333 0.6667 1.0000 1.0000 1.0000 [73,] 0.4630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4444 0.0000 0.6500 [74,] 0.4630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7222 1.0000 1.0000 [75,] 0.5926 0.2500 0.2500 1.0000 1.0000 0.2500 0.2500 1.0000 1.0000 1.0000 [76,] 0.7315 0.5833 0.1667 1.0000 1.0000 0.1667 1.0000 1.0000 1.0000 1.0000 [77,] 0.2963 1.0000 1.0000 0.4545 0.4545 1.0000 1.0000 0.4141 1.0000 0.5636 [78,] 0.4815 1.0000 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [79,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 0.4444 1.0000 0.6667 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [1,] 0.6296 0.5259 0.2163 0.4630 1.0000 0.4259 0.4815 0.8148 0.4603 0.3156 [2,] 1.0000 1.0000 0.6212 1.0000 0.3333 0.7083 1.0000 0.3333 1.0000 0.6232 [3,] 1.0000 0.7333 0.6212 1.0000 0.3333 0.7083 0.3333 0.3333 1.0000 0.8116 [4,] 0.3333 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 0.4286 0.4783 [5,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [6,] 1.0000 1.0000 0.4318 1.0000 0.3333 1.0000 1.0000 0.0000 1.0000 0.6232 [7,] 1.0000 0.4667 1.0000 1.0000 1.0000 0.1250 0.3333 1.0000 1.0000 1.0000 [8,] 0.8056 1.0000 0.3434 0.7222 1.0000 1.0000 1.0000 1.0000 0.4048 0.2572 [9,] 1.0000 1.0000 0.4545 1.0000 1.0000 1.0000 1.0000 1.0000 0.6786 0.4565 [10,] 1.0000 1.0000 0.3864 1.0000 1.0000 1.0000 1.0000 1.0000 0.8286 0.5130 [11,] 0.0000 0.4667 0.6212 1.0000 1.0000 1.0000 1.0000 1.0000 0.7619 0.4348 [12,] 0.4667 0.0000 0.8773 1.0000 1.0000 0.5500 0.4000 1.0000 1.0000 0.7565 [13,] 0.6212 0.8773 0.0000 0.7273 0.4773 1.0000 1.0000 0.4318 0.7175 0.2441 [14,] 1.0000 1.0000 0.7273 0.0000 1.0000 1.0000 1.0000 1.0000 0.6786 0.4565 [15,] 1.0000 1.0000 0.4773 1.0000 0.0000 1.0000 1.0000 0.3333 1.0000 0.4783 [16,] 1.0000 0.5500 1.0000 1.0000 1.0000 0.0000 0.3750 1.0000 1.0000 1.0000 [17,] 1.0000 0.4000 1.0000 1.0000 1.0000 0.3750 0.0000 1.0000 1.0000 1.0000 [18,] 1.0000 1.0000 0.4318 1.0000 0.3333 1.0000 1.0000 0.0000 1.0000 0.6232 [19,] 0.7619 1.0000 0.7175 0.6786 1.0000 1.0000 1.0000 1.0000 0.0000 0.3478 [20,] 0.4348 0.7565 0.2441 0.4565 0.4783 1.0000 1.0000 0.6232 0.3478 0.0000 [21,] 0.6167 0.5000 0.2841 0.7250 0.4750 0.5500 0.4750 0.4250 0.7107 0.3457 [22,] 1.0000 1.0000 0.4545 1.0000 0.2500 1.0000 1.0000 0.1667 1.0000 0.7283 [23,] 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 0.4286 0.4783 [24,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 0.4783 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4286 0.4783 [26,] 1.0000 0.7333 0.8106 1.0000 1.0000 0.4167 1.0000 0.6667 1.0000 1.0000 [27,] 1.0000 0.4667 1.0000 1.0000 1.0000 0.1250 0.3333 1.0000 1.0000 1.0000 [28,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 [29,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [30,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 0.4783 [31,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [32,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 0.4000 1.0000 1.0000 1.0000 0.3750 0.0000 1.0000 1.0000 1.0000 [35,] 1.0000 1.0000 0.6212 1.0000 0.3333 0.7083 1.0000 0.3333 1.0000 0.6232 [36,] 0.4531 0.4219 0.1562 0.4688 0.4844 0.5781 0.4844 0.4531 0.3906 0.1406 [37,] 1.0000 1.0000 0.4545 1.0000 0.2500 1.0000 1.0000 0.5833 1.0000 0.4565 [38,] 1.0000 1.0000 0.4773 1.0000 0.0000 1.0000 1.0000 0.3333 1.0000 0.4783 [39,] 0.3333 0.4000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [40,] 1.0000 1.0000 0.4318 1.0000 0.3333 1.0000 1.0000 0.0000 1.0000 0.6232 [41,] 1.0000 1.0000 0.4318 1.0000 0.3333 1.0000 1.0000 0.3333 1.0000 0.6232 [42,] 0.4167 0.7750 0.5568 1.0000 1.0000 1.0000 1.0000 1.0000 0.8036 0.5598 [43,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [44,] 1.0000 1.0000 0.4318 1.0000 1.0000 1.0000 1.0000 1.0000 0.7619 0.4348 [45,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 [46,] 1.0000 1.0000 0.3455 0.7000 1.0000 1.0000 1.0000 1.0000 0.6357 0.3543 [47,] 1.0000 1.0000 0.4773 1.0000 0.0000 1.0000 1.0000 0.3333 1.0000 0.4783 [48,] 0.3333 0.4000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [49,] 1.0000 1.0000 0.4091 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4130 [50,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 0.4783 [51,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 0.4286 0.4783 [52,] 1.0000 1.0000 0.7273 0.5000 1.0000 1.0000 1.0000 1.0000 0.3571 0.4565 [53,] 1.0000 1.0000 0.4091 1.0000 1.0000 1.0000 1.0000 1.0000 0.8036 0.4130 [54,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.3571 0.4565 [55,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7619 0.6232 [57,] 1.0000 1.0000 0.4318 1.0000 1.0000 1.0000 1.0000 1.0000 0.7619 0.6232 [58,] 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 0.4286 0.4783 [59,] 0.7083 1.0000 0.4091 0.6250 1.0000 1.0000 1.0000 1.0000 0.8036 0.4130 [60,] 1.0000 0.7750 0.5568 1.0000 0.3750 0.7500 0.3750 0.1250 1.0000 0.7065 [61,] 1.0000 1.0000 0.6212 0.5833 1.0000 1.0000 1.0000 1.0000 0.5238 0.6232 [62,] 1.0000 1.0000 0.4773 1.0000 0.0000 1.0000 1.0000 0.3333 1.0000 0.4783 [63,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 [64,] 1.0000 1.0000 0.4091 1.0000 1.0000 1.0000 1.0000 1.0000 0.8036 0.5598 [65,] 0.5833 0.6500 0.7273 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4565 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7619 0.6232 [67,] 0.5833 1.0000 0.4545 1.0000 1.0000 1.0000 1.0000 1.0000 0.6786 0.4565 [68,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [69,] 0.7708 1.0000 0.3182 0.6875 1.0000 1.0000 1.0000 1.0000 0.8661 0.4103 [70,] 0.3333 0.4000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [71,] 0.6667 0.7333 0.4318 1.0000 1.0000 1.0000 1.0000 1.0000 0.7619 0.4348 [72,] 1.0000 1.0000 0.6212 1.0000 0.3333 0.7083 1.0000 0.3333 1.0000 0.6232 [73,] 1.0000 1.0000 0.4545 1.0000 1.0000 1.0000 1.0000 1.0000 0.6786 0.4565 [74,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.3571 0.4565 [75,] 1.0000 0.6333 0.6818 1.0000 0.4167 0.3750 0.4167 0.2500 1.0000 0.7899 [76,] 1.0000 1.0000 0.4545 1.0000 0.2500 1.0000 1.0000 0.1667 1.0000 0.7283 [77,] 0.5758 0.8545 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 0.7662 0.3953 [78,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 [79,] 0.3333 0.4000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [80,] 1.0000 0.4667 1.0000 1.0000 1.0000 0.1250 0.3333 1.0000 1.0000 1.0000 [,21] [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30] [1,] 0.2602 0.7315 0.4815 1.0000 0.4815 0.4444 0.4444 0.4815 0.4815 1.0000 [2,] 0.4250 0.5833 1.0000 0.3333 1.0000 0.6667 0.6667 1.0000 1.0000 0.3333 [3,] 0.4250 0.1667 1.0000 1.0000 1.0000 0.6667 0.6667 0.3333 1.0000 1.0000 [4,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [5,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [6,] 0.4250 0.1667 1.0000 0.3333 1.0000 0.6667 1.0000 0.3333 1.0000 0.3333 [7,] 0.4250 1.0000 1.0000 1.0000 1.0000 0.3333 0.0000 1.0000 1.0000 1.0000 [8,] 0.4722 1.0000 1.0000 1.0000 0.4722 1.0000 1.0000 1.0000 1.0000 1.0000 [9,] 0.4500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [10,] 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [11,] 0.6167 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [12,] 0.5000 1.0000 1.0000 1.0000 1.0000 0.7333 0.4667 1.0000 1.0000 1.0000 [13,] 0.2841 0.4545 1.0000 0.4773 1.0000 0.8106 1.0000 0.4773 0.4773 0.4773 [14,] 0.7250 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [15,] 0.4750 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [16,] 0.5500 1.0000 1.0000 1.0000 1.0000 0.4167 0.1250 1.0000 1.0000 1.0000 [17,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [18,] 0.4250 0.1667 1.0000 0.3333 1.0000 0.6667 1.0000 0.3333 1.0000 0.3333 [19,] 0.7107 1.0000 0.4286 1.0000 0.4286 1.0000 1.0000 1.0000 1.0000 1.0000 [20,] 0.3457 0.7283 0.4783 0.4783 0.4783 1.0000 1.0000 1.0000 0.4783 0.4783 [21,] 0.0000 0.4500 1.0000 0.4750 1.0000 0.4250 0.4250 0.4750 0.4750 0.4750 [22,] 0.4500 0.0000 1.0000 1.0000 1.0000 0.5833 1.0000 0.2500 1.0000 1.0000 [23,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 0.4750 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [25,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 0.4250 0.5833 1.0000 1.0000 1.0000 0.0000 0.3333 0.3333 1.0000 1.0000 [27,] 0.4250 1.0000 1.0000 1.0000 1.0000 0.3333 0.0000 1.0000 1.0000 1.0000 [28,] 0.4750 0.2500 1.0000 1.0000 1.0000 0.3333 1.0000 0.0000 1.0000 1.0000 [29,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 [30,] 0.4750 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [32,] 0.4750 0.2500 1.0000 1.0000 1.0000 0.3333 1.0000 0.0000 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [35,] 0.4250 0.5833 1.0000 0.3333 1.0000 0.6667 0.6667 1.0000 1.0000 0.3333 [36,] 0.1875 0.4688 0.4844 0.4844 0.4844 0.4531 0.4531 0.4844 0.4844 0.4844 [37,] 0.4500 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [38,] 0.4750 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 0.4250 0.1667 1.0000 0.3333 1.0000 0.6667 1.0000 0.3333 1.0000 0.3333 [41,] 0.6167 0.5833 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 [42,] 0.5500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [43,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 [44,] 0.4250 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [45,] 0.4750 0.2500 1.0000 1.0000 1.0000 0.3333 1.0000 0.0000 1.0000 1.0000 [46,] 0.3250 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4500 1.0000 [47,] 0.4750 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [48,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [49,] 0.4000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 1.0000 [50,] 0.4750 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [51,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [52,] 0.7250 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [53,] 0.4000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [54,] 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [55,] 0.4750 0.2500 1.0000 1.0000 1.0000 0.3333 1.0000 0.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [57,] 0.4250 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [58,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 0.5500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [60,] 0.4000 0.2500 1.0000 0.3750 1.0000 0.7083 0.7083 0.3750 1.0000 0.3750 [61,] 0.6167 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [62,] 0.4750 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [63,] 0.4750 0.2500 1.0000 1.0000 1.0000 0.3333 1.0000 0.0000 1.0000 1.0000 [64,] 0.5500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [65,] 0.4500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [67,] 0.4500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [68,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [69,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [70,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [71,] 0.6167 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [72,] 0.4250 0.5833 1.0000 0.3333 1.0000 0.6667 0.6667 1.0000 1.0000 0.3333 [73,] 0.4500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 0.2500 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 [75,] 0.3500 0.3333 1.0000 0.4167 1.0000 0.2500 0.2500 0.4167 1.0000 0.4167 [76,] 0.4500 0.0000 1.0000 1.0000 1.0000 0.5833 1.0000 0.2500 1.0000 1.0000 [77,] 0.5068 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 [78,] 0.4750 0.2500 1.0000 1.0000 1.0000 0.3333 1.0000 0.0000 1.0000 1.0000 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 0.6167 1.0000 1.0000 1.0000 1.0000 0.6667 0.3333 1.0000 1.0000 1.0000 [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] [,39] [,40] [1,] 0.4815 0.4815 0.4815 0.4815 0.8148 0.1123 0.7315 1.0000 0.4815 0.8148 [2,] 1.0000 1.0000 1.0000 1.0000 0.0000 0.4531 0.5833 0.3333 1.0000 0.3333 [3,] 1.0000 0.3333 1.0000 0.3333 0.6667 0.4531 0.5833 0.3333 1.0000 0.3333 [4,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [5,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [6,] 1.0000 0.3333 1.0000 1.0000 0.3333 0.4531 0.5833 0.3333 1.0000 0.0000 [7,] 1.0000 1.0000 1.0000 0.3333 0.6667 0.4531 1.0000 1.0000 1.0000 1.0000 [8,] 0.4722 1.0000 1.0000 1.0000 1.0000 0.2622 0.7222 1.0000 1.0000 1.0000 [9,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4688 0.5000 1.0000 1.0000 1.0000 [10,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4219 0.6500 1.0000 1.0000 1.0000 [11,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4531 1.0000 1.0000 0.3333 1.0000 [12,] 1.0000 1.0000 1.0000 0.4000 1.0000 0.4219 1.0000 1.0000 0.4000 1.0000 [13,] 0.4773 0.4773 1.0000 1.0000 0.6212 0.1562 0.4545 0.4773 0.4773 0.4318 [14,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4688 1.0000 1.0000 1.0000 1.0000 [15,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.4844 0.2500 0.0000 1.0000 0.3333 [16,] 1.0000 1.0000 0.3750 0.3750 0.7083 0.5781 1.0000 1.0000 1.0000 1.0000 [17,] 1.0000 1.0000 1.0000 0.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [18,] 1.0000 0.3333 1.0000 1.0000 0.3333 0.4531 0.5833 0.3333 1.0000 0.0000 [19,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.3906 1.0000 1.0000 1.0000 1.0000 [20,] 0.4783 1.0000 1.0000 1.0000 0.6232 0.1406 0.4565 0.4783 0.4783 0.6232 [21,] 1.0000 0.4750 1.0000 0.4750 0.4250 0.1875 0.4500 0.4750 1.0000 0.4250 [22,] 1.0000 0.2500 1.0000 1.0000 0.5833 0.4688 0.5000 0.2500 1.0000 0.1667 [23,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [24,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.4844 1.0000 1.0000 1.0000 0.3333 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 0.3333 1.0000 1.0000 0.6667 0.4531 1.0000 1.0000 1.0000 0.6667 [27,] 1.0000 1.0000 1.0000 0.3333 0.6667 0.4531 1.0000 1.0000 1.0000 1.0000 [28,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 0.3333 [29,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [30,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.4844 1.0000 1.0000 1.0000 0.3333 [31,] 0.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [32,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 0.3333 [33,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 1.0000 1.0000 0.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [35,] 1.0000 1.0000 1.0000 1.0000 0.0000 0.4531 0.5833 0.3333 1.0000 0.3333 [36,] 0.4844 0.4844 1.0000 0.4844 0.4531 0.0000 0.4688 0.4844 0.4844 0.4531 [37,] 1.0000 1.0000 1.0000 1.0000 0.5833 0.4688 0.0000 0.2500 1.0000 0.5833 [38,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.4844 0.2500 0.0000 1.0000 0.3333 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 0.0000 1.0000 [40,] 1.0000 0.3333 1.0000 1.0000 0.3333 0.4531 0.5833 0.3333 1.0000 0.0000 [41,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.4531 0.5833 0.3333 1.0000 0.3333 [42,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4375 1.0000 1.0000 1.0000 1.0000 [43,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [44,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4531 1.0000 1.0000 1.0000 1.0000 [45,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 0.3333 [46,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.3438 0.7000 1.0000 1.0000 1.0000 [47,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.4844 0.2500 0.0000 1.0000 0.3333 [48,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [49,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4375 1.0000 1.0000 1.0000 1.0000 [50,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.4844 1.0000 1.0000 1.0000 0.3333 [51,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [52,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4688 1.0000 1.0000 1.0000 1.0000 [53,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4375 0.6250 1.0000 1.0000 1.0000 [54,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4688 1.0000 1.0000 1.0000 1.0000 [55,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 0.3333 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.6354 1.0000 1.0000 1.0000 1.0000 [57,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4531 0.5833 1.0000 1.0000 1.0000 [58,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [59,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4375 1.0000 1.0000 1.0000 1.0000 [60,] 1.0000 0.3750 1.0000 0.3750 0.4167 0.4375 0.6250 0.3750 1.0000 0.1250 [61,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4531 1.0000 1.0000 1.0000 1.0000 [62,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.4844 0.2500 0.0000 1.0000 0.3333 [63,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 0.3333 [64,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4375 0.6250 1.0000 1.0000 1.0000 [65,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4688 1.0000 1.0000 1.0000 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.6354 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4688 1.0000 1.0000 1.0000 1.0000 [68,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [69,] 0.4375 1.0000 1.0000 1.0000 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 [70,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [71,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4531 1.0000 1.0000 0.3333 1.0000 [72,] 1.0000 1.0000 1.0000 1.0000 0.0000 0.4531 0.5833 0.3333 1.0000 0.3333 [73,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4688 0.5000 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4688 1.0000 1.0000 1.0000 1.0000 [75,] 1.0000 0.4167 1.0000 0.4167 0.2500 0.4062 0.6667 0.4167 1.0000 0.2500 [76,] 1.0000 0.2500 1.0000 1.0000 0.5833 0.4688 0.5000 0.2500 1.0000 0.1667 [77,] 0.4545 1.0000 1.0000 1.0000 1.0000 0.3281 1.0000 1.0000 0.4545 1.0000 [78,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 0.3333 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 0.0000 1.0000 [80,] 1.0000 1.0000 0.3333 0.3333 1.0000 0.6354 1.0000 1.0000 1.0000 1.0000 [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50] [1,] 0.8148 0.5694 0.4815 0.4444 0.4815 0.3833 1.0000 1.0000 0.4259 1.0000 [2,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 0.3333 [3,] 0.6667 1.0000 1.0000 1.0000 0.3333 1.0000 0.3333 1.0000 1.0000 1.0000 [4,] 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [5,] 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 1.0000 [6,] 0.3333 1.0000 1.0000 1.0000 0.3333 1.0000 0.3333 1.0000 1.0000 0.3333 [7,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [8,] 1.0000 0.5417 1.0000 0.4167 1.0000 0.3778 1.0000 1.0000 0.6944 1.0000 [9,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4000 1.0000 1.0000 1.0000 1.0000 [10,] 1.0000 0.7750 1.0000 0.7333 1.0000 0.5500 1.0000 1.0000 0.7750 1.0000 [11,] 1.0000 0.4167 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 [12,] 1.0000 0.7750 1.0000 1.0000 1.0000 1.0000 1.0000 0.4000 1.0000 1.0000 [13,] 0.4318 0.5568 0.4773 0.4318 0.4773 0.3455 0.4773 1.0000 0.4091 0.4773 [14,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.7000 1.0000 1.0000 1.0000 1.0000 [15,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [16,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [17,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [18,] 0.3333 1.0000 1.0000 1.0000 0.3333 1.0000 0.3333 1.0000 1.0000 0.3333 [19,] 1.0000 0.8036 1.0000 0.7619 1.0000 0.6357 1.0000 1.0000 1.0000 1.0000 [20,] 0.6232 0.5598 0.4783 0.4348 1.0000 0.3543 0.4783 0.4783 0.4130 0.4783 [21,] 0.6167 0.5500 0.4750 0.4250 0.4750 0.3250 0.4750 0.4750 0.4000 0.4750 [22,] 0.5833 1.0000 1.0000 1.0000 0.2500 1.0000 0.2500 1.0000 1.0000 1.0000 [23,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 [27,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [28,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [29,] 1.0000 1.0000 0.0000 1.0000 1.0000 0.4500 1.0000 1.0000 0.3750 1.0000 [30,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [32,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [35,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 0.3333 [36,] 0.4531 0.4375 0.4844 0.4531 0.4844 0.3438 0.4844 0.4844 0.4375 0.4844 [37,] 0.5833 1.0000 1.0000 1.0000 1.0000 0.7000 0.2500 1.0000 1.0000 1.0000 [38,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 0.3333 1.0000 1.0000 1.0000 0.3333 1.0000 0.3333 1.0000 1.0000 0.3333 [41,] 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 0.3333 [42,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 0.7500 1.0000 [43,] 1.0000 1.0000 0.0000 1.0000 1.0000 0.4500 1.0000 1.0000 0.3750 1.0000 [44,] 1.0000 1.0000 1.0000 0.0000 1.0000 0.3500 1.0000 1.0000 0.7083 1.0000 [45,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [46,] 1.0000 1.0000 0.4500 0.3500 1.0000 0.0000 1.0000 1.0000 0.4750 1.0000 [47,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [48,] 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [49,] 1.0000 0.7500 0.3750 0.7083 1.0000 0.4750 1.0000 1.0000 0.0000 1.0000 [50,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [51,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4500 1.0000 1.0000 1.0000 1.0000 [52,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.7000 1.0000 1.0000 1.0000 1.0000 [53,] 1.0000 0.7500 1.0000 1.0000 1.0000 0.6500 1.0000 1.0000 0.7500 1.0000 [54,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [55,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.7833 1.0000 1.0000 1.0000 1.0000 [57,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.3500 1.0000 1.0000 1.0000 1.0000 [58,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 1.0000 0.5000 1.0000 1.0000 1.0000 0.8250 1.0000 1.0000 0.7500 1.0000 [60,] 0.4167 1.0000 1.0000 1.0000 0.3750 1.0000 0.3750 1.0000 1.0000 0.3750 [61,] 1.0000 1.0000 1.0000 0.6667 1.0000 0.5667 1.0000 1.0000 1.0000 1.0000 [62,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [63,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [64,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4750 1.0000 1.0000 1.0000 1.0000 [65,] 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 0.2500 0.6250 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.7833 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6250 1.0000 [68,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [69,] 1.0000 0.4375 1.0000 0.5417 1.0000 0.5500 1.0000 1.0000 0.4375 1.0000 [70,] 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [71,] 1.0000 1.0000 1.0000 0.3333 1.0000 0.5667 1.0000 1.0000 1.0000 1.0000 [72,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 0.3333 [73,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4000 1.0000 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [75,] 0.5000 1.0000 1.0000 1.0000 0.4167 1.0000 0.4167 1.0000 1.0000 0.4167 [76,] 0.5833 1.0000 1.0000 1.0000 0.2500 1.0000 0.2500 1.0000 1.0000 1.0000 [77,] 1.0000 0.4886 0.4545 0.3636 1.0000 0.6182 1.0000 1.0000 0.4886 1.0000 [78,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [1,] 0.4815 0.4630 0.4259 0.4630 0.4815 1.0000 0.4444 0.4815 0.4259 0.7130 [2,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4167 [3,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 0.1250 [4,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 1.0000 [5,] 1.0000 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 1.0000 [6,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 0.1250 [7,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7083 [8,] 0.4722 0.7222 0.3889 0.7222 1.0000 0.4167 0.4167 1.0000 0.3889 1.0000 [9,] 0.2500 0.5000 0.2500 1.0000 1.0000 1.0000 0.1667 1.0000 1.0000 1.0000 [10,] 1.0000 1.0000 0.3250 1.0000 1.0000 1.0000 0.4667 1.0000 0.7750 1.0000 [11,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7083 1.0000 [12,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7750 [13,] 0.4773 0.7273 0.4091 1.0000 0.4773 1.0000 0.4318 1.0000 0.4091 0.5568 [14,] 1.0000 0.5000 1.0000 0.5000 1.0000 1.0000 1.0000 0.2500 0.6250 1.0000 [15,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [16,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [17,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [18,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 0.1250 [19,] 0.4286 0.3571 0.8036 0.3571 1.0000 0.7619 0.7619 0.4286 0.8036 1.0000 [20,] 0.4783 0.4565 0.4130 0.4565 1.0000 0.6232 0.6232 0.4783 0.4130 0.7065 [21,] 0.4750 0.7250 0.4000 1.0000 0.4750 1.0000 0.4250 1.0000 0.5500 0.4000 [22,] 1.0000 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 0.2500 [23,] 1.0000 0.2500 1.0000 0.2500 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [24,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 0.7083 [27,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7083 [28,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [29,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [30,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [32,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [35,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4167 [36,] 0.4844 0.4688 0.4375 0.4688 0.4844 0.6354 0.4531 0.4844 0.4375 0.4375 [37,] 1.0000 1.0000 0.6250 1.0000 1.0000 1.0000 0.5833 1.0000 1.0000 0.6250 [38,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 0.1250 [41,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4167 [42,] 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 [43,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [44,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [45,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [46,] 0.4500 0.7000 0.6500 1.0000 1.0000 0.7833 0.3500 1.0000 0.8250 1.0000 [47,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [48,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [49,] 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 [50,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [51,] 0.0000 0.2500 0.3750 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [52,] 0.2500 0.0000 0.6250 0.5000 1.0000 1.0000 0.5833 0.2500 1.0000 1.0000 [53,] 0.3750 0.6250 0.0000 1.0000 1.0000 1.0000 0.4167 1.0000 0.7500 1.0000 [54,] 1.0000 0.5000 1.0000 0.0000 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 [55,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [57,] 0.3333 0.5833 0.4167 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [58,] 1.0000 0.2500 1.0000 0.2500 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [59,] 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 [60,] 1.0000 1.0000 1.0000 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 0.0000 [61,] 1.0000 0.5833 1.0000 0.5833 1.0000 1.0000 0.6667 0.3333 1.0000 1.0000 [62,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [63,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [64,] 0.3750 0.6250 0.5000 1.0000 1.0000 1.0000 0.1250 1.0000 1.0000 1.0000 [65,] 1.0000 1.0000 0.6250 1.0000 1.0000 1.0000 1.0000 1.0000 0.6250 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 1.0000 0.6250 1.0000 1.0000 1.0000 1.0000 1.0000 0.2500 1.0000 [68,] 1.0000 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [69,] 1.0000 1.0000 0.8125 1.0000 1.0000 1.0000 1.0000 1.0000 0.4375 1.0000 [70,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [71,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [72,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4167 [73,] 0.2500 0.5000 0.2500 1.0000 1.0000 1.0000 0.1667 1.0000 1.0000 1.0000 [74,] 1.0000 0.5000 1.0000 0.5000 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 [75,] 1.0000 1.0000 1.0000 1.0000 0.4167 1.0000 1.0000 1.0000 1.0000 0.1667 [76,] 1.0000 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 0.2500 [77,] 1.0000 1.0000 0.6591 1.0000 1.0000 1.0000 1.0000 1.0000 0.6591 1.0000 [78,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7083 [,61] [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [1,] 0.4444 1.0000 0.4815 0.5694 0.7315 1.0000 0.4630 0.4815 0.3519 1.0000 [2,] 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [3,] 1.0000 0.3333 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [4,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.2500 1.0000 0.4375 1.0000 [5,] 1.0000 1.0000 1.0000 1.0000 0.2500 1.0000 0.2500 1.0000 0.4375 1.0000 [6,] 1.0000 0.3333 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [7,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [8,] 0.6111 1.0000 1.0000 0.5417 0.7222 0.4167 0.4444 0.4722 0.3681 1.0000 [9,] 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [10,] 0.4667 1.0000 1.0000 0.5500 0.6500 1.0000 0.6500 0.4000 0.8375 1.0000 [11,] 1.0000 1.0000 1.0000 1.0000 0.5833 1.0000 0.5833 1.0000 0.7708 0.3333 [12,] 1.0000 1.0000 1.0000 1.0000 0.6500 1.0000 1.0000 1.0000 1.0000 0.4000 [13,] 0.6212 0.4773 0.4773 0.4091 0.7273 1.0000 0.4545 0.4773 0.3182 1.0000 [14,] 0.5833 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6875 1.0000 [15,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [16,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [17,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [18,] 1.0000 0.3333 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [19,] 0.5238 1.0000 1.0000 0.8036 1.0000 0.7619 0.6786 1.0000 0.8661 1.0000 [20,] 0.6232 0.4783 1.0000 0.5598 0.4565 0.6232 0.4565 0.4783 0.4103 0.4783 [21,] 0.6167 0.4750 0.4750 0.5500 0.4500 1.0000 0.4500 0.4750 0.4750 0.4750 [22,] 1.0000 0.2500 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [23,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [27,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [28,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [29,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [30,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4375 1.0000 [32,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [35,] 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [36,] 0.4531 0.4844 0.4844 0.4375 0.4688 0.6354 0.4688 0.4844 0.3750 0.4844 [37,] 1.0000 0.2500 1.0000 0.6250 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [38,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 1.0000 0.3333 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [41,] 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [42,] 1.0000 1.0000 1.0000 1.0000 0.2500 1.0000 0.2500 1.0000 0.4375 0.3750 [43,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [44,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.5417 1.0000 [45,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [46,] 0.5667 1.0000 1.0000 0.4750 1.0000 0.7833 1.0000 1.0000 0.5500 1.0000 [47,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [48,] 1.0000 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 0.0000 [49,] 1.0000 1.0000 1.0000 1.0000 0.6250 1.0000 0.6250 1.0000 0.4375 1.0000 [50,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [51,] 1.0000 1.0000 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [52,] 0.5833 1.0000 1.0000 0.6250 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [53,] 1.0000 1.0000 1.0000 0.5000 0.6250 1.0000 0.6250 0.3750 0.8125 1.0000 [54,] 0.5833 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [55,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [57,] 0.6667 1.0000 1.0000 0.1250 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [58,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 1.0000 1.0000 1.0000 1.0000 0.6250 1.0000 0.2500 1.0000 0.4375 1.0000 [60,] 1.0000 0.3750 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [61,] 0.0000 1.0000 1.0000 0.7083 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [62,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [63,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [64,] 0.7083 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [65,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 0.5000 1.0000 0.6875 0.2500 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 0.0000 1.0000 0.3750 1.0000 [68,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [69,] 1.0000 1.0000 1.0000 1.0000 0.6875 1.0000 0.3750 1.0000 0.0000 1.0000 [70,] 1.0000 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 0.0000 [71,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7708 1.0000 [72,] 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [73,] 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [74,] 0.5833 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [75,] 1.0000 0.4167 0.4167 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [76,] 1.0000 0.2500 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [77,] 0.7879 1.0000 1.0000 1.0000 0.7045 1.0000 0.4091 0.4545 0.3523 1.0000 [78,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [,71] [,72] [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [1,] 0.4444 0.8148 0.4630 0.4630 0.5926 0.7315 0.2963 0.4815 0.4815 0.4444 [2,] 1.0000 0.0000 1.0000 1.0000 0.2500 0.5833 1.0000 1.0000 1.0000 1.0000 [3,] 1.0000 0.6667 1.0000 1.0000 0.2500 0.1667 1.0000 0.3333 1.0000 0.6667 [4,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 1.0000 1.0000 [5,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 1.0000 1.0000 [6,] 1.0000 0.3333 1.0000 1.0000 0.2500 0.1667 1.0000 0.3333 1.0000 1.0000 [7,] 1.0000 0.6667 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 0.3333 [8,] 0.6111 1.0000 0.4444 0.7222 1.0000 1.0000 0.4141 1.0000 1.0000 1.0000 [9,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [10,] 0.7333 1.0000 0.6500 1.0000 1.0000 1.0000 0.5636 1.0000 1.0000 1.0000 [11,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 0.5758 1.0000 0.3333 1.0000 [12,] 0.7333 1.0000 1.0000 1.0000 0.6333 1.0000 0.8545 1.0000 0.4000 0.4667 [13,] 0.4318 0.6212 0.4545 1.0000 0.6818 0.4545 0.2500 0.4773 0.4773 1.0000 [14,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [15,] 1.0000 0.3333 1.0000 1.0000 0.4167 0.2500 1.0000 1.0000 1.0000 1.0000 [16,] 1.0000 0.7083 1.0000 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 0.1250 [17,] 1.0000 1.0000 1.0000 1.0000 0.4167 1.0000 1.0000 1.0000 1.0000 0.3333 [18,] 1.0000 0.3333 1.0000 1.0000 0.2500 0.1667 1.0000 0.3333 1.0000 1.0000 [19,] 0.7619 1.0000 0.6786 0.3571 1.0000 1.0000 0.7662 1.0000 1.0000 1.0000 [20,] 0.4348 0.6232 0.4565 0.4565 0.7899 0.7283 0.3953 1.0000 0.4783 1.0000 [21,] 0.6167 0.4250 0.4500 1.0000 0.3500 0.4500 0.5068 0.4750 1.0000 0.6167 [22,] 1.0000 0.5833 1.0000 1.0000 0.3333 0.0000 1.0000 0.2500 1.0000 1.0000 [23,] 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 1.0000 0.3333 1.0000 1.0000 0.4167 1.0000 1.0000 1.0000 1.0000 1.0000 [25,] 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 0.6667 1.0000 1.0000 0.2500 0.5833 1.0000 0.3333 1.0000 0.6667 [27,] 1.0000 0.6667 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 0.3333 [28,] 1.0000 1.0000 1.0000 1.0000 0.4167 0.2500 1.0000 0.0000 1.0000 1.0000 [29,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 1.0000 1.0000 [30,] 1.0000 0.3333 1.0000 1.0000 0.4167 1.0000 1.0000 1.0000 1.0000 1.0000 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 1.0000 1.0000 [32,] 1.0000 1.0000 1.0000 1.0000 0.4167 0.2500 1.0000 0.0000 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 [34,] 1.0000 1.0000 1.0000 1.0000 0.4167 1.0000 1.0000 1.0000 1.0000 0.3333 [35,] 1.0000 0.0000 1.0000 1.0000 0.2500 0.5833 1.0000 1.0000 1.0000 1.0000 [36,] 0.4531 0.4531 0.4688 0.4688 0.4062 0.4688 0.3281 0.4844 0.4844 0.6354 [37,] 1.0000 0.5833 0.5000 1.0000 0.6667 0.5000 1.0000 1.0000 1.0000 1.0000 [38,] 1.0000 0.3333 1.0000 1.0000 0.4167 0.2500 1.0000 1.0000 1.0000 1.0000 [39,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 0.0000 1.0000 [40,] 1.0000 0.3333 1.0000 1.0000 0.2500 0.1667 1.0000 0.3333 1.0000 1.0000 [41,] 1.0000 0.3333 1.0000 1.0000 0.5000 0.5833 1.0000 1.0000 1.0000 1.0000 [42,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4886 1.0000 1.0000 1.0000 [43,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 1.0000 1.0000 [44,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.3636 1.0000 1.0000 1.0000 [45,] 1.0000 1.0000 1.0000 1.0000 0.4167 0.2500 1.0000 0.0000 1.0000 1.0000 [46,] 0.5667 1.0000 0.4000 1.0000 1.0000 1.0000 0.6182 1.0000 1.0000 1.0000 [47,] 1.0000 0.3333 1.0000 1.0000 0.4167 0.2500 1.0000 1.0000 1.0000 1.0000 [48,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [49,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4886 1.0000 1.0000 1.0000 [50,] 1.0000 0.3333 1.0000 1.0000 0.4167 1.0000 1.0000 1.0000 1.0000 1.0000 [51,] 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [52,] 1.0000 1.0000 0.5000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [53,] 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 0.6591 1.0000 1.0000 1.0000 [54,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [55,] 1.0000 1.0000 1.0000 1.0000 0.4167 0.2500 1.0000 0.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [57,] 1.0000 1.0000 0.1667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [58,] 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6591 1.0000 1.0000 1.0000 [60,] 1.0000 0.4167 1.0000 1.0000 0.1667 0.2500 1.0000 0.3750 1.0000 0.7083 [61,] 0.6667 1.0000 1.0000 0.5833 1.0000 1.0000 0.7879 1.0000 1.0000 1.0000 [62,] 1.0000 0.3333 1.0000 1.0000 0.4167 0.2500 1.0000 1.0000 1.0000 1.0000 [63,] 1.0000 1.0000 1.0000 1.0000 0.4167 0.2500 1.0000 0.0000 1.0000 1.0000 [64,] 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [65,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7045 1.0000 1.0000 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4091 1.0000 1.0000 1.0000 [68,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 1.0000 1.0000 [69,] 0.7708 1.0000 1.0000 1.0000 1.0000 1.0000 0.3523 1.0000 1.0000 1.0000 [70,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [71,] 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3636 1.0000 0.3333 1.0000 [72,] 1.0000 0.0000 1.0000 1.0000 0.2500 0.5833 1.0000 1.0000 1.0000 1.0000 [73,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [75,] 1.0000 0.2500 1.0000 1.0000 0.0000 0.3333 1.0000 0.4167 1.0000 0.5000 [76,] 1.0000 0.5833 1.0000 1.0000 0.3333 0.0000 1.0000 0.2500 1.0000 1.0000 [77,] 0.3636 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 0.4545 1.0000 [78,] 1.0000 1.0000 1.0000 1.0000 0.4167 0.2500 1.0000 0.0000 1.0000 1.0000 [79,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 0.0000 1.0000 [80,] 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.0000 > > > > cleanEx() > nameEx("kykladspecreg") > ### * kykladspecreg > > flush(stderr()); flush(stdout()) > > ### Name: kykladspecreg > ### Title: Snail presence-absence data from Aegean sea > ### Aliases: kykladspecreg > ### Keywords: datasets > > ### ** Examples > > data(kykladspecreg) > > > > cleanEx() > nameEx("lcomponent") > ### * lcomponent > > flush(stderr()); flush(stdout()) > > ### Name: lcomponent > ### Title: Largest connectivity component > ### Aliases: lcomponent > ### Keywords: cluster > > ### ** Examples > > data(kykladspecreg) > j <- jaccard(t(kykladspecreg)) > lcomponent(j) $lc [1] 8 $ne [1] 60 > > > > cleanEx() > nameEx("lociplots") > ### * lociplots > > flush(stderr()); flush(stdout()) > > ### Name: lociplots > ### Title: Visualises clusters of markers vs. species > ### Aliases: lociplots > ### Keywords: cluster > > ### ** Examples > > > > > cleanEx() > nameEx("nastats") > ### * nastats > > flush(stderr()); flush(stdout()) > > ### Name: nastats > ### Title: Missing values statistics for matrix > ### Aliases: nastats > ### Keywords: manip > > ### ** Examples > > xx <- cbind(c(1,2,3),c(0,0,1),c(5,3,1)) > nastats(xx,nastr=0) $narow [1] 1 1 0 $nacol [1] 0 2 0 > > > > cleanEx() > nameEx("nb") > ### * nb > > flush(stderr()); flush(stdout()) > > ### Name: nb > ### Title: Neighborhood list for Aegean islands > ### Aliases: nb > ### Keywords: datasets > > ### ** Examples > > data(nb) > # nb <- list() > # for (i in 1:34) > # nb <- c(nb,list(scan(file="(path/)nb.dat", > # skip=i-1,nlines=1))) > > > > cleanEx() > nameEx("nbtest") > ### * nbtest > > flush(stderr()); flush(stdout()) > > ### Name: nbtest > ### Title: Test of neighborhood list > ### Aliases: nbtest > ### Keywords: spatial > > ### ** Examples > > data(nb) > nbtest(nb) > nb[[1]][1] <- 1 > try(nbtest(nb)) 1 is neighbor of itself. 2 is neighbor of 1 but 1 is not neighbor of 2 . Error in nbtest(nb) : Improper neighborhood list. > > > > cleanEx() > nameEx("nn") > ### * nn > > flush(stderr()); flush(stdout()) > > ### Name: nn > ### Title: Mean distance to kth nearest neighbor > ### Aliases: nn > ### Keywords: cluster > > ### ** Examples > > data(kykladspecreg) > j <- jaccard(t(kykladspecreg)) > nn(j,4) [1] 0.5727 > > > > cleanEx() > nameEx("phipt") > ### * phipt > > flush(stderr()); flush(stdout()) > > ### Name: phipt > ### Title: Distances between communities, auxiliary functions > ### Aliases: phipt cfchord shared.problist diploidcomlist > ### Keywords: spatial multivariate > > ### ** Examples > > options(digits=4) > data(tetragonula) > tnb <- + coord2dist(coordmatrix=tetragonula.coord[83:120,],cut=50,file.format="decimal2",neighbors=TRUE) > ta <- alleleconvert(strmatrix=tetragonula[83:120,]) > tai <- alleleinit(allelematrix=ta,neighborhood=tnb$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) $phipt [1] 0.5804 $vap [1] 0.266 $n0 [1] 1.333 $sst [1] 0.7393 $ssg [1] NA NA NA 0.0000 NA 0.1923 $msa [1] 0.547 $msw [1] 0.1923 > tdip <- diploidcomlist(tai,tetracoms,diploid=TRUE) > cfchord(tdip[[4]],tdip[[6]]) [1] 3.108 > shared.problist(tdip[[4]],tdip[[6]]) [1] 0.08333 > > > > > cleanEx() > nameEx("piecewiselin") > ### * piecewiselin > > flush(stderr()); flush(stdout()) > > ### Name: piecewiselin > ### Title: Piecewise linear transformation for distance matrices > ### Aliases: piecewiselin > ### Keywords: cluster spatial > > ### ** Examples > > options(digits=4) > data(waterdist) > piecewiselin(waterdist) V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 1 0.0000 0.1389 0.7639 0.9028 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 2 0.1389 0.0000 0.6250 0.7639 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 3 0.7639 0.6250 0.0000 0.1389 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 4 0.9028 0.7639 0.1389 0.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 5 1.0000 1.0000 1.0000 1.0000 0.0000 0.9722 1.0000 1 1.0000 1.0000 1.00000 6 1.0000 1.0000 1.0000 1.0000 0.9722 0.0000 0.7639 1 1.0000 1.0000 1.00000 7 1.0000 1.0000 1.0000 1.0000 1.0000 0.7639 0.0000 1 0.9028 1.0000 1.00000 8 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0 1.0000 1.0000 1.00000 9 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9028 1 0.0000 0.9028 1.00000 10 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 0.9028 0.0000 0.83333 11 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 0.8333 0.00000 12 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 0.9722 0.13889 13 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 0.9028 0.06944 14 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 15 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 16 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 17 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 18 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 19 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 20 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 21 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 22 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 23 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 24 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 25 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 26 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 27 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 28 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 29 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 30 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 31 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 32 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 33 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 34 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 1.0000 1.0000 1.00000 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 1 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 2 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 3 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 4 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 5 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 6 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 7 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 8 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 9 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 10 0.9722 0.90278 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 11 0.1389 0.06944 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 12 0.0000 0.20833 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 13 0.2083 0.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 14 1.0000 1.00000 0.0000 0.6250 0.9722 1.0000 1.00000 1.00000 1.0000 1.0000 15 1.0000 1.00000 0.6250 0.0000 0.3472 0.9722 1.00000 1.00000 1.0000 1.0000 16 1.0000 1.00000 0.9722 0.3472 0.0000 0.6250 1.00000 1.00000 0.9722 1.0000 17 1.0000 1.00000 1.0000 0.9722 0.6250 0.0000 0.83333 0.76389 0.3472 0.6250 18 1.0000 1.00000 1.0000 1.0000 1.0000 0.8333 0.00000 0.06944 0.4861 0.7639 19 1.0000 1.00000 1.0000 1.0000 1.0000 0.7639 0.06944 0.00000 0.4167 0.6944 20 1.0000 1.00000 1.0000 1.0000 0.9722 0.3472 0.48611 0.41667 0.0000 0.2778 21 1.0000 1.00000 1.0000 1.0000 1.0000 0.6250 0.76389 0.69444 0.2778 0.0000 22 1.0000 1.00000 1.0000 1.0000 1.0000 0.8333 0.97222 0.90278 0.4861 0.2083 23 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 0.9028 24 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 25 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 26 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 27 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 28 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 29 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 30 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 31 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 32 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 33 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 34 1.0000 1.00000 1.0000 1.0000 1.0000 1.0000 1.00000 1.00000 1.0000 1.0000 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 1 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 2 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 3 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 4 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 5 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 6 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 7 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 8 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 9 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 10 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 11 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 12 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 13 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 14 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 15 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 16 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 17 0.8333 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 18 0.9722 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 19 0.9028 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 20 0.4861 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 21 0.2083 0.9028 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 22 0.0000 0.6944 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 23 0.6944 0.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 24 1.0000 1.0000 0 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 25 1.0000 1.0000 1 0 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 26 1.0000 1.0000 1 1 0 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 1 27 1.0000 1.0000 1 1 1 0.0000 1 0.6250 1.0000 1.0000 0.9722 0.7639 1 28 1.0000 1.0000 1 1 1 1.0000 0 1.0000 1.0000 1.0000 1.0000 1.0000 1 29 1.0000 1.0000 1 1 1 0.6250 1 0.0000 1.0000 0.9028 0.6250 0.4167 1 30 1.0000 1.0000 1 1 1 1.0000 1 1.0000 0.0000 0.1389 0.4167 0.6250 1 31 1.0000 1.0000 1 1 1 1.0000 1 0.9028 0.1389 0.0000 0.2778 0.4861 1 32 1.0000 1.0000 1 1 1 0.9722 1 0.6250 0.4167 0.2778 0.0000 0.2083 1 33 1.0000 1.0000 1 1 1 0.7639 1 0.4167 0.6250 0.4861 0.2083 0.0000 1 34 1.0000 1.0000 1 1 1 1.0000 1 1.0000 1.0000 1.0000 1.0000 1.0000 0 > > > > cleanEx() > nameEx("plotdistreg") > ### * plotdistreg > > flush(stderr()); flush(stdout()) > > ### Name: plotdistreg > ### Title: Plots for within-groups and between-groups distance regression > ### Aliases: plotdistreg > ### Keywords: htest regression spatial > > ### ** 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)) > > > > > > cleanEx() > nameEx("pop.sim") > ### * pop.sim > > flush(stderr()); flush(stdout()) > > ### Name: pop.sim > ### Title: p-value simulation for presence-absence matrices clustering test > ### Aliases: pop.sim > ### Keywords: cluster htest > > ### ** 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) Simulation run 1 statistics value= 0.3411 Simulation run 2 statistics value= 0.3216 Simulation run 3 statistics value= 0.3327 Simulation run 4 statistics value= 0.3312 Simulation run 5 statistics value= 0.3324 Data value: 0.2715 $results [1] 0.3411 0.3216 0.3327 0.3312 0.3324 $p.above [1] 1 $p.below [1] 0.1667 $datac [1] 0.2715 $testc [1] 3 > > > > cleanEx() > nameEx("prab.sarestimate") > ### * prab.sarestimate > > flush(stderr()); flush(stdout()) > > ### Name: prab.sarestimate > ### Title: Estimates SAR model from log-abundance matrix of prab-object. > ### Aliases: prab.sarestimate > ### Keywords: spatial > > ### ** 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) $sar [1] FALSE $intercept (Intercept) 1.099 $sigma [1] 1.247 $regeffects region2 region3 region4 region5 region6 0.00000 -0.07355 0.49432 0.68160 0.60247 0.79395 $speffects species2 species3 species4 species5 species6 species7 0.00000 -1.09861 -0.40547 -0.40547 -0.16596 -0.16596 0.52719 species8 species9 species10 species11 species12 species13 species14 0.18062 -0.36869 2.46711 2.47182 2.30603 0.80632 -0.54572 species15 species16 species17 species18 species19 species20 species21 -1.00782 2.85032 3.11414 -0.14604 -1.25954 0.83431 0.08007 species22 species23 species24 species25 species26 species27 species28 -2.00564 -0.65161 2.63456 4.32596 3.37032 2.71942 2.62771 species29 species30 species31 species32 species33 species34 species35 2.74067 1.55140 1.84436 2.92940 1.70625 2.91888 2.58579 species36 species37 species38 species39 species40 species41 species42 2.74788 1.04336 0.96179 1.40339 1.94121 -1.83207 1.64401 species43 species44 species45 species46 species47 species48 species49 1.66446 2.26606 2.04573 2.30031 1.10246 3.38612 3.26381 species50 species51 species52 species53 species54 species55 species56 2.73126 2.55095 -0.12882 1.97614 2.30456 -0.33191 -1.02506 species57 species58 species59 species60 species61 species62 species63 -1.02506 -1.02506 -0.33191 2.21389 -0.61585 -0.41312 -1.30900 species64 species65 species66 species67 species68 species69 species70 -2.30798 -2.30798 -1.74512 1.28949 1.95714 1.33182 0.67160 species71 species72 species73 species74 species75 species76 species77 2.09371 -1.59293 -0.89978 -1.59293 0.48651 -1.59293 1.04613 species78 species79 species80 species81 species82 species83 species84 -0.49432 -1.68657 -1.68657 -0.99343 -2.49129 1.77327 0.65449 species85 species86 species87 species88 species89 species90 species91 -2.10515 1.52821 1.33911 1.82843 0.39980 2.59715 -0.17442 species92 species93 species94 species95 species96 species97 species98 -0.42345 0.15602 -1.52283 0.52237 -0.39392 -1.78021 -1.08707 species99 species100 species101 species102 species103 species104 species105 -1.78021 -1.85222 0.56194 -0.07454 -0.19512 -0.41024 1.53796 species106 species107 species108 species109 species110 species111 species112 -0.56166 0.29333 3.47629 1.38764 0.44999 3.22455 -0.48728 species113 species114 species115 species116 species117 species118 species119 1.82139 0.17443 -0.77644 1.03934 1.90289 2.20717 -0.60247 species120 species121 species122 species123 species124 species125 species126 0.09068 0.69682 -1.70108 -1.00793 -0.47729 0.04762 0.36855 species127 species128 species129 species130 species131 species132 species133 -0.16777 -0.82386 0.54425 -0.64553 -0.47729 -1.45025 -0.82386 species134 species135 species136 species137 species138 species139 species140 0.92222 3.17482 1.70106 -0.29895 -1.19941 -1.19941 -1.19941 species141 species142 species143 species144 0.81549 1.32632 -0.28312 -1.19941 $lmobject Call: lm(formula = logabund ~ region + species, data = abundreg) Coefficients: (Intercept) region2 region3 region4 region5 region6 1.0986 -0.0736 0.4943 0.6816 0.6025 0.7939 species2 species3 species4 species5 species6 species7 -1.0986 -0.4055 -0.4055 -0.1660 -0.1660 0.5272 species8 species9 species10 species11 species12 species13 0.1806 -0.3687 2.4671 2.4718 2.3060 0.8063 species14 species15 species16 species17 species18 species19 -0.5457 -1.0078 2.8503 3.1141 -0.1460 -1.2595 species20 species21 species22 species23 species24 species25 0.8343 0.0801 -2.0056 -0.6516 2.6346 4.3260 species26 species27 species28 species29 species30 species31 3.3703 2.7194 2.6277 2.7407 1.5514 1.8444 species32 species33 species34 species35 species36 species37 2.9294 1.7062 2.9189 2.5858 2.7479 1.0434 species38 species39 species40 species41 species42 species43 0.9618 1.4034 1.9412 -1.8321 1.6440 1.6645 species44 species45 species46 species47 species48 species49 2.2661 2.0457 2.3003 1.1025 3.3861 3.2638 species50 species51 species52 species53 species54 species55 2.7313 2.5509 -0.1288 1.9761 2.3046 -0.3319 species56 species57 species58 species59 species60 species61 -1.0251 -1.0251 -1.0251 -0.3319 2.2139 -0.6158 species62 species63 species64 species65 species66 species67 -0.4131 -1.3090 -2.3080 -2.3080 -1.7451 1.2895 species68 species69 species70 species71 species72 species73 1.9571 1.3318 0.6716 2.0937 -1.5929 -0.8998 species74 species75 species76 species77 species78 species79 -1.5929 0.4865 -1.5929 1.0461 -0.4943 -1.6866 species80 species81 species82 species83 species84 species85 -1.6866 -0.9934 -2.4913 1.7733 0.6545 -2.1052 species86 species87 species88 species89 species90 species91 1.5282 1.3391 1.8284 0.3998 2.5971 -0.1744 species92 species93 species94 species95 species96 species97 -0.4234 0.1560 -1.5228 0.5224 -0.3939 -1.7802 species98 species99 species100 species101 species102 species103 -1.0871 -1.7802 -1.8522 0.5619 -0.0745 -0.1951 species104 species105 species106 species107 species108 species109 -0.4102 1.5380 -0.5617 0.2933 3.4763 1.3876 species110 species111 species112 species113 species114 species115 0.4500 3.2245 -0.4873 1.8214 0.1744 -0.7764 species116 species117 species118 species119 species120 species121 1.0393 1.9029 2.2072 -0.6025 0.0907 0.6968 species122 species123 species124 species125 species126 species127 -1.7011 -1.0079 -0.4773 0.0476 0.3685 -0.1678 species128 species129 species130 species131 species132 species133 -0.8239 0.5442 -0.6455 -0.4773 -1.4502 -0.8239 species134 species135 species136 species137 species138 species139 0.9222 3.1748 1.7011 -0.2990 -1.1994 -1.1994 species140 species141 species142 species143 species144 -1.1994 0.8155 1.3263 -0.2831 -1.1994 > > > > cleanEx() > nameEx("prabclust") > ### * prabclust > > flush(stderr()); flush(stdout()) > > ### Name: prabclust > ### Title: Clustering for biotic elements or for species delimitation > ### (mixture method) > ### Aliases: prabclust print.prabclust > ### Keywords: cluster spatial > > ### ** Examples > > > > > cleanEx() > nameEx("prabinit") > ### * prabinit > > flush(stderr()); flush(stdout()) > > ### Name: prabinit > ### Title: Presence-absence/abundance matrix initialization > ### Aliases: prabinit print.prab prab > ### Keywords: spatial cluster > > ### ** 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) Presence-absence matrix object with 80 species and 34 regions, including regions neighborhoods and between-species distance matrix of type kulczynski . > > > > cleanEx() > nameEx("prabtest") > ### * prabtest > > flush(stderr()); flush(stdout()) > > ### Name: prabtest > ### Title: Parametric bootstrap test for clustering in presence-absence > ### matrices > ### Aliases: prabtest summary.prabtest print.summary.prabtest > ### Keywords: cluster spatial > > ### ** 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) Simulation run 1 statistics value= 0.5082 Simulation run 2 statistics value= 0.4583 Simulation run 3 statistics value= 0.4407 Simulation run 4 statistics value= 0.4422 Simulation run 5 statistics value= 0.5478 Data value: 0.408 > # These settings are chosen to make the example execution > # a bit faster; usually you will use prabtest(kprab). > summary(kpt) * Parametric bootstrap test for presence-absence data * Test statistics: distratio , Tuning constant= 0.25 Distance: kulczynski Simulation runs: 5 Disjunction parameter for presence-absence pattern: 0.35 Statistics value for original data: 0.408 Mean for null data: 0.4795 , range: 0.4407 0.5478 p= 0.1667 > > > > cleanEx() > nameEx("qkulczynski") > ### * qkulczynski > > flush(stderr()); flush(stdout()) > > ### Name: qkulczynski > ### Title: Quantitative Kulczynski distance matrix > ### Aliases: qkulczynski > ### Keywords: cluster spatial > > ### ** Examples > > options(digits=4) > data(kykladspecreg) > qkulczynski(t(kykladspecreg)) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 0.0000 0.8148 0.6296 0.4815 0.4815 0.8148 0.4444 0.3056 0.4630 0.4074 [2,] 0.8148 0.0000 0.6667 1.0000 1.0000 0.3333 0.6667 1.0000 1.0000 1.0000 [3,] 0.6296 0.6667 0.0000 1.0000 1.0000 0.3333 0.6667 1.0000 1.0000 1.0000 [4,] 0.4815 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 0.4722 1.0000 1.0000 [5,] 0.4815 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 0.4722 1.0000 0.4000 [6,] 0.8148 0.3333 0.3333 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [7,] 0.4444 0.6667 0.6667 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [8,] 0.3056 1.0000 1.0000 0.4722 0.4722 1.0000 1.0000 0.0000 0.4444 0.3611 [9,] 0.4630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4444 0.0000 0.6500 [10,] 0.4074 1.0000 1.0000 1.0000 0.4000 1.0000 1.0000 0.3611 0.6500 0.0000 [11,] 0.6296 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 0.8056 1.0000 1.0000 [12,] 0.5259 1.0000 0.7333 1.0000 1.0000 1.0000 0.4667 1.0000 1.0000 1.0000 [13,] 0.2163 0.6212 0.6212 0.4773 0.4773 0.4318 1.0000 0.3434 0.4545 0.3864 [14,] 0.4630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7222 1.0000 1.0000 [15,] 1.0000 0.3333 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [16,] 0.4259 0.7083 0.7083 1.0000 1.0000 1.0000 0.1250 1.0000 1.0000 1.0000 [17,] 0.4815 1.0000 0.3333 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [18,] 0.8148 0.3333 0.3333 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [19,] 0.4603 1.0000 1.0000 0.4286 1.0000 1.0000 1.0000 0.4048 0.6786 0.8286 [20,] 0.3156 0.6232 0.8116 0.4783 0.4783 0.6232 1.0000 0.2572 0.4565 0.5130 [21,] 0.2602 0.4250 0.4250 0.4750 0.4750 0.4250 0.4250 0.4722 0.4500 0.3750 [22,] 0.7315 0.5833 0.1667 1.0000 1.0000 0.1667 1.0000 1.0000 1.0000 1.0000 [23,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 1.0000 0.3333 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [25,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4722 1.0000 1.0000 [26,] 0.4444 0.6667 0.6667 1.0000 1.0000 0.6667 0.3333 1.0000 1.0000 1.0000 [27,] 0.4444 0.6667 0.6667 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [28,] 0.4815 1.0000 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [29,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [30,] 1.0000 0.3333 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [31,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4722 1.0000 1.0000 [32,] 0.4815 1.0000 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [33,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 0.4815 1.0000 0.3333 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [35,] 0.8148 0.0000 0.6667 1.0000 1.0000 0.3333 0.6667 1.0000 1.0000 1.0000 [36,] 0.1123 0.4531 0.4531 0.4844 0.4844 0.4531 0.4531 0.2622 0.4688 0.4219 [37,] 0.7315 0.5833 0.5833 1.0000 1.0000 0.5833 1.0000 0.7222 0.5000 0.6500 [38,] 1.0000 0.3333 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [39,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 0.8148 0.3333 0.3333 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [41,] 0.8148 0.3333 0.6667 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [42,] 0.5694 1.0000 1.0000 0.3750 0.3750 1.0000 1.0000 0.5417 1.0000 0.7750 [43,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [44,] 0.4444 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4167 1.0000 0.7333 [45,] 0.4815 1.0000 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [46,] 0.3833 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3778 0.4000 0.5500 [47,] 1.0000 0.3333 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [48,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [49,] 0.4259 1.0000 1.0000 1.0000 0.3750 1.0000 1.0000 0.6944 1.0000 0.7750 [50,] 1.0000 0.3333 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [51,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4722 0.2500 1.0000 [52,] 0.4630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7222 0.5000 1.0000 [53,] 0.4259 1.0000 1.0000 1.0000 0.3750 1.0000 1.0000 0.3889 0.2500 0.3250 [54,] 0.4630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7222 1.0000 1.0000 [55,] 0.4815 1.0000 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4167 1.0000 1.0000 [57,] 0.4444 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4167 0.1667 0.4667 [58,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 0.4259 1.0000 1.0000 0.3750 0.3750 1.0000 1.0000 0.3889 1.0000 0.7750 [60,] 0.7130 0.4167 0.1250 1.0000 1.0000 0.1250 0.7083 1.0000 1.0000 1.0000 [61,] 0.4444 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6111 1.0000 0.4667 [62,] 1.0000 0.3333 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [63,] 0.4815 1.0000 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [64,] 0.5694 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.5417 0.2500 0.5500 [65,] 0.7315 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 0.7222 1.0000 0.6500 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4167 1.0000 1.0000 [67,] 0.4630 1.0000 1.0000 0.2500 0.2500 1.0000 1.0000 0.4444 1.0000 0.6500 [68,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4722 1.0000 0.4000 [69,] 0.3519 1.0000 1.0000 0.4375 0.4375 1.0000 1.0000 0.3681 1.0000 0.8375 [70,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [71,] 0.4444 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6111 1.0000 0.7333 [72,] 0.8148 0.0000 0.6667 1.0000 1.0000 0.3333 0.6667 1.0000 1.0000 1.0000 [73,] 0.4630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4444 0.0000 0.6500 [74,] 0.4630 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7222 1.0000 1.0000 [75,] 0.5926 0.2500 0.2500 1.0000 1.0000 0.2500 0.2500 1.0000 1.0000 1.0000 [76,] 0.7315 0.5833 0.1667 1.0000 1.0000 0.1667 1.0000 1.0000 1.0000 1.0000 [77,] 0.2963 1.0000 1.0000 0.4545 0.4545 1.0000 1.0000 0.4141 1.0000 0.5636 [78,] 0.4815 1.0000 0.3333 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 [79,] 0.4815 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 0.4444 1.0000 0.6667 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [1,] 0.6296 0.5259 0.2163 0.4630 1.0000 0.4259 0.4815 0.8148 0.4603 0.3156 [2,] 1.0000 1.0000 0.6212 1.0000 0.3333 0.7083 1.0000 0.3333 1.0000 0.6232 [3,] 1.0000 0.7333 0.6212 1.0000 0.3333 0.7083 0.3333 0.3333 1.0000 0.8116 [4,] 0.3333 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 0.4286 0.4783 [5,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [6,] 1.0000 1.0000 0.4318 1.0000 0.3333 1.0000 1.0000 0.0000 1.0000 0.6232 [7,] 1.0000 0.4667 1.0000 1.0000 1.0000 0.1250 0.3333 1.0000 1.0000 1.0000 [8,] 0.8056 1.0000 0.3434 0.7222 1.0000 1.0000 1.0000 1.0000 0.4048 0.2572 [9,] 1.0000 1.0000 0.4545 1.0000 1.0000 1.0000 1.0000 1.0000 0.6786 0.4565 [10,] 1.0000 1.0000 0.3864 1.0000 1.0000 1.0000 1.0000 1.0000 0.8286 0.5130 [11,] 0.0000 0.4667 0.6212 1.0000 1.0000 1.0000 1.0000 1.0000 0.7619 0.4348 [12,] 0.4667 0.0000 0.8773 1.0000 1.0000 0.5500 0.4000 1.0000 1.0000 0.7565 [13,] 0.6212 0.8773 0.0000 0.7273 0.4773 1.0000 1.0000 0.4318 0.7175 0.2441 [14,] 1.0000 1.0000 0.7273 0.0000 1.0000 1.0000 1.0000 1.0000 0.6786 0.4565 [15,] 1.0000 1.0000 0.4773 1.0000 0.0000 1.0000 1.0000 0.3333 1.0000 0.4783 [16,] 1.0000 0.5500 1.0000 1.0000 1.0000 0.0000 0.3750 1.0000 1.0000 1.0000 [17,] 1.0000 0.4000 1.0000 1.0000 1.0000 0.3750 0.0000 1.0000 1.0000 1.0000 [18,] 1.0000 1.0000 0.4318 1.0000 0.3333 1.0000 1.0000 0.0000 1.0000 0.6232 [19,] 0.7619 1.0000 0.7175 0.6786 1.0000 1.0000 1.0000 1.0000 0.0000 0.3478 [20,] 0.4348 0.7565 0.2441 0.4565 0.4783 1.0000 1.0000 0.6232 0.3478 0.0000 [21,] 0.6167 0.5000 0.2841 0.7250 0.4750 0.5500 0.4750 0.4250 0.7107 0.3457 [22,] 1.0000 1.0000 0.4545 1.0000 0.2500 1.0000 1.0000 0.1667 1.0000 0.7283 [23,] 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 0.4286 0.4783 [24,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 0.4783 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4286 0.4783 [26,] 1.0000 0.7333 0.8106 1.0000 1.0000 0.4167 1.0000 0.6667 1.0000 1.0000 [27,] 1.0000 0.4667 1.0000 1.0000 1.0000 0.1250 0.3333 1.0000 1.0000 1.0000 [28,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 [29,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [30,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 0.4783 [31,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [32,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 0.4000 1.0000 1.0000 1.0000 0.3750 0.0000 1.0000 1.0000 1.0000 [35,] 1.0000 1.0000 0.6212 1.0000 0.3333 0.7083 1.0000 0.3333 1.0000 0.6232 [36,] 0.4531 0.4219 0.1562 0.4688 0.4844 0.5781 0.4844 0.4531 0.3906 0.1406 [37,] 1.0000 1.0000 0.4545 1.0000 0.2500 1.0000 1.0000 0.5833 1.0000 0.4565 [38,] 1.0000 1.0000 0.4773 1.0000 0.0000 1.0000 1.0000 0.3333 1.0000 0.4783 [39,] 0.3333 0.4000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [40,] 1.0000 1.0000 0.4318 1.0000 0.3333 1.0000 1.0000 0.0000 1.0000 0.6232 [41,] 1.0000 1.0000 0.4318 1.0000 0.3333 1.0000 1.0000 0.3333 1.0000 0.6232 [42,] 0.4167 0.7750 0.5568 1.0000 1.0000 1.0000 1.0000 1.0000 0.8036 0.5598 [43,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [44,] 1.0000 1.0000 0.4318 1.0000 1.0000 1.0000 1.0000 1.0000 0.7619 0.4348 [45,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 [46,] 1.0000 1.0000 0.3455 0.7000 1.0000 1.0000 1.0000 1.0000 0.6357 0.3543 [47,] 1.0000 1.0000 0.4773 1.0000 0.0000 1.0000 1.0000 0.3333 1.0000 0.4783 [48,] 0.3333 0.4000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [49,] 1.0000 1.0000 0.4091 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4130 [50,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 0.4783 [51,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 0.4286 0.4783 [52,] 1.0000 1.0000 0.7273 0.5000 1.0000 1.0000 1.0000 1.0000 0.3571 0.4565 [53,] 1.0000 1.0000 0.4091 1.0000 1.0000 1.0000 1.0000 1.0000 0.8036 0.4130 [54,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.3571 0.4565 [55,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7619 0.6232 [57,] 1.0000 1.0000 0.4318 1.0000 1.0000 1.0000 1.0000 1.0000 0.7619 0.6232 [58,] 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 0.4286 0.4783 [59,] 0.7083 1.0000 0.4091 0.6250 1.0000 1.0000 1.0000 1.0000 0.8036 0.4130 [60,] 1.0000 0.7750 0.5568 1.0000 0.3750 0.7500 0.3750 0.1250 1.0000 0.7065 [61,] 1.0000 1.0000 0.6212 0.5833 1.0000 1.0000 1.0000 1.0000 0.5238 0.6232 [62,] 1.0000 1.0000 0.4773 1.0000 0.0000 1.0000 1.0000 0.3333 1.0000 0.4783 [63,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 [64,] 1.0000 1.0000 0.4091 1.0000 1.0000 1.0000 1.0000 1.0000 0.8036 0.5598 [65,] 0.5833 0.6500 0.7273 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4565 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7619 0.6232 [67,] 0.5833 1.0000 0.4545 1.0000 1.0000 1.0000 1.0000 1.0000 0.6786 0.4565 [68,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [69,] 0.7708 1.0000 0.3182 0.6875 1.0000 1.0000 1.0000 1.0000 0.8661 0.4103 [70,] 0.3333 0.4000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [71,] 0.6667 0.7333 0.4318 1.0000 1.0000 1.0000 1.0000 1.0000 0.7619 0.4348 [72,] 1.0000 1.0000 0.6212 1.0000 0.3333 0.7083 1.0000 0.3333 1.0000 0.6232 [73,] 1.0000 1.0000 0.4545 1.0000 1.0000 1.0000 1.0000 1.0000 0.6786 0.4565 [74,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.3571 0.4565 [75,] 1.0000 0.6333 0.6818 1.0000 0.4167 0.3750 0.4167 0.2500 1.0000 0.7899 [76,] 1.0000 1.0000 0.4545 1.0000 0.2500 1.0000 1.0000 0.1667 1.0000 0.7283 [77,] 0.5758 0.8545 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 0.7662 0.3953 [78,] 1.0000 1.0000 0.4773 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 [79,] 0.3333 0.4000 0.4773 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4783 [80,] 1.0000 0.4667 1.0000 1.0000 1.0000 0.1250 0.3333 1.0000 1.0000 1.0000 [,21] [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30] [1,] 0.2602 0.7315 0.4815 1.0000 0.4815 0.4444 0.4444 0.4815 0.4815 1.0000 [2,] 0.4250 0.5833 1.0000 0.3333 1.0000 0.6667 0.6667 1.0000 1.0000 0.3333 [3,] 0.4250 0.1667 1.0000 1.0000 1.0000 0.6667 0.6667 0.3333 1.0000 1.0000 [4,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [5,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [6,] 0.4250 0.1667 1.0000 0.3333 1.0000 0.6667 1.0000 0.3333 1.0000 0.3333 [7,] 0.4250 1.0000 1.0000 1.0000 1.0000 0.3333 0.0000 1.0000 1.0000 1.0000 [8,] 0.4722 1.0000 1.0000 1.0000 0.4722 1.0000 1.0000 1.0000 1.0000 1.0000 [9,] 0.4500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [10,] 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [11,] 0.6167 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [12,] 0.5000 1.0000 1.0000 1.0000 1.0000 0.7333 0.4667 1.0000 1.0000 1.0000 [13,] 0.2841 0.4545 1.0000 0.4773 1.0000 0.8106 1.0000 0.4773 0.4773 0.4773 [14,] 0.7250 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [15,] 0.4750 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [16,] 0.5500 1.0000 1.0000 1.0000 1.0000 0.4167 0.1250 1.0000 1.0000 1.0000 [17,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [18,] 0.4250 0.1667 1.0000 0.3333 1.0000 0.6667 1.0000 0.3333 1.0000 0.3333 [19,] 0.7107 1.0000 0.4286 1.0000 0.4286 1.0000 1.0000 1.0000 1.0000 1.0000 [20,] 0.3457 0.7283 0.4783 0.4783 0.4783 1.0000 1.0000 1.0000 0.4783 0.4783 [21,] 0.0000 0.4500 1.0000 0.4750 1.0000 0.4250 0.4250 0.4750 0.4750 0.4750 [22,] 0.4500 0.0000 1.0000 1.0000 1.0000 0.5833 1.0000 0.2500 1.0000 1.0000 [23,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 0.4750 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [25,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 0.4250 0.5833 1.0000 1.0000 1.0000 0.0000 0.3333 0.3333 1.0000 1.0000 [27,] 0.4250 1.0000 1.0000 1.0000 1.0000 0.3333 0.0000 1.0000 1.0000 1.0000 [28,] 0.4750 0.2500 1.0000 1.0000 1.0000 0.3333 1.0000 0.0000 1.0000 1.0000 [29,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 [30,] 0.4750 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [32,] 0.4750 0.2500 1.0000 1.0000 1.0000 0.3333 1.0000 0.0000 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [35,] 0.4250 0.5833 1.0000 0.3333 1.0000 0.6667 0.6667 1.0000 1.0000 0.3333 [36,] 0.1875 0.4688 0.4844 0.4844 0.4844 0.4531 0.4531 0.4844 0.4844 0.4844 [37,] 0.4500 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [38,] 0.4750 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 0.4250 0.1667 1.0000 0.3333 1.0000 0.6667 1.0000 0.3333 1.0000 0.3333 [41,] 0.6167 0.5833 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 [42,] 0.5500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [43,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 [44,] 0.4250 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [45,] 0.4750 0.2500 1.0000 1.0000 1.0000 0.3333 1.0000 0.0000 1.0000 1.0000 [46,] 0.3250 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4500 1.0000 [47,] 0.4750 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [48,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [49,] 0.4000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 1.0000 [50,] 0.4750 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [51,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [52,] 0.7250 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [53,] 0.4000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [54,] 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [55,] 0.4750 0.2500 1.0000 1.0000 1.0000 0.3333 1.0000 0.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [57,] 0.4250 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [58,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 0.5500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [60,] 0.4000 0.2500 1.0000 0.3750 1.0000 0.7083 0.7083 0.3750 1.0000 0.3750 [61,] 0.6167 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [62,] 0.4750 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [63,] 0.4750 0.2500 1.0000 1.0000 1.0000 0.3333 1.0000 0.0000 1.0000 1.0000 [64,] 0.5500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [65,] 0.4500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [67,] 0.4500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [68,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [69,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [70,] 0.4750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [71,] 0.6167 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [72,] 0.4250 0.5833 1.0000 0.3333 1.0000 0.6667 0.6667 1.0000 1.0000 0.3333 [73,] 0.4500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 0.2500 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 [75,] 0.3500 0.3333 1.0000 0.4167 1.0000 0.2500 0.2500 0.4167 1.0000 0.4167 [76,] 0.4500 0.0000 1.0000 1.0000 1.0000 0.5833 1.0000 0.2500 1.0000 1.0000 [77,] 0.5068 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 [78,] 0.4750 0.2500 1.0000 1.0000 1.0000 0.3333 1.0000 0.0000 1.0000 1.0000 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 0.6167 1.0000 1.0000 1.0000 1.0000 0.6667 0.3333 1.0000 1.0000 1.0000 [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] [,39] [,40] [1,] 0.4815 0.4815 0.4815 0.4815 0.8148 0.1123 0.7315 1.0000 0.4815 0.8148 [2,] 1.0000 1.0000 1.0000 1.0000 0.0000 0.4531 0.5833 0.3333 1.0000 0.3333 [3,] 1.0000 0.3333 1.0000 0.3333 0.6667 0.4531 0.5833 0.3333 1.0000 0.3333 [4,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [5,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [6,] 1.0000 0.3333 1.0000 1.0000 0.3333 0.4531 0.5833 0.3333 1.0000 0.0000 [7,] 1.0000 1.0000 1.0000 0.3333 0.6667 0.4531 1.0000 1.0000 1.0000 1.0000 [8,] 0.4722 1.0000 1.0000 1.0000 1.0000 0.2622 0.7222 1.0000 1.0000 1.0000 [9,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4688 0.5000 1.0000 1.0000 1.0000 [10,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4219 0.6500 1.0000 1.0000 1.0000 [11,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4531 1.0000 1.0000 0.3333 1.0000 [12,] 1.0000 1.0000 1.0000 0.4000 1.0000 0.4219 1.0000 1.0000 0.4000 1.0000 [13,] 0.4773 0.4773 1.0000 1.0000 0.6212 0.1562 0.4545 0.4773 0.4773 0.4318 [14,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4688 1.0000 1.0000 1.0000 1.0000 [15,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.4844 0.2500 0.0000 1.0000 0.3333 [16,] 1.0000 1.0000 0.3750 0.3750 0.7083 0.5781 1.0000 1.0000 1.0000 1.0000 [17,] 1.0000 1.0000 1.0000 0.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [18,] 1.0000 0.3333 1.0000 1.0000 0.3333 0.4531 0.5833 0.3333 1.0000 0.0000 [19,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.3906 1.0000 1.0000 1.0000 1.0000 [20,] 0.4783 1.0000 1.0000 1.0000 0.6232 0.1406 0.4565 0.4783 0.4783 0.6232 [21,] 1.0000 0.4750 1.0000 0.4750 0.4250 0.1875 0.4500 0.4750 1.0000 0.4250 [22,] 1.0000 0.2500 1.0000 1.0000 0.5833 0.4688 0.5000 0.2500 1.0000 0.1667 [23,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [24,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.4844 1.0000 1.0000 1.0000 0.3333 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 0.3333 1.0000 1.0000 0.6667 0.4531 1.0000 1.0000 1.0000 0.6667 [27,] 1.0000 1.0000 1.0000 0.3333 0.6667 0.4531 1.0000 1.0000 1.0000 1.0000 [28,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 0.3333 [29,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [30,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.4844 1.0000 1.0000 1.0000 0.3333 [31,] 0.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [32,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 0.3333 [33,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 1.0000 1.0000 0.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [35,] 1.0000 1.0000 1.0000 1.0000 0.0000 0.4531 0.5833 0.3333 1.0000 0.3333 [36,] 0.4844 0.4844 1.0000 0.4844 0.4531 0.0000 0.4688 0.4844 0.4844 0.4531 [37,] 1.0000 1.0000 1.0000 1.0000 0.5833 0.4688 0.0000 0.2500 1.0000 0.5833 [38,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.4844 0.2500 0.0000 1.0000 0.3333 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 0.0000 1.0000 [40,] 1.0000 0.3333 1.0000 1.0000 0.3333 0.4531 0.5833 0.3333 1.0000 0.0000 [41,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.4531 0.5833 0.3333 1.0000 0.3333 [42,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4375 1.0000 1.0000 1.0000 1.0000 [43,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [44,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4531 1.0000 1.0000 1.0000 1.0000 [45,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 0.3333 [46,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.3438 0.7000 1.0000 1.0000 1.0000 [47,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.4844 0.2500 0.0000 1.0000 0.3333 [48,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [49,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4375 1.0000 1.0000 1.0000 1.0000 [50,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.4844 1.0000 1.0000 1.0000 0.3333 [51,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [52,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4688 1.0000 1.0000 1.0000 1.0000 [53,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4375 0.6250 1.0000 1.0000 1.0000 [54,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4688 1.0000 1.0000 1.0000 1.0000 [55,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 0.3333 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.6354 1.0000 1.0000 1.0000 1.0000 [57,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4531 0.5833 1.0000 1.0000 1.0000 [58,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [59,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4375 1.0000 1.0000 1.0000 1.0000 [60,] 1.0000 0.3750 1.0000 0.3750 0.4167 0.4375 0.6250 0.3750 1.0000 0.1250 [61,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4531 1.0000 1.0000 1.0000 1.0000 [62,] 1.0000 1.0000 1.0000 1.0000 0.3333 0.4844 0.2500 0.0000 1.0000 0.3333 [63,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 0.3333 [64,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4375 0.6250 1.0000 1.0000 1.0000 [65,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4688 1.0000 1.0000 1.0000 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.6354 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4688 1.0000 1.0000 1.0000 1.0000 [68,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [69,] 0.4375 1.0000 1.0000 1.0000 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 [70,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 1.0000 [71,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4531 1.0000 1.0000 0.3333 1.0000 [72,] 1.0000 1.0000 1.0000 1.0000 0.0000 0.4531 0.5833 0.3333 1.0000 0.3333 [73,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4688 0.5000 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4688 1.0000 1.0000 1.0000 1.0000 [75,] 1.0000 0.4167 1.0000 0.4167 0.2500 0.4062 0.6667 0.4167 1.0000 0.2500 [76,] 1.0000 0.2500 1.0000 1.0000 0.5833 0.4688 0.5000 0.2500 1.0000 0.1667 [77,] 0.4545 1.0000 1.0000 1.0000 1.0000 0.3281 1.0000 1.0000 0.4545 1.0000 [78,] 1.0000 0.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 1.0000 0.3333 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4844 1.0000 1.0000 0.0000 1.0000 [80,] 1.0000 1.0000 0.3333 0.3333 1.0000 0.6354 1.0000 1.0000 1.0000 1.0000 [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50] [1,] 0.8148 0.5694 0.4815 0.4444 0.4815 0.3833 1.0000 1.0000 0.4259 1.0000 [2,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 0.3333 [3,] 0.6667 1.0000 1.0000 1.0000 0.3333 1.0000 0.3333 1.0000 1.0000 1.0000 [4,] 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [5,] 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 1.0000 [6,] 0.3333 1.0000 1.0000 1.0000 0.3333 1.0000 0.3333 1.0000 1.0000 0.3333 [7,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [8,] 1.0000 0.5417 1.0000 0.4167 1.0000 0.3778 1.0000 1.0000 0.6944 1.0000 [9,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4000 1.0000 1.0000 1.0000 1.0000 [10,] 1.0000 0.7750 1.0000 0.7333 1.0000 0.5500 1.0000 1.0000 0.7750 1.0000 [11,] 1.0000 0.4167 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 [12,] 1.0000 0.7750 1.0000 1.0000 1.0000 1.0000 1.0000 0.4000 1.0000 1.0000 [13,] 0.4318 0.5568 0.4773 0.4318 0.4773 0.3455 0.4773 1.0000 0.4091 0.4773 [14,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.7000 1.0000 1.0000 1.0000 1.0000 [15,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [16,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [17,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [18,] 0.3333 1.0000 1.0000 1.0000 0.3333 1.0000 0.3333 1.0000 1.0000 0.3333 [19,] 1.0000 0.8036 1.0000 0.7619 1.0000 0.6357 1.0000 1.0000 1.0000 1.0000 [20,] 0.6232 0.5598 0.4783 0.4348 1.0000 0.3543 0.4783 0.4783 0.4130 0.4783 [21,] 0.6167 0.5500 0.4750 0.4250 0.4750 0.3250 0.4750 0.4750 0.4000 0.4750 [22,] 0.5833 1.0000 1.0000 1.0000 0.2500 1.0000 0.2500 1.0000 1.0000 1.0000 [23,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 [27,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [28,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [29,] 1.0000 1.0000 0.0000 1.0000 1.0000 0.4500 1.0000 1.0000 0.3750 1.0000 [30,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [32,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [35,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 0.3333 [36,] 0.4531 0.4375 0.4844 0.4531 0.4844 0.3438 0.4844 0.4844 0.4375 0.4844 [37,] 0.5833 1.0000 1.0000 1.0000 1.0000 0.7000 0.2500 1.0000 1.0000 1.0000 [38,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 0.3333 1.0000 1.0000 1.0000 0.3333 1.0000 0.3333 1.0000 1.0000 0.3333 [41,] 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 0.3333 [42,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 0.7500 1.0000 [43,] 1.0000 1.0000 0.0000 1.0000 1.0000 0.4500 1.0000 1.0000 0.3750 1.0000 [44,] 1.0000 1.0000 1.0000 0.0000 1.0000 0.3500 1.0000 1.0000 0.7083 1.0000 [45,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [46,] 1.0000 1.0000 0.4500 0.3500 1.0000 0.0000 1.0000 1.0000 0.4750 1.0000 [47,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [48,] 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [49,] 1.0000 0.7500 0.3750 0.7083 1.0000 0.4750 1.0000 1.0000 0.0000 1.0000 [50,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 [51,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4500 1.0000 1.0000 1.0000 1.0000 [52,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.7000 1.0000 1.0000 1.0000 1.0000 [53,] 1.0000 0.7500 1.0000 1.0000 1.0000 0.6500 1.0000 1.0000 0.7500 1.0000 [54,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [55,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.7833 1.0000 1.0000 1.0000 1.0000 [57,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.3500 1.0000 1.0000 1.0000 1.0000 [58,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 1.0000 0.5000 1.0000 1.0000 1.0000 0.8250 1.0000 1.0000 0.7500 1.0000 [60,] 0.4167 1.0000 1.0000 1.0000 0.3750 1.0000 0.3750 1.0000 1.0000 0.3750 [61,] 1.0000 1.0000 1.0000 0.6667 1.0000 0.5667 1.0000 1.0000 1.0000 1.0000 [62,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [63,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [64,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4750 1.0000 1.0000 1.0000 1.0000 [65,] 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 0.2500 0.6250 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.7833 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6250 1.0000 [68,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [69,] 1.0000 0.4375 1.0000 0.5417 1.0000 0.5500 1.0000 1.0000 0.4375 1.0000 [70,] 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [71,] 1.0000 1.0000 1.0000 0.3333 1.0000 0.5667 1.0000 1.0000 1.0000 1.0000 [72,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 0.3333 [73,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.4000 1.0000 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [75,] 0.5000 1.0000 1.0000 1.0000 0.4167 1.0000 0.4167 1.0000 1.0000 0.4167 [76,] 0.5833 1.0000 1.0000 1.0000 0.2500 1.0000 0.2500 1.0000 1.0000 1.0000 [77,] 1.0000 0.4886 0.4545 0.3636 1.0000 0.6182 1.0000 1.0000 0.4886 1.0000 [78,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [1,] 0.4815 0.4630 0.4259 0.4630 0.4815 1.0000 0.4444 0.4815 0.4259 0.7130 [2,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4167 [3,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 0.1250 [4,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 1.0000 [5,] 1.0000 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 1.0000 [6,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 0.1250 [7,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7083 [8,] 0.4722 0.7222 0.3889 0.7222 1.0000 0.4167 0.4167 1.0000 0.3889 1.0000 [9,] 0.2500 0.5000 0.2500 1.0000 1.0000 1.0000 0.1667 1.0000 1.0000 1.0000 [10,] 1.0000 1.0000 0.3250 1.0000 1.0000 1.0000 0.4667 1.0000 0.7750 1.0000 [11,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7083 1.0000 [12,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7750 [13,] 0.4773 0.7273 0.4091 1.0000 0.4773 1.0000 0.4318 1.0000 0.4091 0.5568 [14,] 1.0000 0.5000 1.0000 0.5000 1.0000 1.0000 1.0000 0.2500 0.6250 1.0000 [15,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [16,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 [17,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [18,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 0.1250 [19,] 0.4286 0.3571 0.8036 0.3571 1.0000 0.7619 0.7619 0.4286 0.8036 1.0000 [20,] 0.4783 0.4565 0.4130 0.4565 1.0000 0.6232 0.6232 0.4783 0.4130 0.7065 [21,] 0.4750 0.7250 0.4000 1.0000 0.4750 1.0000 0.4250 1.0000 0.5500 0.4000 [22,] 1.0000 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 0.2500 [23,] 1.0000 0.2500 1.0000 0.2500 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [24,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 0.7083 [27,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7083 [28,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [29,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [30,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [32,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [35,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4167 [36,] 0.4844 0.4688 0.4375 0.4688 0.4844 0.6354 0.4531 0.4844 0.4375 0.4375 [37,] 1.0000 1.0000 0.6250 1.0000 1.0000 1.0000 0.5833 1.0000 1.0000 0.6250 [38,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 1.0000 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 0.1250 [41,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4167 [42,] 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 [43,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [44,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [45,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [46,] 0.4500 0.7000 0.6500 1.0000 1.0000 0.7833 0.3500 1.0000 0.8250 1.0000 [47,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [48,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [49,] 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 0.7500 1.0000 [50,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [51,] 0.0000 0.2500 0.3750 1.0000 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 [52,] 0.2500 0.0000 0.6250 0.5000 1.0000 1.0000 0.5833 0.2500 1.0000 1.0000 [53,] 0.3750 0.6250 0.0000 1.0000 1.0000 1.0000 0.4167 1.0000 0.7500 1.0000 [54,] 1.0000 0.5000 1.0000 0.0000 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 [55,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [57,] 0.3333 0.5833 0.4167 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 [58,] 1.0000 0.2500 1.0000 0.2500 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [59,] 1.0000 1.0000 0.7500 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 [60,] 1.0000 1.0000 1.0000 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 0.0000 [61,] 1.0000 0.5833 1.0000 0.5833 1.0000 1.0000 0.6667 0.3333 1.0000 1.0000 [62,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [63,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [64,] 0.3750 0.6250 0.5000 1.0000 1.0000 1.0000 0.1250 1.0000 1.0000 1.0000 [65,] 1.0000 1.0000 0.6250 1.0000 1.0000 1.0000 1.0000 1.0000 0.6250 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 1.0000 0.6250 1.0000 1.0000 1.0000 1.0000 1.0000 0.2500 1.0000 [68,] 1.0000 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [69,] 1.0000 1.0000 0.8125 1.0000 1.0000 1.0000 1.0000 1.0000 0.4375 1.0000 [70,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [71,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [72,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4167 [73,] 0.2500 0.5000 0.2500 1.0000 1.0000 1.0000 0.1667 1.0000 1.0000 1.0000 [74,] 1.0000 0.5000 1.0000 0.5000 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 [75,] 1.0000 1.0000 1.0000 1.0000 0.4167 1.0000 1.0000 1.0000 1.0000 0.1667 [76,] 1.0000 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 0.2500 [77,] 1.0000 1.0000 0.6591 1.0000 1.0000 1.0000 1.0000 1.0000 0.6591 1.0000 [78,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.3750 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7083 [,61] [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [1,] 0.4444 1.0000 0.4815 0.5694 0.7315 1.0000 0.4630 0.4815 0.3519 1.0000 [2,] 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [3,] 1.0000 0.3333 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [4,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.2500 1.0000 0.4375 1.0000 [5,] 1.0000 1.0000 1.0000 1.0000 0.2500 1.0000 0.2500 1.0000 0.4375 1.0000 [6,] 1.0000 0.3333 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [7,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [8,] 0.6111 1.0000 1.0000 0.5417 0.7222 0.4167 0.4444 0.4722 0.3681 1.0000 [9,] 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [10,] 0.4667 1.0000 1.0000 0.5500 0.6500 1.0000 0.6500 0.4000 0.8375 1.0000 [11,] 1.0000 1.0000 1.0000 1.0000 0.5833 1.0000 0.5833 1.0000 0.7708 0.3333 [12,] 1.0000 1.0000 1.0000 1.0000 0.6500 1.0000 1.0000 1.0000 1.0000 0.4000 [13,] 0.6212 0.4773 0.4773 0.4091 0.7273 1.0000 0.4545 0.4773 0.3182 1.0000 [14,] 0.5833 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6875 1.0000 [15,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [16,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [17,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [18,] 1.0000 0.3333 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [19,] 0.5238 1.0000 1.0000 0.8036 1.0000 0.7619 0.6786 1.0000 0.8661 1.0000 [20,] 0.6232 0.4783 1.0000 0.5598 0.4565 0.6232 0.4565 0.4783 0.4103 0.4783 [21,] 0.6167 0.4750 0.4750 0.5500 0.4500 1.0000 0.4500 0.4750 0.4750 0.4750 [22,] 1.0000 0.2500 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [23,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [25,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [27,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [28,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [29,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [30,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4375 1.0000 [32,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [34,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [35,] 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [36,] 0.4531 0.4844 0.4844 0.4375 0.4688 0.6354 0.4688 0.4844 0.3750 0.4844 [37,] 1.0000 0.2500 1.0000 0.6250 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [38,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [39,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [40,] 1.0000 0.3333 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [41,] 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [42,] 1.0000 1.0000 1.0000 1.0000 0.2500 1.0000 0.2500 1.0000 0.4375 0.3750 [43,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [44,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.5417 1.0000 [45,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [46,] 0.5667 1.0000 1.0000 0.4750 1.0000 0.7833 1.0000 1.0000 0.5500 1.0000 [47,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [48,] 1.0000 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 0.0000 [49,] 1.0000 1.0000 1.0000 1.0000 0.6250 1.0000 0.6250 1.0000 0.4375 1.0000 [50,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [51,] 1.0000 1.0000 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [52,] 0.5833 1.0000 1.0000 0.6250 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [53,] 1.0000 1.0000 1.0000 0.5000 0.6250 1.0000 0.6250 0.3750 0.8125 1.0000 [54,] 0.5833 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [55,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [57,] 0.6667 1.0000 1.0000 0.1250 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [58,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 1.0000 1.0000 1.0000 1.0000 0.6250 1.0000 0.2500 1.0000 0.4375 1.0000 [60,] 1.0000 0.3750 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [61,] 0.0000 1.0000 1.0000 0.7083 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [62,] 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [63,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [64,] 0.7083 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [65,] 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 0.5000 1.0000 0.6875 0.2500 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 0.0000 1.0000 0.3750 1.0000 [68,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 [69,] 1.0000 1.0000 1.0000 1.0000 0.6875 1.0000 0.3750 1.0000 0.0000 1.0000 [70,] 1.0000 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 0.0000 [71,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7708 1.0000 [72,] 1.0000 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [73,] 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [74,] 0.5833 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [75,] 1.0000 0.4167 0.4167 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [76,] 1.0000 0.2500 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [77,] 0.7879 1.0000 1.0000 1.0000 0.7045 1.0000 0.4091 0.4545 0.3523 1.0000 [78,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [79,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [80,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [,71] [,72] [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [1,] 0.4444 0.8148 0.4630 0.4630 0.5926 0.7315 0.2963 0.4815 0.4815 0.4444 [2,] 1.0000 0.0000 1.0000 1.0000 0.2500 0.5833 1.0000 1.0000 1.0000 1.0000 [3,] 1.0000 0.6667 1.0000 1.0000 0.2500 0.1667 1.0000 0.3333 1.0000 0.6667 [4,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 1.0000 1.0000 [5,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 1.0000 1.0000 [6,] 1.0000 0.3333 1.0000 1.0000 0.2500 0.1667 1.0000 0.3333 1.0000 1.0000 [7,] 1.0000 0.6667 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 0.3333 [8,] 0.6111 1.0000 0.4444 0.7222 1.0000 1.0000 0.4141 1.0000 1.0000 1.0000 [9,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [10,] 0.7333 1.0000 0.6500 1.0000 1.0000 1.0000 0.5636 1.0000 1.0000 1.0000 [11,] 0.6667 1.0000 1.0000 1.0000 1.0000 1.0000 0.5758 1.0000 0.3333 1.0000 [12,] 0.7333 1.0000 1.0000 1.0000 0.6333 1.0000 0.8545 1.0000 0.4000 0.4667 [13,] 0.4318 0.6212 0.4545 1.0000 0.6818 0.4545 0.2500 0.4773 0.4773 1.0000 [14,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [15,] 1.0000 0.3333 1.0000 1.0000 0.4167 0.2500 1.0000 1.0000 1.0000 1.0000 [16,] 1.0000 0.7083 1.0000 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 0.1250 [17,] 1.0000 1.0000 1.0000 1.0000 0.4167 1.0000 1.0000 1.0000 1.0000 0.3333 [18,] 1.0000 0.3333 1.0000 1.0000 0.2500 0.1667 1.0000 0.3333 1.0000 1.0000 [19,] 0.7619 1.0000 0.6786 0.3571 1.0000 1.0000 0.7662 1.0000 1.0000 1.0000 [20,] 0.4348 0.6232 0.4565 0.4565 0.7899 0.7283 0.3953 1.0000 0.4783 1.0000 [21,] 0.6167 0.4250 0.4500 1.0000 0.3500 0.4500 0.5068 0.4750 1.0000 0.6167 [22,] 1.0000 0.5833 1.0000 1.0000 0.3333 0.0000 1.0000 0.2500 1.0000 1.0000 [23,] 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [24,] 1.0000 0.3333 1.0000 1.0000 0.4167 1.0000 1.0000 1.0000 1.0000 1.0000 [25,] 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [26,] 1.0000 0.6667 1.0000 1.0000 0.2500 0.5833 1.0000 0.3333 1.0000 0.6667 [27,] 1.0000 0.6667 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 0.3333 [28,] 1.0000 1.0000 1.0000 1.0000 0.4167 0.2500 1.0000 0.0000 1.0000 1.0000 [29,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 1.0000 1.0000 [30,] 1.0000 0.3333 1.0000 1.0000 0.4167 1.0000 1.0000 1.0000 1.0000 1.0000 [31,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 1.0000 1.0000 [32,] 1.0000 1.0000 1.0000 1.0000 0.4167 0.2500 1.0000 0.0000 1.0000 1.0000 [33,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3333 [34,] 1.0000 1.0000 1.0000 1.0000 0.4167 1.0000 1.0000 1.0000 1.0000 0.3333 [35,] 1.0000 0.0000 1.0000 1.0000 0.2500 0.5833 1.0000 1.0000 1.0000 1.0000 [36,] 0.4531 0.4531 0.4688 0.4688 0.4062 0.4688 0.3281 0.4844 0.4844 0.6354 [37,] 1.0000 0.5833 0.5000 1.0000 0.6667 0.5000 1.0000 1.0000 1.0000 1.0000 [38,] 1.0000 0.3333 1.0000 1.0000 0.4167 0.2500 1.0000 1.0000 1.0000 1.0000 [39,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 0.0000 1.0000 [40,] 1.0000 0.3333 1.0000 1.0000 0.2500 0.1667 1.0000 0.3333 1.0000 1.0000 [41,] 1.0000 0.3333 1.0000 1.0000 0.5000 0.5833 1.0000 1.0000 1.0000 1.0000 [42,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4886 1.0000 1.0000 1.0000 [43,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 1.0000 1.0000 [44,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.3636 1.0000 1.0000 1.0000 [45,] 1.0000 1.0000 1.0000 1.0000 0.4167 0.2500 1.0000 0.0000 1.0000 1.0000 [46,] 0.5667 1.0000 0.4000 1.0000 1.0000 1.0000 0.6182 1.0000 1.0000 1.0000 [47,] 1.0000 0.3333 1.0000 1.0000 0.4167 0.2500 1.0000 1.0000 1.0000 1.0000 [48,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [49,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4886 1.0000 1.0000 1.0000 [50,] 1.0000 0.3333 1.0000 1.0000 0.4167 1.0000 1.0000 1.0000 1.0000 1.0000 [51,] 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [52,] 1.0000 1.0000 0.5000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [53,] 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 0.6591 1.0000 1.0000 1.0000 [54,] 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [55,] 1.0000 1.0000 1.0000 1.0000 0.4167 0.2500 1.0000 0.0000 1.0000 1.0000 [56,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [57,] 1.0000 1.0000 0.1667 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [58,] 1.0000 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [59,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6591 1.0000 1.0000 1.0000 [60,] 1.0000 0.4167 1.0000 1.0000 0.1667 0.2500 1.0000 0.3750 1.0000 0.7083 [61,] 0.6667 1.0000 1.0000 0.5833 1.0000 1.0000 0.7879 1.0000 1.0000 1.0000 [62,] 1.0000 0.3333 1.0000 1.0000 0.4167 0.2500 1.0000 1.0000 1.0000 1.0000 [63,] 1.0000 1.0000 1.0000 1.0000 0.4167 0.2500 1.0000 0.0000 1.0000 1.0000 [64,] 1.0000 1.0000 0.2500 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [65,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7045 1.0000 1.0000 1.0000 [66,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [67,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4091 1.0000 1.0000 1.0000 [68,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 1.0000 1.0000 [69,] 0.7708 1.0000 1.0000 1.0000 1.0000 1.0000 0.3523 1.0000 1.0000 1.0000 [70,] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [71,] 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3636 1.0000 0.3333 1.0000 [72,] 1.0000 0.0000 1.0000 1.0000 0.2500 0.5833 1.0000 1.0000 1.0000 1.0000 [73,] 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [74,] 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [75,] 1.0000 0.2500 1.0000 1.0000 0.0000 0.3333 1.0000 0.4167 1.0000 0.5000 [76,] 1.0000 0.5833 1.0000 1.0000 0.3333 0.0000 1.0000 0.2500 1.0000 1.0000 [77,] 0.3636 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 0.4545 1.0000 [78,] 1.0000 1.0000 1.0000 1.0000 0.4167 0.2500 1.0000 0.0000 1.0000 1.0000 [79,] 0.3333 1.0000 1.0000 1.0000 1.0000 1.0000 0.4545 1.0000 0.0000 1.0000 [80,] 1.0000 1.0000 1.0000 1.0000 0.5000 1.0000 1.0000 1.0000 1.0000 0.0000 > > > > cleanEx() > nameEx("randpop.nb") > ### * randpop.nb > > flush(stderr()); flush(stdout()) > > ### Name: randpop.nb > ### Title: Simulation of presence-absence matrices (non-clustered) > ### Aliases: randpop.nb > ### Keywords: spatial > > ### ** Examples > > data(nb) > set.seed(2346) > randpop.nb(nb, p.nb=0.1, n.species=5, vector.species=c(1,10,20,30,34)) Species 1 Species 2 Species 3 Species 4 Species 5 [,1] [,2] [,3] [,4] [,5] [1,] 0 1 0 1 1 [2,] 0 1 0 1 1 [3,] 1 1 1 1 1 [4,] 0 1 0 1 1 [5,] 1 0 0 0 1 [6,] 1 0 0 0 1 [7,] 1 0 0 0 1 [8,] 0 1 0 1 1 [9,] 1 0 0 0 1 [10,] 1 0 0 0 1 [11,] 1 0 0 0 1 [12,] 1 0 0 0 1 [13,] 1 0 0 0 1 [14,] 0 1 0 0 1 [15,] 0 1 1 0 1 [16,] 0 1 0 1 1 [17,] 0 1 0 1 1 [18,] 0 1 1 0 1 [19,] 0 1 1 0 1 [20,] 0 1 1 0 1 [21,] 0 1 0 1 1 [22,] 0 1 0 1 1 [23,] 0 1 0 1 1 [24,] 0 0 0 0 1 [25,] 0 0 0 0 1 [26,] 0 0 0 0 1 [27,] 0 0 0 0 1 [28,] 0 1 0 0 1 [29,] 0 1 0 0 1 [30,] 1 1 1 0 1 [31,] 0 0 1 0 1 [32,] 0 1 1 0 1 [33,] 0 1 1 0 1 [34,] 0 0 1 0 1 > > > > cleanEx() > nameEx("regdist") > ### * regdist > > flush(stderr()); flush(stdout()) > > ### Name: regdist > ### Title: Regression between subsets of dissimilarity matrices > ### Aliases: regdist > ### Keywords: regression spatial > > ### ** 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) (Intercept) 0.1701 > > > > cleanEx() > nameEx("regdistbetween") > ### * regdistbetween > > flush(stderr()); flush(stdout()) > > ### Name: regdistbetween > ### Title: Testing equality of within-groups and between-groups distances > ### regression > ### Aliases: regdistbetween print.regdistbetween > ### Keywords: htest regression spatial > > ### ** 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$distmat,grouping=species,groups=c(1,2)) > print(rtest2) Testing whether regression for between-group distances is compatible with regressions for within-group distances Groups: 1 and 2 Approx. p-value: 1.256e-17 Difference between coefficients at between groups center (plain): 0.182 Difference between coefficients at between groups center (jackknife): 0.1826 Standard error of difference (jackknife): 0.01147 Welch-Satterthwaite degrees of freedom: 34 > > > > cleanEx() > nameEx("regdistbetweenone") > ### * regdistbetweenone > > flush(stderr()); flush(stdout()) > > ### Name: regdistbetweenone > ### Title: Testing equality of one within-group and between-two groups > ### distances regression > ### Aliases: regdistbetweenone > ### Keywords: htest regression spatial > > ### ** Examples > > options(digits=4) > data(veronica) > ver.geo <- coord2dist(coordmatrix=veronica.coord[173:207,],file.format="decimal2") > vei <- prabinit(prabmatrix=veronica[173:207,],distance="jaccard") > > species <-c(rep(1,13),rep(2,22)) > loggeo <- log(ver.geo+quantile(as.vector(as.dist(ver.geo)),0.25)) > rtest3 <- + regdistbetweenone(dmx=loggeo,dmy=vei$distmat,grouping=species,groups=c(1,2),rgroup=1) > print(rtest3) Testing whether regression for between-group distances is compatible with regressions for within-group distances for species 1 The maximum Bonferroni p-value over both species can be used to test the H0 that the between-group distances are compatible with within-group distances of at least one group. Groups: 1 and 2 Approx. p-value: 3.73e-05 Difference between coefficients at between groups center (plain): 0.2106 Difference between coefficients at between groups center (jackknife): 0.2089 Standard error of difference (jackknife): 0.0383 Welch-Satterthwaite degrees of freedom: 14.51 > > > > cleanEx() > nameEx("regdistdiff") > ### * regdistdiff > > flush(stderr()); flush(stdout()) > > ### Name: regdistdiff > ### Title: Regression difference between within-group dissimilarities > ### Aliases: regdistdiff > ### Keywords: regression spatial > > ### ** 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$distmat,grouping=species,xcenter=0,xcenterbetween=100) (Intercept) 0.03348 > > > > > cleanEx() > nameEx("regdistdiffone") > ### * regdistdiffone > > flush(stderr()); flush(stdout()) > > ### Name: regdistdiffone > ### Title: Regression difference within reference group and between-group > ### dissimilarities > ### Aliases: regdistdiffone > ### Keywords: regression spatial > > ### ** Examples > > options(digits=4) > data(veronica) > ver.geo <- coord2dist(coordmatrix=veronica.coord[173:207,], + file.format="decimal2") > vei <- prabinit(prabmatrix=veronica[173:207,],distance="jaccard") > > species <-c(rep(1,13),rep(2,22)) > regdistdiffone(rep(TRUE,35),ver.geo,vei$distmat,grouping=species, + xcenter=0,xcenterbetween=100,rgroup=2) (Intercept) 0.03813 > > > > > cleanEx() > nameEx("regeqdist") > ### * regeqdist > > flush(stderr()); flush(stdout()) > > ### Name: regeqdist > ### Title: Testing equality of two distance-regressions > ### Aliases: regeqdist print.regeqdist > ### Keywords: htest regression spatial > > ### ** 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) Testing equality for distance-based regressions in two groups 1 and 2 Approx. p-values (intercept, slope): 0.003998 0.0605 Approx. Bonferroni p-value: 0.007995 Difference between coefficients, plain (intercept,slope): -0.06991 0.08496 Difference between coefficients, jackknife (intercept,slope): -0.07094 0.08914 Standard error of difference, jackknife (intercept, slope): 0.02285 0.0446 Welch-Satterthwaite degrees of freedom: 31.55 18.59 > > > > cleanEx() > nameEx("regpop.sar") > ### * regpop.sar > > flush(stderr()); flush(stdout()) > > ### Name: regpop.sar > ### Title: Simulation of abundance matrices (non-clustered) > ### Aliases: regpop.sar > ### Keywords: spatial > > ### ** 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)) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [1,] 4.424 0.000 0.8045 0.000 0.000 0.7319 0.000 0.000 0.000 0.00 0.0 [2,] 0.000 0.000 0.0000 0.000 8.046 0.8970 0.000 0.000 0.000 0.00 76.9 [3,] 0.000 0.000 0.0000 0.000 1.917 0.0000 0.000 0.000 5.008 0.00 210.3 [4,] 0.000 7.642 0.0000 7.431 0.000 0.0000 1.226 2.502 4.128 11.49 0.0 [5,] 0.000 0.000 0.0000 0.000 0.000 0.0000 4.246 133.432 0.000 132.26 0.0 [6,] 0.000 0.000 0.0000 0.000 0.000 0.0000 0.000 0.000 0.000 0.00 0.0 [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [1,] 16.114 1.068 0.0000 2.4622 404.77 0.00 0.000 14.294 0.000 0.0000 [2,] 3.669 10.747 0.0000 1.2480 18.38 0.00 0.000 34.194 18.530 0.5066 [3,] 11.510 10.088 0.8249 0.0000 629.79 111.86 2.409 1.659 27.031 0.4347 [4,] 0.000 0.000 1.0274 0.7724 24.22 74.13 3.245 0.000 27.119 9.7972 [5,] 0.000 0.000 0.7984 0.0000 0.00 189.14 10.626 0.000 7.639 13.3716 [6,] 0.000 0.000 0.0000 0.0000 0.00 117.30 75.762 0.000 0.000 0.0000 [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30] [,31] [1,] 0.0000 3.743 117.847 0.00 17.746 0.00 46.90 180.60 0.000 47.30 [2,] 0.0000 9.335 8.234 2049.43 5.588 112.80 297.63 12.07 110.090 21.14 [3,] 9.3162 4.942 84.625 411.04 328.606 36.18 22.85 49.93 24.489 17.95 [4,] 1.3383 11.273 191.337 204.17 414.441 44.73 95.56 30.25 18.483 458.78 [5,] 0.4080 0.000 73.913 62.83 454.085 261.21 414.47 491.25 2.666 14.00 [6,] 0.9697 0.000 0.000 227.12 0.000 76.67 0.00 0.00 181.894 0.00 [,32] [,33] [,34] [,35] [,36] [,37] [,38] [,39] [,40] [,41] [1,] 0.00 97.88 222.36 0.00 0.00 0.0000 0.000 0.000 0.000 0.0000 [2,] 10.62 17.21 53.77 233.64 1656.27 2.0648 0.000 1.461 8.303 0.0000 [3,] 152.77 17.70 22.70 108.82 178.64 0.2479 24.992 71.287 205.565 0.0000 [4,] 39.92 25.13 184.99 521.93 39.45 12.2536 4.424 79.126 121.705 1.5343 [5,] 21.91 77.67 61.62 96.63 107.93 16.1391 35.054 62.037 4.294 2.0139 [6,] 27.84 0.00 0.00 386.98 69.13 39.5206 9.292 205.161 19.097 0.7267 [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50] [,51] [1,] 42.553 27.020 39.19 173.65 100.58 0.9178 690.79 174.87 9.388 0.000 [2,] 49.356 10.292 47.30 12.35 97.86 56.5983 17.88 99.52 1.778 5.443 [3,] 25.261 20.218 90.16 98.51 29.28 114.4679 73.83 115.62 1155.097 84.260 [4,] 45.695 96.290 13.40 287.37 19.61 2.2788 100.57 35.04 55.117 72.366 [5,] 8.033 1.743 16.41 73.70 335.14 17.1360 14.84 223.43 1332.808 24.677 [6,] 61.728 0.000 77.37 30.34 41.83 9.8310 183.61 215.06 6.160 100.328 [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62] [1,] 0.09669 8.171 37.41 0.000 0.72 0.0000 0.7817 0.000 0.00 0.000 0.000 [2,] 1.18816 79.494 23.09 0.000 0.00 0.0000 0.0000 0.000 0.00 0.000 0.000 [3,] 26.49855 68.599 38.35 0.000 0.00 0.7298 0.0000 1.813 0.00 0.000 0.000 [4,] 14.82499 114.494 20.41 0.000 0.00 0.0000 0.0000 0.000 0.00 5.108 8.019 [5,] 21.91348 69.903 96.20 3.581 0.00 0.0000 0.0000 0.000 88.43 2.593 43.366 [6,] 19.85901 93.736 44.97 0.000 0.00 0.0000 0.0000 0.000 21.42 0.000 0.000 [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [1,] 0.0000 1.6928 0.0000 0.00000 0.00 0.000 1.010 0.000 1308.6496 0.00 [2,] 0.1577 3.7789 0.0000 0.05428 15.91 3.422 2.169 43.231 0.7525 0.00 [3,] 3.3496 0.5399 0.0000 0.05835 103.76 481.297 6.162 30.241 13.9907 0.00 [4,] 0.0000 0.0000 0.1205 2.36815 118.47 118.624 15.613 1.420 111.5256 0.00 [5,] 0.0000 0.0000 0.5610 0.00000 11.98 33.775 120.410 9.955 358.6050 7.33 [6,] 0.0000 0.0000 1.2115 0.00000 0.00 0.000 0.000 21.423 0.0000 0.00 [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [1,] 0.00 9.319 0.000 0.0000 21.23 0.000 0.0000 0.000 0.0000 0.0000 0.0000 [2,] 0.00 0.000 4.296 0.0000 0.00 0.000 0.0000 0.000 0.0000 0.1549 0.0000 [3,] 0.00 0.000 0.000 0.0000 0.00 0.000 2.2060 0.000 1.7816 0.7675 0.9948 [4,] 0.00 0.000 0.000 0.0000 0.00 0.000 0.9584 0.000 0.5883 0.0000 64.6948 [5,] 36.35 0.000 0.000 0.0000 0.00 0.000 0.0000 4.073 0.0000 0.0000 0.0000 [6,] 0.00 0.000 0.000 0.2721 0.00 1.389 0.0000 2.233 0.0000 0.0000 0.0000 [,84] [,85] [,86] [,87] [,88] [,89] [,90] [,91] [,92] [,93] [1,] 0.000 0.0000 4.115 0.000 0.00 5.710 42.39 0.0000 0.000 0.0000 [2,] 0.000 0.0000 119.898 0.000 12.88 8.801 31.65 0.9771 0.000 0.0000 [3,] 0.000 0.0000 62.177 144.154 764.64 16.274 0.00 1.0832 1.128 2.1739 [4,] 0.000 0.7238 0.000 39.057 22.10 0.000 47.00 3.4273 2.121 12.0662 [5,] 5.593 0.2873 0.000 4.722 19.18 1.445 0.00 0.0000 20.916 0.4322 [6,] 5.749 0.0000 0.000 0.000 0.00 0.000 0.00 6.3784 6.227 4.5634 [,94] [,95] [,96] [,97] [,98] [,99] [,100] [,101] [,102] [,103] [,104] [1,] 2.13848 0.00 0.0000 0.000 0.00 0.000 0.000 0.000 0.00 0.00 20.839 [2,] 0.56436 19.82 0.0000 0.000 0.00 0.000 0.000 0.000 0.00 0.00 1.886 [3,] 0.96561 0.00 0.9187 1.009 0.00 2.635 1.225 2.627 0.00 0.00 4.058 [4,] 0.00000 0.00 0.0000 0.000 0.00 0.000 1.248 15.379 57.85 19.40 0.000 [5,] 0.00000 0.00 0.0000 0.000 10.57 0.000 0.000 0.000 19.40 28.51 0.000 [6,] 0.07315 0.00 0.0000 0.000 0.00 0.000 0.000 0.000 0.00 0.00 0.000 [,105] [,106] [,107] [,108] [,109] [,110] [,111] [,112] [,113] [,114] [1,] 95.213 0.0000 0.000 0.0 0.00 2.478 0.0 1.427 0.00 0.000 [2,] 16.094 2.3227 0.000 0.0 0.00 4.015 134.4 2.172 0.00 1.361 [3,] 8.076 0.3911 0.000 0.0 0.00 0.000 199.4 10.125 14.07 1.361 [4,] 0.000 1.9842 4.952 179.6 22.81 0.000 482.7 0.000 89.43 0.000 [5,] 0.000 0.0000 25.439 270.5 106.16 2.054 0.0 0.000 75.67 1.501 [6,] 0.000 0.0000 5.020 274.2 38.40 0.000 0.0 0.000 0.00 0.000 [,115] [,116] [,117] [,118] [,119] [,120] [,121] [,122] [,123] [,124] [1,] 0.000 0.000 4.532 0.000 0.00 0.00 0.000 0.0000 0.000 0.000 [2,] 0.000 5.105 5.263 0.000 0.00 0.00 0.000 0.0000 0.000 5.520 [3,] 0.000 38.983 10.434 0.000 0.00 0.00 0.000 0.0000 0.000 5.381 [4,] 7.147 11.642 0.000 4.931 12.12 0.00 0.000 0.4516 0.000 0.000 [5,] 5.823 0.000 0.000 68.353 0.00 0.00 0.000 0.0000 0.000 0.000 [6,] 10.483 0.000 0.000 17.950 0.00 14.78 4.115 0.0000 0.834 0.000 [,125] [,126] [,127] [,128] [,129] [,130] [,131] [,132] [,133] [,134] [1,] 0.000 6.528 1.093 0.000 1.618 0.0000 0.000 0.0000 0.000 0.000 [2,] 0.000 1.259 0.000 0.000 1.670 0.0000 0.000 0.0000 0.000 0.000 [3,] 0.000 0.000 0.000 0.000 0.000 0.0000 0.000 0.0000 7.442 8.463 [4,] 0.000 0.000 0.000 0.000 0.000 0.0000 0.000 0.0000 11.517 9.638 [5,] 17.848 0.000 10.653 2.863 0.000 0.2388 2.056 0.9576 0.000 0.000 [6,] 5.016 0.000 0.000 1.827 0.000 1.5242 1.875 1.7932 0.000 0.000 [,135] [,136] [,137] [,138] [,139] [,140] [,141] [,142] [,143] [,144] [1,] 0.00 0.00 6.7066 0.000 0.000 0.00 0.00 0.0 0.000 0.0000 [2,] 0.00 0.00 0.4899 0.000 0.000 0.00 0.00 0.0 3.212 0.0000 [3,] 0.00 0.00 0.0000 0.000 0.000 0.00 0.00 0.0 0.000 0.0000 [4,] 0.00 23.28 0.0000 1.909 1.235 0.00 42.65 0.0 0.000 0.4584 [5,] 36.27 559.44 0.0000 0.000 0.000 1.46 0.00 23.2 0.000 0.0000 [6,] 32.01 0.00 0.0000 0.000 0.000 0.00 0.00 0.0 0.000 0.0000 > > > > cleanEx() > nameEx("siskiyou") > ### * siskiyou > > flush(stderr()); flush(stdout()) > > ### Name: siskiyou > ### Title: Herbs of the Siskiyou Mountains > ### Aliases: siskiyou siskiyou.nb siskiyou.groups > ### Keywords: datasets > > ### ** Examples > > data(siskiyou) > > > > cleanEx() > nameEx("specgroups") > ### * specgroups > > flush(stderr()); flush(stdout()) > > ### Name: specgroups > ### Title: Average within-group distances for given groups > ### Aliases: specgroups > ### Keywords: cluster > > ### ** 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) $overall [1] 0.5864 $gr [1] NaN NaN NaN NaN NaN NaN 0.7897 NaN NaN NaN [11] NaN NaN NaN NaN 0.2778 NaN NaN NaN 1.0000 NaN [21] 0.4815 NaN 0.3530 NaN 0.2257 NaN 0.7591 NaN NaN NaN [31] NaN 0.5354 NaN NaN NaN NaN NaN NaN NaN 0.7529 [41] NaN NaN NaN NaN NaN NaN 0.4842 NaN NaN NaN [51] 0.9123 NaN NaN NaN NaN NaN NaN 0.8442 NaN NaN [61] NaN NaN 0.2595 NaN NaN NaN NaN NaN NaN 0.7163 [71] NaN NaN NaN NaN 0.4578 NaN 0.4293 0.4432 NaN NaN [81] 0.5259 NaN NaN NaN NaN NaN 0.4325 NaN NaN NaN [91] NaN NaN NaN NaN 0.6856 0.2832 NaN NaN NaN NaN [101] NaN NaN NaN NaN NaN NaN NaN 0.5829 NaN > > > > cleanEx() > nameEx("stressvals") > ### * stressvals > > flush(stderr()); flush(stdout()) > > ### Name: stressvals > ### Title: Stress values for different dimensions of Kruskal's MDS > ### Aliases: stressvals > ### Keywords: multivariate > > ### ** Examples > > options(digits=4) > data(tetragonula) > set.seed(112233) > taiselect <- sample(236,40) > # Use data subset to make execution faster. > tnb <- + coord2dist(coordmatrix=tetragonula.coord[taiselect,], + cut=50,file.format="decimal2",neighbors=TRUE) > ta <- alleleconvert(strmatrix=tetragonula[taiselect,]) > tai <- alleleinit(allelematrix=ta,neighborhood=tnb$nblist) > stressvals(tai,mdsdim=1:3)$MDSstress [1] 32.72 10.16 5.40 > > > > cleanEx() > nameEx("tetragonula") > ### * tetragonula > > flush(stderr()); flush(stdout()) > > ### Name: tetragonula > ### Title: Microsatellite genetic data of Tetragonula bees > ### Aliases: tetragonula tetragonula.coord > ### Keywords: datasets > > ### ** Examples > > data(tetragonula) > > > > cleanEx() > nameEx("toprab") > ### * toprab > > flush(stderr()); flush(stdout()) > > ### Name: toprab > ### Title: Convert abundance matrix into presence/absence matrix > ### Aliases: toprab > ### Keywords: manip > > ### ** Examples > > data(siskiyou) > x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb, + distance="none") > toprab(x) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] V1 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V2 FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE V4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V5 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] V1 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V2 TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE V3 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V4 FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V5 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE V6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] V1 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V2 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V3 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V4 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V5 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] V1 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V2 TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V3 TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V4 TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V5 TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE V6 FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [,49] [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] V1 TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE V2 TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V3 TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE V4 TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE V5 TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE V6 TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [,61] [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] V1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V2 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE V3 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V4 FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE V5 FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE V6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] V1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V3 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V4 FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE V5 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [,85] [,86] [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] V1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V3 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE V4 FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V5 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE V6 FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE [,97] [,98] [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] V1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V4 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V5 FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE [,108] [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [,118] V1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V4 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V5 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V6 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127] [,128] [,129] V1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V5 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE V6 FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE [,130] [,131] [,132] [,133] [,134] [,135] [,136] [,137] [,138] [,139] [,140] V1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE V5 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE V6 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [,141] [,142] [,143] [,144] V1 FALSE FALSE FALSE FALSE V2 FALSE FALSE FALSE FALSE V3 FALSE FALSE FALSE FALSE V4 FALSE FALSE FALSE FALSE V5 FALSE FALSE FALSE FALSE V6 TRUE TRUE TRUE TRUE > > > > cleanEx() > nameEx("unbuild.charmatrix") > ### * unbuild.charmatrix > > flush(stderr()); flush(stdout()) > > ### Name: unbuild.charmatrix > ### Title: Internal: create allele list out of character matrix > ### Aliases: unbuild.charmatrix > ### Keywords: manip > > ### ** 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)) List of 13 $ :List of 50 ..$ : chr [1:2] "F" "G" ..$ : chr [1:2] "C" "G" ..$ : chr [1:2] "F" "I" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "D" "F" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "C" "H" ..$ : chr [1:2] "F" "G" ..$ : chr [1:2] "C" "G" ..$ : chr [1:2] "F" "F" ..$ : chr [1:2] "F" "I" ..$ : chr [1:2] "C" "G" ..$ : chr [1:2] "F" "G" ..$ : chr [1:2] "F" "G" ..$ : chr [1:2] "F" "G" ..$ : chr [1:2] "F" "G" ..$ : chr [1:2] "E" "H" ..$ : chr [1:2] "E" "G" ..$ : chr [1:2] "F" "G" ..$ : chr [1:2] "C" "G" ..$ : chr [1:2] "C" "G" ..$ : chr [1:2] "C" "G" ..$ : chr [1:2] "C" "G" ..$ : chr [1:2] "C" "G" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "C" "F" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "F" "G" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "C" "G" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "C" "G" ..$ : chr [1:2] "F" "G" ..$ : chr [1:2] "C" "F" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "A" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "A" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "A" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "A" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" $ :List of 50 ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA $ :List of 50 ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "G" "K" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "H" "H" ..$ : chr [1:2] "H" "H" ..$ : chr [1:2] "J" "K" ..$ : chr [1:2] "G" "H" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "H" "H" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "I" "H" ..$ : chr [1:2] "G" "J" ..$ : chr [1:2] "G" "J" ..$ : chr [1:2] "J" "K" ..$ : chr [1:2] "G" "J" ..$ : chr [1:2] "G" "H" ..$ : chr [1:2] "G" "J" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "G" "H" ..$ : chr [1:2] "G" "H" ..$ : chr [1:2] "G" "H" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "G" "K" ..$ : chr [1:2] "H" "J" ..$ : chr [1:2] "G" "J" ..$ : chr [1:2] "G" "H" ..$ : chr [1:2] "G" "K" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "H" "I" ..$ : chr [1:2] "E" "E" ..$ : chr [1:2] "A" "B" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "E" "E" ..$ : chr [1:2] "E" "E" ..$ : chr [1:2] "E" "D" ..$ : chr [1:2] "E" "E" ..$ : chr [1:2] "B" "F" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "E" "F" ..$ : chr [1:2] "B" "C" $ :List of 50 ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "B" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "B" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "B" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "B" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "B" "A" ..$ : chr [1:2] "B" "A" ..$ : chr [1:2] "A" "A" $ :List of 50 ..$ : chr [1:2] "A" "D" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "C" "B" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "A" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "A" "C" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "A" "C" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "A" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "A" "D" ..$ : chr [1:2] "D" "E" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "C" "A" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "E" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA $ :List of 50 ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "D" "C" ..$ : chr [1:2] "D" "C" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "C" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "D" "C" ..$ : chr [1:2] "D" "C" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "C" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "E" "D" ..$ : chr [1:2] "A" "D" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "C" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "D" $ :List of 50 ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "F" "E" ..$ : chr [1:2] "D" "C" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "C" ..$ : chr [1:2] "G" "B" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "C" ..$ : chr [1:2] "D" "C" ..$ : chr [1:2] "D" "C" ..$ : chr [1:2] "D" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "D" "D" $ :List of 50 ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "E" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "A" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "A" "C" ..$ : chr [1:2] "C" "C" $ :List of 50 ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : logi NA ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "C" "E" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "C" "A" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "D" "D" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "D" ..$ : chr [1:2] "B" "E" $ :List of 50 ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "C" "B" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "C" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" $ :List of 50 ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "A" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" $ :List of 50 ..$ : chr [1:2] "E" "L" ..$ : chr [1:2] "G" "I" ..$ : chr [1:2] "G" "H" ..$ : chr [1:2] "I" "M" ..$ : chr [1:2] "I" "I" ..$ : chr [1:2] "E" "I" ..$ : chr [1:2] "G" "I" ..$ : chr [1:2] "F" "G" ..$ : chr [1:2] "G" "G" ..$ : chr [1:2] "H" "I" ..$ : chr [1:2] "E" "I" ..$ : chr [1:2] "H" "H" ..$ : chr [1:2] "E" "G" ..$ : chr [1:2] "E" "L" ..$ : chr [1:2] "E" "L" ..$ : chr [1:2] "G" "K" ..$ : chr [1:2] "I" "I" ..$ : chr [1:2] "I" "M" ..$ : chr [1:2] "E" "H" ..$ : chr [1:2] "I" "I" ..$ : chr [1:2] "G" "H" ..$ : chr [1:2] "F" "H" ..$ : chr [1:2] "E" "I" ..$ : chr [1:2] "G" "J" ..$ : chr [1:2] "H" "I" ..$ : chr [1:2] "E" "G" ..$ : chr [1:2] "E" "F" ..$ : chr [1:2] "H" "H" ..$ : chr [1:2] "I" "H" ..$ : chr [1:2] "E" "F" ..$ : chr [1:2] "H" "J" ..$ : chr [1:2] "F" "G" ..$ : chr [1:2] "E" "J" ..$ : chr [1:2] "G" "K" ..$ : chr [1:2] "I" "I" ..$ : chr [1:2] "A" "H" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "B" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "C" "D" ..$ : chr [1:2] "A" "B" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "B" "A" ..$ : chr [1:2] "B" "A" ..$ : chr [1:2] "B" "A" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "B" "B" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "H" $ :List of 50 ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "A" "A" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "D" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "D" "B" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" ..$ : chr [1:2] "C" "C" > > > > cleanEx() > nameEx("veronica") > ### * veronica > > flush(stderr()); flush(stdout()) > > ### Name: veronica > ### Title: Genetic AFLP data of Veronica plants > ### Aliases: veronica veronica.coord > ### Keywords: datasets > > ### ** Examples > > data(veronica) > > > > cleanEx() > nameEx("waterdist") > ### * waterdist > > flush(stderr()); flush(stdout()) > > ### Name: waterdist > ### Title: Overwater distances between islands in the Aegean sea > ### Aliases: waterdist > ### Keywords: datasets > > ### ** Examples > > data(waterdist) > > > > ### *