R : Copyright 2005, The R Foundation for Statistical Computing Version 2.1.1 (2005-06-20), ISBN 3-900051-07-0 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. 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 a HTML browser interface to help. Type 'q()' to quit R. > ### *
> ### > attach(NULL, name = "CheckExEnv") > assign(".CheckExEnv", as.environment(2), pos = length(search())) # base > ## add some hooks to label plot pages for base and grid graphics > setHook("plot.new", ".newplot.hook") > setHook("persp", ".newplot.hook") > setHook("grid.newpage", ".gridplot.hook") > > assign("cleanEx", + function(env = .GlobalEnv) { + rm(list = ls(envir = env, all.names = TRUE), envir = env) + RNGkind("default", "default") + set.seed(1) + options(warn = 1) + delayedAssign("T", stop("T used instead of TRUE"), + assign.env = .CheckExEnv) + delayedAssign("F", stop("F used instead of FALSE"), + assign.env = .CheckExEnv) + sch <- search() + newitems <- sch[! sch %in% .oldSearch] + for(item in rev(newitems)) + eval(substitute(detach(item), list(item=item))) + missitems <- .oldSearch[! .oldSearch %in% sch] + if(length(missitems)) + warning("items ", paste(missitems, collapse=", "), + " have been removed from the search path") + }, + env = .CheckExEnv) > assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now > assign("ptime", proc.time(), env = .CheckExEnv) > grDevices::postscript("prabclus-Examples.ps") > assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv) > options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly")) > options(warn = 1) > library('prabclus') Loading required package: mclust Attaching package: 'mclust' The following object(s) are masked from package:stats : density > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .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) Loading required package: mva Warning: package 'mva' has been merged into 'stats' [1] -33087.29 [1] -33249.03 [1] -33338.22 [1] -33381.63 [1] -33401.38 > plot(chevron[,2:3],col=1+nnc$z) > > > > cleanEx(); ..nameEx <- "autoconst" > > ### * autoconst > > flush(stderr()); flush(stdout()) > > ### Name: autoconst > ### Title: Spatial autocorrelation parameter estimation > ### Aliases: autoconst autoreg > ### Keywords: spatial > > ### ** Examples > > data(kykladspecreg) > # Note: If you do not use the installed package, replace this by > # kykladspecreg <- read.table("(path/)kykladspecreg.dat") > data(nb) > # Note: If you do not use the installed package, replace this by > # nb <- list() > # for (i in 1:34) > # nb <- c(nb,list(scan(file="(path/)nb.dat", > # skip=i-1,nlines=1))) > set.seed(1234) > x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb) > ax <- autoconst(x,nperp=2) Calculating disjunction probability for original data 0.3306773 Estimating disj. parameter: Simulations for p= 0 Estimating disj. parameter: Simulations for p= 0.1 Estimating disj. parameter: Simulations for p= 0.2 Estimating disj. parameter: Simulations for p= 0.3 Estimating disj. parameter: Simulations for p= 0.4 Estimating disj. parameter: Simulations for p= 0.5 Estimating disj. parameter: Simulations for p= 0.6 Estimating disj. parameter: Simulations for p= 0.7 Estimating disj. parameter: Simulations for p= 0.8 Estimating disj. parameter: Simulations for p= 0.9 Estimating disj. parameter: Simulations for p= 1 Estimated disjunction parameter = 0.356101 Estimating disj. parameter: Simulations for p= 0.306101 Estimating disj. parameter: Simulations for p= 0.316101 Estimating disj. parameter: Simulations for p= 0.326101 Estimating disj. parameter: Simulations for p= 0.336101 Estimating disj. parameter: Simulations for p= 0.346101 Estimating disj. parameter: Simulations for p= 0.356101 Estimating disj. parameter: Simulations for p= 0.366101 Estimating disj. parameter: Simulations for p= 0.376101 Estimating disj. parameter: Simulations for p= 0.386101 Estimating disj. parameter: Simulations for p= 0.396101 Estimating disj. parameter: Simulations for p= 0.406101 Estimated disjunction parameter = 0.348588 > > > > 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) > # Note: If you do not use the installed package, replace this by > # nb <- list() > # for (i in 1:34) > # nb <- c(nb,list(scan(file="(path/)nb.dat", > # skip=i-1,nlines=1))) > set.seed(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 <- "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 = 2.2804, df = NA, p-value = 0.6889 > > > > 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) > # Note: If you do not use the installed package, replace this by > # nb <- list() > # for (i in 1:34) > # nb <- c(nb,list(scan(file="(path/)nb.dat", > # skip=i-1,nlines=1))) > set.seed(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 <- "distratio" > > ### * distratio > > flush(stderr()); flush(stdout()) > > ### Name: distratio > ### Title: Distance ratio test statistics for distance based clustering > ### Aliases: distratio > ### Keywords: cluster > > ### ** Examples > > data(kykladspecreg) > # Note: If you do not use the installed package, replace this by > # kykladspecreg <- read.table("(path/)kykladspecreg.dat") > j <- jaccard(t(kykladspecreg)) > distratio(j) $dr [1] 0.7158847 $lowmean [1] 0.7158847 $himean [1] 1 $prop [1] 0.25 > > > > 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 > > data(kykladspecreg) > # Note: If you do not use the installed package, replace this by > # kykladspecreg <- read.table("(path/)kykladspecreg.dat") > j <- jaccard(t(kykladspecreg)) > homogen.test(j, testdist="erdos") $p [1] 0.6094525 $p.twoside [1] 0.8640827 $iv [1] 10 $lambda [1] 10.55617 $distcut [1] 0.5 $ne [1] 80 > homogen.test(j, testdist="ling") [1] "Computation of Ling-probabilities..." [1] "finished." $p [1] 0.6163605 $iv [1] 10 $distcut [1] 0.5 $ne [1] 80 > > > > cleanEx(); ..nameEx <- "incmatrix" > > ### * incmatrix > > flush(stderr()); flush(stdout()) > > ### Name: incmatrix > ### Title: Nestedness matrix > ### Aliases: incmatrix > ### Keywords: spatial array > > ### ** Examples > > data(kykladspecreg) > # Note: If you do not use the installed package, replace this by > # kykladspecreg <- read.table("(path/)kykladspecreg.dat") > incmatrix(t(kykladspecreg))$ninc [1] 524 > > > > cleanEx(); ..nameEx <- "jaccard" > > ### * jaccard > > flush(stderr()); flush(stdout()) > > ### Name: jaccard > ### Title: Jaccard distance matrix > ### Aliases: jaccard > ### Keywords: cluster spatial > > ### ** Examples > > data(kykladspecreg) > # Note: If you do not use the installed package, replace this by > # kykladspecreg <- read.table("(path/)kykladspecreg.dat") > jaccard(t(kykladspecreg)) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.0000000 0.9655172 0.9285714 0.9629630 0.9629630 0.9655172 0.8888889 [2,] 0.9655172 0.0000000 0.8000000 1.0000000 1.0000000 0.5000000 0.8000000 [3,] 0.9285714 0.8000000 0.0000000 1.0000000 1.0000000 0.5000000 0.8000000 [4,] 0.9629630 1.0000000 1.0000000 0.0000000 1.0000000 1.0000000 1.0000000 [5,] 0.9629630 1.0000000 1.0000000 1.0000000 0.0000000 1.0000000 1.0000000 [6,] 0.9655172 0.5000000 0.5000000 1.0000000 1.0000000 0.0000000 1.0000000 [7,] 0.8888889 0.8000000 0.8000000 1.0000000 1.0000000 1.0000000 0.0000000 [8,] 0.5000000 1.0000000 1.0000000 0.9444444 0.9444444 1.0000000 1.0000000 [9,] 0.9259259 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [10,] 0.8148148 1.0000000 1.0000000 1.0000000 0.8000000 1.0000000 1.0000000 [11,] 0.9285714 1.0000000 1.0000000 0.6666667 1.0000000 1.0000000 1.0000000 [12,] 0.8571429 1.0000000 0.8571429 1.0000000 1.0000000 1.0000000 0.6666667 [13,] 0.3666667 0.9130435 0.9130435 0.9545455 0.9545455 0.8636364 1.0000000 [14,] 0.9259259 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [15,] 1.0000000 0.6666667 0.6666667 1.0000000 1.0000000 0.6666667 1.0000000 [16,] 0.8518519 0.8333333 0.8333333 1.0000000 1.0000000 1.0000000 0.2500000 [17,] 0.9629630 1.0000000 0.6666667 1.0000000 1.0000000 1.0000000 0.6666667 [18,] 0.9655172 0.5000000 0.5000000 1.0000000 1.0000000 0.0000000 1.0000000 [19,] 0.7857143 1.0000000 1.0000000 0.8571429 1.0000000 1.0000000 1.0000000 [20,] 0.4848485 0.9166667 0.9600000 0.9565217 0.9565217 0.9166667 1.0000000 [21,] 0.4333333 0.8500000 0.8500000 0.9500000 0.9500000 0.8500000 0.8500000 [22,] 0.9642857 0.7500000 0.3333333 1.0000000 1.0000000 0.3333333 1.0000000 [23,] 0.9629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [24,] 1.0000000 0.6666667 1.0000000 1.0000000 1.0000000 0.6666667 1.0000000 [25,] 0.9629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [26,] 0.8888889 0.8000000 0.8000000 1.0000000 1.0000000 0.8000000 0.5000000 [27,] 0.8888889 0.8000000 0.8000000 1.0000000 1.0000000 1.0000000 0.0000000 [28,] 0.9629630 1.0000000 0.6666667 1.0000000 1.0000000 0.6666667 1.0000000 [29,] 0.9629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [30,] 1.0000000 0.6666667 1.0000000 1.0000000 1.0000000 0.6666667 1.0000000 [31,] 0.9629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [32,] 0.9629630 1.0000000 0.6666667 1.0000000 1.0000000 0.6666667 1.0000000 [33,] 0.9629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [34,] 0.9629630 1.0000000 0.6666667 1.0000000 1.0000000 1.0000000 0.6666667 [35,] 0.9655172 0.0000000 0.8000000 1.0000000 1.0000000 0.5000000 0.8000000 [36,] 0.2121212 0.9062500 0.9062500 0.9687500 0.9687500 0.9062500 0.9062500 [37,] 0.9642857 0.7500000 0.7500000 1.0000000 1.0000000 0.7500000 1.0000000 [38,] 1.0000000 0.6666667 0.6666667 1.0000000 1.0000000 0.6666667 1.0000000 [39,] 0.9629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [40,] 0.9655172 0.5000000 0.5000000 1.0000000 1.0000000 0.0000000 1.0000000 [41,] 0.9655172 0.5000000 0.8000000 1.0000000 1.0000000 0.5000000 1.0000000 [42,] 0.8928571 1.0000000 1.0000000 0.7500000 0.7500000 1.0000000 1.0000000 [43,] 0.9629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [44,] 0.8888889 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [45,] 0.9629630 1.0000000 0.6666667 1.0000000 1.0000000 0.6666667 1.0000000 [46,] 0.6785714 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [47,] 1.0000000 0.6666667 0.6666667 1.0000000 1.0000000 0.6666667 1.0000000 [48,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [49,] 0.8518519 1.0000000 1.0000000 1.0000000 0.7500000 1.0000000 1.0000000 [50,] 1.0000000 0.6666667 1.0000000 1.0000000 1.0000000 0.6666667 1.0000000 [51,] 0.9629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [52,] 0.9259259 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [53,] 0.8518519 1.0000000 1.0000000 1.0000000 0.7500000 1.0000000 1.0000000 [54,] 0.9259259 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [55,] 0.9629630 1.0000000 0.6666667 1.0000000 1.0000000 0.6666667 1.0000000 [56,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [57,] 0.8888889 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [58,] 0.9629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [59,] 0.8518519 1.0000000 1.0000000 0.7500000 0.7500000 1.0000000 1.0000000 [60,] 0.9310345 0.6000000 0.2500000 1.0000000 1.0000000 0.2500000 0.8333333 [61,] 0.8888889 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [62,] 1.0000000 0.6666667 0.6666667 1.0000000 1.0000000 0.6666667 1.0000000 [63,] 0.9629630 1.0000000 0.6666667 1.0000000 1.0000000 0.6666667 1.0000000 [64,] 0.8928571 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [65,] 0.9642857 1.0000000 1.0000000 1.0000000 0.5000000 1.0000000 1.0000000 [66,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [67,] 0.9259259 1.0000000 1.0000000 0.5000000 0.5000000 1.0000000 1.0000000 [68,] 0.9629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [69,] 0.7037037 1.0000000 1.0000000 0.8750000 0.8750000 1.0000000 1.0000000 [70,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [71,] 0.8888889 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [72,] 0.9655172 0.0000000 0.8000000 1.0000000 1.0000000 0.5000000 0.8000000 [73,] 0.9259259 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [74,] 0.9259259 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [75,] 0.8620690 0.5000000 0.5000000 1.0000000 1.0000000 0.5000000 0.5000000 [76,] 0.9642857 0.7500000 0.3333333 1.0000000 1.0000000 0.3333333 1.0000000 [77,] 0.5925926 1.0000000 1.0000000 0.9090909 0.9090909 1.0000000 1.0000000 [78,] 0.9629630 1.0000000 0.6666667 1.0000000 1.0000000 0.6666667 1.0000000 [79,] 0.9629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [80,] 0.8888889 1.0000000 0.8000000 1.0000000 1.0000000 1.0000000 0.5000000 [,8] [,9] [,10] [,11] [,12] [,13] [,14] [1,] 0.5000000 0.9259259 0.8148148 0.9285714 0.8571429 0.3666667 0.9259259 [2,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9130435 1.0000000 [3,] 1.0000000 1.0000000 1.0000000 1.0000000 0.8571429 0.9130435 1.0000000 [4,] 0.9444444 1.0000000 1.0000000 0.6666667 1.0000000 0.9545455 1.0000000 [5,] 0.9444444 1.0000000 0.8000000 1.0000000 1.0000000 0.9545455 1.0000000 [6,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.8636364 1.0000000 [7,] 1.0000000 1.0000000 1.0000000 1.0000000 0.6666667 1.0000000 1.0000000 [8,] 0.0000000 0.8888889 0.7222222 0.9500000 1.0000000 0.5185185 0.9473684 [9,] 0.8888889 0.0000000 0.8333333 1.0000000 1.0000000 0.9090909 1.0000000 [10,] 0.7222222 0.8333333 0.0000000 1.0000000 1.0000000 0.7727273 1.0000000 [11,] 0.9500000 1.0000000 1.0000000 0.0000000 0.6666667 0.9130435 1.0000000 [12,] 1.0000000 1.0000000 1.0000000 0.6666667 0.0000000 0.9615385 1.0000000 [13,] 0.5185185 0.9090909 0.7727273 0.9130435 0.9615385 0.0000000 0.9565217 [14,] 0.9473684 1.0000000 1.0000000 1.0000000 1.0000000 0.9565217 0.0000000 [15,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9545455 1.0000000 [16,] 1.0000000 1.0000000 1.0000000 1.0000000 0.7142857 1.0000000 1.0000000 [17,] 1.0000000 1.0000000 1.0000000 1.0000000 0.8000000 1.0000000 1.0000000 [18,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.8636364 1.0000000 [19,] 0.6842105 0.8750000 0.9090909 0.8888889 1.0000000 0.8846154 0.8750000 [20,] 0.4230769 0.9130435 0.8333333 0.8695652 0.9230769 0.3928571 0.9130435 [21,] 0.6428571 0.9000000 0.7500000 0.9047619 0.8095238 0.4444444 0.9523810 [22,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9090909 1.0000000 [23,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.5000000 [24,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9545455 1.0000000 [25,] 0.9444444 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [26,] 1.0000000 1.0000000 1.0000000 1.0000000 0.8571429 0.9583333 1.0000000 [27,] 1.0000000 1.0000000 1.0000000 1.0000000 0.6666667 1.0000000 1.0000000 [28,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9545455 1.0000000 [29,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9545455 1.0000000 [30,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9545455 1.0000000 [31,] 0.9444444 1.0000000 1.0000000 1.0000000 1.0000000 0.9545455 1.0000000 [32,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9545455 1.0000000 [33,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [34,] 1.0000000 1.0000000 1.0000000 1.0000000 0.8000000 1.0000000 1.0000000 [35,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9130435 1.0000000 [36,] 0.4848485 0.9375000 0.8437500 0.9062500 0.8437500 0.3125000 0.9375000 [37,] 0.9473684 0.6666667 0.8333333 1.0000000 1.0000000 0.9090909 1.0000000 [38,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9545455 1.0000000 [39,] 1.0000000 1.0000000 1.0000000 0.6666667 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1.0000000 0.9565217 1.0000000 [21,] 0.9500000 1.0000000 0.9047619 [22,] 0.5000000 1.0000000 1.0000000 [23,] 1.0000000 1.0000000 1.0000000 [24,] 1.0000000 1.0000000 1.0000000 [25,] 1.0000000 1.0000000 1.0000000 [26,] 0.6666667 1.0000000 0.8000000 [27,] 1.0000000 1.0000000 0.5000000 [28,] 0.0000000 1.0000000 1.0000000 [29,] 1.0000000 1.0000000 1.0000000 [30,] 1.0000000 1.0000000 1.0000000 [31,] 1.0000000 1.0000000 1.0000000 [32,] 0.0000000 1.0000000 1.0000000 [33,] 1.0000000 1.0000000 0.6666667 [34,] 1.0000000 1.0000000 0.6666667 [35,] 1.0000000 1.0000000 1.0000000 [36,] 0.9687500 0.9687500 0.9393939 [37,] 1.0000000 1.0000000 1.0000000 [38,] 1.0000000 1.0000000 1.0000000 [39,] 1.0000000 0.0000000 1.0000000 [40,] 0.6666667 1.0000000 1.0000000 [41,] 1.0000000 1.0000000 1.0000000 [42,] 1.0000000 1.0000000 1.0000000 [43,] 1.0000000 1.0000000 1.0000000 [44,] 1.0000000 1.0000000 1.0000000 [45,] 0.0000000 1.0000000 1.0000000 [46,] 1.0000000 1.0000000 1.0000000 [47,] 1.0000000 1.0000000 1.0000000 [48,] 1.0000000 1.0000000 1.0000000 [49,] 1.0000000 1.0000000 1.0000000 [50,] 1.0000000 1.0000000 1.0000000 [51,] 1.0000000 1.0000000 1.0000000 [52,] 1.0000000 1.0000000 1.0000000 [53,] 1.0000000 1.0000000 1.0000000 [54,] 1.0000000 1.0000000 1.0000000 [55,] 0.0000000 1.0000000 1.0000000 [56,] 1.0000000 1.0000000 1.0000000 [57,] 1.0000000 1.0000000 1.0000000 [58,] 1.0000000 1.0000000 1.0000000 [59,] 1.0000000 1.0000000 1.0000000 [60,] 0.7500000 1.0000000 0.8333333 [61,] 1.0000000 1.0000000 1.0000000 [62,] 1.0000000 1.0000000 1.0000000 [63,] 0.0000000 1.0000000 1.0000000 [64,] 1.0000000 1.0000000 1.0000000 [65,] 1.0000000 1.0000000 1.0000000 [66,] 1.0000000 1.0000000 1.0000000 [67,] 1.0000000 1.0000000 1.0000000 [68,] 1.0000000 1.0000000 1.0000000 [69,] 1.0000000 1.0000000 1.0000000 [70,] 1.0000000 1.0000000 1.0000000 [71,] 1.0000000 0.6666667 1.0000000 [72,] 1.0000000 1.0000000 1.0000000 [73,] 1.0000000 1.0000000 1.0000000 [74,] 1.0000000 1.0000000 1.0000000 [75,] 0.8333333 1.0000000 0.7142857 [76,] 0.5000000 1.0000000 1.0000000 [77,] 1.0000000 0.9090909 1.0000000 [78,] 0.0000000 1.0000000 1.0000000 [79,] 1.0000000 0.0000000 1.0000000 [80,] 1.0000000 1.0000000 0.0000000 > > > > cleanEx(); ..nameEx <- "kulczynski" > > ### * kulczynski > > flush(stderr()); flush(stdout()) > > ### Name: kulczynski > ### Title: Kulczynski distance matrix > ### Aliases: kulczynski > ### Keywords: cluster spatial > > ### ** Examples > > data(kykladspecreg) > # Note: If you do not use the installed package, replace this by > # kykladspecreg <- read.table("(path/)kykladspecreg.dat") > kulczynski(t(kykladspecreg)) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.0000000 0.8148148 0.6296296 0.4814815 0.4814815 0.8148148 0.4444444 [2,] 0.8148148 0.0000000 0.6666667 1.0000000 1.0000000 0.3333333 0.6666667 [3,] 0.6296296 0.6666667 0.0000000 1.0000000 1.0000000 0.3333333 0.6666667 [4,] 0.4814815 1.0000000 1.0000000 0.0000000 1.0000000 1.0000000 1.0000000 [5,] 0.4814815 1.0000000 1.0000000 1.0000000 0.0000000 1.0000000 1.0000000 [6,] 0.8148148 0.3333333 0.3333333 1.0000000 1.0000000 0.0000000 1.0000000 [7,] 0.4444444 0.6666667 0.6666667 1.0000000 1.0000000 1.0000000 0.0000000 [8,] 0.3055556 1.0000000 1.0000000 0.4722222 0.4722222 1.0000000 1.0000000 [9,] 0.4629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [10,] 0.4074074 1.0000000 1.0000000 1.0000000 0.4000000 1.0000000 1.0000000 [11,] 0.6296296 1.0000000 1.0000000 0.3333333 1.0000000 1.0000000 1.0000000 [12,] 0.5259259 1.0000000 0.7333333 1.0000000 1.0000000 1.0000000 0.4666667 [13,] 0.2163300 0.6212121 0.6212121 0.4772727 0.4772727 0.4318182 1.0000000 [14,] 0.4629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [15,] 1.0000000 0.3333333 0.3333333 1.0000000 1.0000000 0.3333333 1.0000000 [16,] 0.4259259 0.7083333 0.7083333 1.0000000 1.0000000 1.0000000 0.1250000 [17,] 0.4814815 1.0000000 0.3333333 1.0000000 1.0000000 1.0000000 0.3333333 [18,] 0.8148148 0.3333333 0.3333333 1.0000000 1.0000000 0.0000000 1.0000000 [19,] 0.4603175 1.0000000 1.0000000 0.4285714 1.0000000 1.0000000 1.0000000 [20,] 0.3156200 0.6231884 0.8115942 0.4782609 0.4782609 0.6231884 1.0000000 [21,] 0.2601852 0.4250000 0.4250000 0.4750000 0.4750000 0.4250000 0.4250000 [22,] 0.7314815 0.5833333 0.1666667 1.0000000 1.0000000 0.1666667 1.0000000 [23,] 0.4814815 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [24,] 1.0000000 0.3333333 1.0000000 1.0000000 1.0000000 0.3333333 1.0000000 [25,] 0.4814815 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [26,] 0.4444444 0.6666667 0.6666667 1.0000000 1.0000000 0.6666667 0.3333333 [27,] 0.4444444 0.6666667 0.6666667 1.0000000 1.0000000 1.0000000 0.0000000 [28,] 0.4814815 1.0000000 0.3333333 1.0000000 1.0000000 0.3333333 1.0000000 [29,] 0.4814815 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [30,] 1.0000000 0.3333333 1.0000000 1.0000000 1.0000000 0.3333333 1.0000000 [31,] 0.4814815 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [32,] 0.4814815 1.0000000 0.3333333 1.0000000 1.0000000 0.3333333 1.0000000 [33,] 0.4814815 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [34,] 0.4814815 1.0000000 0.3333333 1.0000000 1.0000000 1.0000000 0.3333333 [35,] 0.8148148 0.0000000 0.6666667 1.0000000 1.0000000 0.3333333 0.6666667 [36,] 0.1122685 0.4531250 0.4531250 0.4843750 0.4843750 0.4531250 0.4531250 [37,] 0.7314815 0.5833333 0.5833333 1.0000000 1.0000000 0.5833333 1.0000000 [38,] 1.0000000 0.3333333 0.3333333 1.0000000 1.0000000 0.3333333 1.0000000 [39,] 0.4814815 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [40,] 0.8148148 0.3333333 0.3333333 1.0000000 1.0000000 0.0000000 1.0000000 [41,] 0.8148148 0.3333333 0.6666667 1.0000000 1.0000000 0.3333333 1.0000000 [42,] 0.5694444 1.0000000 1.0000000 0.3750000 0.3750000 1.0000000 1.0000000 [43,] 0.4814815 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [44,] 0.4444444 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [45,] 0.4814815 1.0000000 0.3333333 1.0000000 1.0000000 0.3333333 1.0000000 [46,] 0.3833333 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [47,] 1.0000000 0.3333333 0.3333333 1.0000000 1.0000000 0.3333333 1.0000000 [48,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [49,] 0.4259259 1.0000000 1.0000000 1.0000000 0.3750000 1.0000000 1.0000000 [50,] 1.0000000 0.3333333 1.0000000 1.0000000 1.0000000 0.3333333 1.0000000 [51,] 0.4814815 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [52,] 0.4629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [53,] 0.4259259 1.0000000 1.0000000 1.0000000 0.3750000 1.0000000 1.0000000 [54,] 0.4629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [55,] 0.4814815 1.0000000 0.3333333 1.0000000 1.0000000 0.3333333 1.0000000 [56,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [57,] 0.4444444 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [58,] 0.4814815 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [59,] 0.4259259 1.0000000 1.0000000 0.3750000 0.3750000 1.0000000 1.0000000 [60,] 0.7129630 0.4166667 0.1250000 1.0000000 1.0000000 0.1250000 0.7083333 [61,] 0.4444444 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [62,] 1.0000000 0.3333333 0.3333333 1.0000000 1.0000000 0.3333333 1.0000000 [63,] 0.4814815 1.0000000 0.3333333 1.0000000 1.0000000 0.3333333 1.0000000 [64,] 0.5694444 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [65,] 0.7314815 1.0000000 1.0000000 1.0000000 0.2500000 1.0000000 1.0000000 [66,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [67,] 0.4629630 1.0000000 1.0000000 0.2500000 0.2500000 1.0000000 1.0000000 [68,] 0.4814815 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [69,] 0.3518519 1.0000000 1.0000000 0.4375000 0.4375000 1.0000000 1.0000000 [70,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [71,] 0.4444444 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [72,] 0.8148148 0.0000000 0.6666667 1.0000000 1.0000000 0.3333333 0.6666667 [73,] 0.4629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [74,] 0.4629630 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [75,] 0.5925926 0.2500000 0.2500000 1.0000000 1.0000000 0.2500000 0.2500000 [76,] 0.7314815 0.5833333 0.1666667 1.0000000 1.0000000 0.1666667 1.0000000 [77,] 0.2962963 1.0000000 1.0000000 0.4545455 0.4545455 1.0000000 1.0000000 [78,] 0.4814815 1.0000000 0.3333333 1.0000000 1.0000000 0.3333333 1.0000000 [79,] 0.4814815 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [80,] 0.4444444 1.0000000 0.6666667 1.0000000 1.0000000 1.0000000 0.3333333 [,8] [,9] [,10] [,11] [,12] [,13] [,14] [1,] 0.3055556 0.4629630 0.4074074 0.6296296 0.5259259 0.2163300 0.4629630 [2,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.6212121 1.0000000 [3,] 1.0000000 1.0000000 1.0000000 1.0000000 0.7333333 0.6212121 1.0000000 [4,] 0.4722222 1.0000000 1.0000000 0.3333333 1.0000000 0.4772727 1.0000000 [5,] 0.4722222 1.0000000 0.4000000 1.0000000 1.0000000 0.4772727 1.0000000 [6,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4318182 1.0000000 [7,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4666667 1.0000000 1.0000000 [8,] 0.0000000 0.4444444 0.3611111 0.8055556 1.0000000 0.3434343 0.7222222 [9,] 0.4444444 0.0000000 0.6500000 1.0000000 1.0000000 0.4545455 1.0000000 [10,] 0.3611111 0.6500000 0.0000000 1.0000000 1.0000000 0.3863636 1.0000000 [11,] 0.8055556 1.0000000 1.0000000 0.0000000 0.4666667 0.6212121 1.0000000 [12,] 1.0000000 1.0000000 1.0000000 0.4666667 0.0000000 0.8772727 1.0000000 [13,] 0.3434343 0.4545455 0.3863636 0.6212121 0.8772727 0.0000000 0.7272727 [14,] 0.7222222 1.0000000 1.0000000 1.0000000 1.0000000 0.7272727 0.0000000 [15,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [16,] 1.0000000 1.0000000 1.0000000 1.0000000 0.5500000 1.0000000 1.0000000 [17,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4000000 1.0000000 1.0000000 [18,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4318182 1.0000000 [19,] 0.4047619 0.6785714 0.8285714 0.7619048 1.0000000 0.7175325 0.6785714 [20,] 0.2572464 0.4565217 0.5130435 0.4347826 0.7565217 0.2440711 0.4565217 [21,] 0.4722222 0.4500000 0.3750000 0.6166667 0.5000000 0.2840909 0.7250000 [22,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4545455 1.0000000 [23,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.2500000 [24,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [25,] 0.4722222 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [26,] 1.0000000 1.0000000 1.0000000 1.0000000 0.7333333 0.8106061 1.0000000 [27,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4666667 1.0000000 1.0000000 [28,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [29,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [30,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [31,] 0.4722222 1.0000000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [32,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [33,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [34,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4000000 1.0000000 1.0000000 [35,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.6212121 1.0000000 [36,] 0.2621528 0.4687500 0.4218750 0.4531250 0.4218750 0.1562500 0.4687500 [37,] 0.7222222 0.5000000 0.6500000 1.0000000 1.0000000 0.4545455 1.0000000 [38,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [39,] 1.0000000 1.0000000 1.0000000 0.3333333 0.4000000 0.4772727 1.0000000 [40,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4318182 1.0000000 [41,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4318182 1.0000000 [42,] 0.5416667 1.0000000 0.7750000 0.4166667 0.7750000 0.5568182 1.0000000 [43,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [44,] 0.4166667 1.0000000 0.7333333 1.0000000 1.0000000 0.4318182 1.0000000 [45,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [46,] 0.3777778 0.4000000 0.5500000 1.0000000 1.0000000 0.3454545 0.7000000 [47,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [48,] 1.0000000 1.0000000 1.0000000 0.3333333 0.4000000 1.0000000 1.0000000 [49,] 0.6944444 1.0000000 0.7750000 1.0000000 1.0000000 0.4090909 1.0000000 [50,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [51,] 0.4722222 0.2500000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [52,] 0.7222222 0.5000000 1.0000000 1.0000000 1.0000000 0.7272727 0.5000000 [53,] 0.3888889 0.2500000 0.3250000 1.0000000 1.0000000 0.4090909 1.0000000 [54,] 0.7222222 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.5000000 [55,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [56,] 0.4166667 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [57,] 0.4166667 0.1666667 0.4666667 1.0000000 1.0000000 0.4318182 1.0000000 [58,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.2500000 [59,] 0.3888889 1.0000000 0.7750000 0.7083333 1.0000000 0.4090909 0.6250000 [60,] 1.0000000 1.0000000 1.0000000 1.0000000 0.7750000 0.5568182 1.0000000 [61,] 0.6111111 1.0000000 0.4666667 1.0000000 1.0000000 0.6212121 0.5833333 [62,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [63,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [64,] 0.5416667 0.2500000 0.5500000 1.0000000 1.0000000 0.4090909 1.0000000 [65,] 0.7222222 1.0000000 0.6500000 0.5833333 0.6500000 0.7272727 1.0000000 [66,] 0.4166667 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [67,] 0.4444444 1.0000000 0.6500000 0.5833333 1.0000000 0.4545455 1.0000000 [68,] 0.4722222 1.0000000 0.4000000 1.0000000 1.0000000 0.4772727 1.0000000 [69,] 0.3680556 1.0000000 0.8375000 0.7708333 1.0000000 0.3181818 0.6875000 [70,] 1.0000000 1.0000000 1.0000000 0.3333333 0.4000000 1.0000000 1.0000000 [71,] 0.6111111 1.0000000 0.7333333 0.6666667 0.7333333 0.4318182 1.0000000 [72,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.6212121 1.0000000 [73,] 0.4444444 0.0000000 0.6500000 1.0000000 1.0000000 0.4545455 1.0000000 [74,] 0.7222222 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.5000000 [75,] 1.0000000 1.0000000 1.0000000 1.0000000 0.6333333 0.6818182 1.0000000 [76,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4545455 1.0000000 [77,] 0.4141414 1.0000000 0.5636364 0.5757576 0.8545455 0.2500000 1.0000000 [78,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4772727 1.0000000 [79,] 1.0000000 1.0000000 1.0000000 0.3333333 0.4000000 0.4772727 1.0000000 [80,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4666667 1.0000000 1.0000000 [,15] [,16] [,17] [,18] [,19] [,20] [,21] [1,] 1.0000000 0.4259259 0.4814815 0.8148148 0.4603175 0.3156200 0.2601852 [2,] 0.3333333 0.7083333 1.0000000 0.3333333 1.0000000 0.6231884 0.4250000 [3,] 0.3333333 0.7083333 0.3333333 0.3333333 1.0000000 0.8115942 0.4250000 [4,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4285714 0.4782609 0.4750000 [5,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4782609 0.4750000 [6,] 0.3333333 1.0000000 1.0000000 0.0000000 1.0000000 0.6231884 0.4250000 [7,] 1.0000000 0.1250000 0.3333333 1.0000000 1.0000000 1.0000000 0.4250000 [8,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4047619 0.2572464 0.4722222 [9,] 1.0000000 1.0000000 1.0000000 1.0000000 0.6785714 0.4565217 0.4500000 [10,] 1.0000000 1.0000000 1.0000000 1.0000000 0.8285714 0.5130435 0.3750000 [11,] 1.0000000 1.0000000 1.0000000 1.0000000 0.7619048 0.4347826 0.6166667 [12,] 1.0000000 0.5500000 0.4000000 1.0000000 1.0000000 0.7565217 0.5000000 [13,] 0.4772727 1.0000000 1.0000000 0.4318182 0.7175325 0.2440711 0.2840909 [14,] 1.0000000 1.0000000 1.0000000 1.0000000 0.6785714 0.4565217 0.7250000 [15,] 0.0000000 1.0000000 1.0000000 0.3333333 1.0000000 0.4782609 0.4750000 [16,] 1.0000000 0.0000000 0.3750000 1.0000000 1.0000000 1.0000000 0.5500000 [17,] 1.0000000 0.3750000 0.0000000 1.0000000 1.0000000 1.0000000 0.4750000 [18,] 0.3333333 1.0000000 1.0000000 0.0000000 1.0000000 0.6231884 0.4250000 [19,] 1.0000000 1.0000000 1.0000000 1.0000000 0.0000000 0.3478261 0.7107143 [20,] 0.4782609 1.0000000 1.0000000 0.6231884 0.3478261 0.0000000 0.3456522 [21,] 0.4750000 0.5500000 0.4750000 0.4250000 0.7107143 0.3456522 0.0000000 [22,] 0.2500000 1.0000000 1.0000000 0.1666667 1.0000000 0.7282609 0.4500000 [23,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4285714 0.4782609 1.0000000 [24,] 1.0000000 1.0000000 1.0000000 0.3333333 1.0000000 0.4782609 0.4750000 [25,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4285714 0.4782609 1.0000000 [26,] 1.0000000 0.4166667 1.0000000 0.6666667 1.0000000 1.0000000 0.4250000 [27,] 1.0000000 0.1250000 0.3333333 1.0000000 1.0000000 1.0000000 0.4250000 [28,] 1.0000000 1.0000000 1.0000000 0.3333333 1.0000000 1.0000000 0.4750000 [29,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4782609 0.4750000 [30,] 1.0000000 1.0000000 1.0000000 0.3333333 1.0000000 0.4782609 0.4750000 [31,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4782609 1.0000000 [32,] 1.0000000 1.0000000 1.0000000 0.3333333 1.0000000 1.0000000 0.4750000 [33,] 1.0000000 0.3750000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [34,] 1.0000000 0.3750000 0.0000000 1.0000000 1.0000000 1.0000000 0.4750000 [35,] 0.3333333 0.7083333 1.0000000 0.3333333 1.0000000 0.6231884 0.4250000 [36,] 0.4843750 0.5781250 0.4843750 0.4531250 0.3906250 0.1406250 0.1875000 [37,] 0.2500000 1.0000000 1.0000000 0.5833333 1.0000000 0.4565217 0.4500000 [38,] 0.0000000 1.0000000 1.0000000 0.3333333 1.0000000 0.4782609 0.4750000 [39,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4782609 1.0000000 [40,] 0.3333333 1.0000000 1.0000000 0.0000000 1.0000000 0.6231884 0.4250000 [41,] 0.3333333 1.0000000 1.0000000 0.3333333 1.0000000 0.6231884 0.6166667 [42,] 1.0000000 1.0000000 1.0000000 1.0000000 0.8035714 0.5597826 0.5500000 [43,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4782609 0.4750000 [44,] 1.0000000 1.0000000 1.0000000 1.0000000 0.7619048 0.4347826 0.4250000 [45,] 1.0000000 1.0000000 1.0000000 0.3333333 1.0000000 1.0000000 0.4750000 [46,] 1.0000000 1.0000000 1.0000000 1.0000000 0.6357143 0.3543478 0.3250000 [47,] 0.0000000 1.0000000 1.0000000 0.3333333 1.0000000 0.4782609 0.4750000 [48,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4782609 0.4750000 [49,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4130435 0.4000000 [50,] 1.0000000 1.0000000 1.0000000 0.3333333 1.0000000 0.4782609 0.4750000 [51,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4285714 0.4782609 0.4750000 [52,] 1.0000000 1.0000000 1.0000000 1.0000000 0.3571429 0.4565217 0.7250000 [53,] 1.0000000 1.0000000 1.0000000 1.0000000 0.8035714 0.4130435 0.4000000 [54,] 1.0000000 1.0000000 1.0000000 1.0000000 0.3571429 0.4565217 1.0000000 [55,] 1.0000000 1.0000000 1.0000000 0.3333333 1.0000000 1.0000000 0.4750000 [56,] 1.0000000 1.0000000 1.0000000 1.0000000 0.7619048 0.6231884 1.0000000 [57,] 1.0000000 1.0000000 1.0000000 1.0000000 0.7619048 0.6231884 0.4250000 [58,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4285714 0.4782609 1.0000000 [59,] 1.0000000 1.0000000 1.0000000 1.0000000 0.8035714 0.4130435 0.5500000 [60,] 0.3750000 0.7500000 0.3750000 0.1250000 1.0000000 0.7065217 0.4000000 [61,] 1.0000000 1.0000000 1.0000000 1.0000000 0.5238095 0.6231884 0.6166667 [62,] 0.0000000 1.0000000 1.0000000 0.3333333 1.0000000 0.4782609 0.4750000 [63,] 1.0000000 1.0000000 1.0000000 0.3333333 1.0000000 1.0000000 0.4750000 [64,] 1.0000000 1.0000000 1.0000000 1.0000000 0.8035714 0.5597826 0.5500000 [65,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4565217 0.4500000 [66,] 1.0000000 1.0000000 1.0000000 1.0000000 0.7619048 0.6231884 1.0000000 [67,] 1.0000000 1.0000000 1.0000000 1.0000000 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1.0000000 0.4375000 0.3750000 [43,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [44,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.5416667 1.0000000 [45,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [46,] 0.4750000 1.0000000 0.7833333 1.0000000 1.0000000 0.5500000 1.0000000 [47,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [48,] 1.0000000 0.2500000 1.0000000 1.0000000 1.0000000 1.0000000 0.0000000 [49,] 1.0000000 0.6250000 1.0000000 0.6250000 1.0000000 0.4375000 1.0000000 [50,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [51,] 0.3750000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [52,] 0.6250000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [53,] 0.5000000 0.6250000 1.0000000 0.6250000 0.3750000 0.8125000 1.0000000 [54,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [55,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [56,] 1.0000000 1.0000000 0.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [57,] 0.1250000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [58,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [59,] 1.0000000 0.6250000 1.0000000 0.2500000 1.0000000 0.4375000 1.0000000 [60,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [61,] 0.7083333 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [62,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [63,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [64,] 0.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [65,] 1.0000000 0.0000000 1.0000000 0.5000000 1.0000000 0.6875000 0.2500000 [66,] 1.0000000 1.0000000 0.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [67,] 1.0000000 0.5000000 1.0000000 0.0000000 1.0000000 0.3750000 1.0000000 [68,] 1.0000000 1.0000000 1.0000000 1.0000000 0.0000000 1.0000000 1.0000000 [69,] 1.0000000 0.6875000 1.0000000 0.3750000 1.0000000 0.0000000 1.0000000 [70,] 1.0000000 0.2500000 1.0000000 1.0000000 1.0000000 1.0000000 0.0000000 [71,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.7708333 1.0000000 [72,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [73,] 0.2500000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [74,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [75,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [76,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [77,] 1.0000000 0.7045455 1.0000000 0.4090909 0.4545455 0.3522727 1.0000000 [78,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [79,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [80,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [,71] [,72] [,73] [,74] [,75] [,76] [,77] [1,] 0.4444444 0.8148148 0.4629630 0.4629630 0.5925926 0.7314815 0.2962963 [2,] 1.0000000 0.0000000 1.0000000 1.0000000 0.2500000 0.5833333 1.0000000 [3,] 1.0000000 0.6666667 1.0000000 1.0000000 0.2500000 0.1666667 1.0000000 [4,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4545455 [5,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4545455 [6,] 1.0000000 0.3333333 1.0000000 1.0000000 0.2500000 0.1666667 1.0000000 [7,] 1.0000000 0.6666667 1.0000000 1.0000000 0.2500000 1.0000000 1.0000000 [8,] 0.6111111 1.0000000 0.4444444 0.7222222 1.0000000 1.0000000 0.4141414 [9,] 1.0000000 1.0000000 0.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [10,] 0.7333333 1.0000000 0.6500000 1.0000000 1.0000000 1.0000000 0.5636364 [11,] 0.6666667 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.5757576 [12,] 0.7333333 1.0000000 1.0000000 1.0000000 0.6333333 1.0000000 0.8545455 [13,] 0.4318182 0.6212121 0.4545455 1.0000000 0.6818182 0.4545455 0.2500000 [14,] 1.0000000 1.0000000 1.0000000 0.5000000 1.0000000 1.0000000 1.0000000 [15,] 1.0000000 0.3333333 1.0000000 1.0000000 0.4166667 0.2500000 1.0000000 [16,] 1.0000000 0.7083333 1.0000000 1.0000000 0.3750000 1.0000000 1.0000000 [17,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4166667 1.0000000 1.0000000 [18,] 1.0000000 0.3333333 1.0000000 1.0000000 0.2500000 0.1666667 1.0000000 [19,] 0.7619048 1.0000000 0.6785714 0.3571429 1.0000000 1.0000000 0.7662338 [20,] 0.4347826 0.6231884 0.4565217 0.4565217 0.7898551 0.7282609 0.3952569 [21,] 0.6166667 0.4250000 0.4500000 1.0000000 0.3500000 0.4500000 0.5068182 [22,] 1.0000000 0.5833333 1.0000000 1.0000000 0.3333333 0.0000000 1.0000000 [23,] 1.0000000 1.0000000 1.0000000 0.2500000 1.0000000 1.0000000 1.0000000 [24,] 1.0000000 0.3333333 1.0000000 1.0000000 0.4166667 1.0000000 1.0000000 [25,] 1.0000000 1.0000000 1.0000000 0.2500000 1.0000000 1.0000000 1.0000000 [26,] 1.0000000 0.6666667 1.0000000 1.0000000 0.2500000 0.5833333 1.0000000 [27,] 1.0000000 0.6666667 1.0000000 1.0000000 0.2500000 1.0000000 1.0000000 [28,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4166667 0.2500000 1.0000000 [29,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4545455 [30,] 1.0000000 0.3333333 1.0000000 1.0000000 0.4166667 1.0000000 1.0000000 [31,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4545455 [32,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4166667 0.2500000 1.0000000 [33,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [34,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4166667 1.0000000 1.0000000 [35,] 1.0000000 0.0000000 1.0000000 1.0000000 0.2500000 0.5833333 1.0000000 [36,] 0.4531250 0.4531250 0.4687500 0.4687500 0.4062500 0.4687500 0.3281250 [37,] 1.0000000 0.5833333 0.5000000 1.0000000 0.6666667 0.5000000 1.0000000 [38,] 1.0000000 0.3333333 1.0000000 1.0000000 0.4166667 0.2500000 1.0000000 [39,] 0.3333333 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4545455 [40,] 1.0000000 0.3333333 1.0000000 1.0000000 0.2500000 0.1666667 1.0000000 [41,] 1.0000000 0.3333333 1.0000000 1.0000000 0.5000000 0.5833333 1.0000000 [42,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4886364 [43,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4545455 [44,] 0.3333333 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.3636364 [45,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4166667 0.2500000 1.0000000 [46,] 0.5666667 1.0000000 0.4000000 1.0000000 1.0000000 1.0000000 0.6181818 [47,] 1.0000000 0.3333333 1.0000000 1.0000000 0.4166667 0.2500000 1.0000000 [48,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [49,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4886364 [50,] 1.0000000 0.3333333 1.0000000 1.0000000 0.4166667 1.0000000 1.0000000 [51,] 1.0000000 1.0000000 0.2500000 1.0000000 1.0000000 1.0000000 1.0000000 [52,] 1.0000000 1.0000000 0.5000000 0.5000000 1.0000000 1.0000000 1.0000000 [53,] 1.0000000 1.0000000 0.2500000 1.0000000 1.0000000 1.0000000 0.6590909 [54,] 1.0000000 1.0000000 1.0000000 0.5000000 1.0000000 1.0000000 1.0000000 [55,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4166667 0.2500000 1.0000000 [56,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [57,] 1.0000000 1.0000000 0.1666667 1.0000000 1.0000000 1.0000000 1.0000000 [58,] 1.0000000 1.0000000 1.0000000 0.2500000 1.0000000 1.0000000 1.0000000 [59,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.6590909 [60,] 1.0000000 0.4166667 1.0000000 1.0000000 0.1666667 0.2500000 1.0000000 [61,] 0.6666667 1.0000000 1.0000000 0.5833333 1.0000000 1.0000000 0.7878788 [62,] 1.0000000 0.3333333 1.0000000 1.0000000 0.4166667 0.2500000 1.0000000 [63,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4166667 0.2500000 1.0000000 [64,] 1.0000000 1.0000000 0.2500000 1.0000000 1.0000000 1.0000000 1.0000000 [65,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.7045455 [66,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [67,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4090909 [68,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4545455 [69,] 0.7708333 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.3522727 [70,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [71,] 0.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.3636364 [72,] 1.0000000 0.0000000 1.0000000 1.0000000 0.2500000 0.5833333 1.0000000 [73,] 1.0000000 1.0000000 0.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [74,] 1.0000000 1.0000000 1.0000000 0.0000000 1.0000000 1.0000000 1.0000000 [75,] 1.0000000 0.2500000 1.0000000 1.0000000 0.0000000 0.3333333 1.0000000 [76,] 1.0000000 0.5833333 1.0000000 1.0000000 0.3333333 0.0000000 1.0000000 [77,] 0.3636364 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.0000000 [78,] 1.0000000 1.0000000 1.0000000 1.0000000 0.4166667 0.2500000 1.0000000 [79,] 0.3333333 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4545455 [80,] 1.0000000 1.0000000 1.0000000 1.0000000 0.5000000 1.0000000 1.0000000 [,78] [,79] [,80] [1,] 0.4814815 0.4814815 0.4444444 [2,] 1.0000000 1.0000000 1.0000000 [3,] 0.3333333 1.0000000 0.6666667 [4,] 1.0000000 1.0000000 1.0000000 [5,] 1.0000000 1.0000000 1.0000000 [6,] 0.3333333 1.0000000 1.0000000 [7,] 1.0000000 1.0000000 0.3333333 [8,] 1.0000000 1.0000000 1.0000000 [9,] 1.0000000 1.0000000 1.0000000 [10,] 1.0000000 1.0000000 1.0000000 [11,] 1.0000000 0.3333333 1.0000000 [12,] 1.0000000 0.4000000 0.4666667 [13,] 0.4772727 0.4772727 1.0000000 [14,] 1.0000000 1.0000000 1.0000000 [15,] 1.0000000 1.0000000 1.0000000 [16,] 1.0000000 1.0000000 0.1250000 [17,] 1.0000000 1.0000000 0.3333333 [18,] 0.3333333 1.0000000 1.0000000 [19,] 1.0000000 1.0000000 1.0000000 [20,] 1.0000000 0.4782609 1.0000000 [21,] 0.4750000 1.0000000 0.6166667 [22,] 0.2500000 1.0000000 1.0000000 [23,] 1.0000000 1.0000000 1.0000000 [24,] 1.0000000 1.0000000 1.0000000 [25,] 1.0000000 1.0000000 1.0000000 [26,] 0.3333333 1.0000000 0.6666667 [27,] 1.0000000 1.0000000 0.3333333 [28,] 0.0000000 1.0000000 1.0000000 [29,] 1.0000000 1.0000000 1.0000000 [30,] 1.0000000 1.0000000 1.0000000 [31,] 1.0000000 1.0000000 1.0000000 [32,] 0.0000000 1.0000000 1.0000000 [33,] 1.0000000 1.0000000 0.3333333 [34,] 1.0000000 1.0000000 0.3333333 [35,] 1.0000000 1.0000000 1.0000000 [36,] 0.4843750 0.4843750 0.6354167 [37,] 1.0000000 1.0000000 1.0000000 [38,] 1.0000000 1.0000000 1.0000000 [39,] 1.0000000 0.0000000 1.0000000 [40,] 0.3333333 1.0000000 1.0000000 [41,] 1.0000000 1.0000000 1.0000000 [42,] 1.0000000 1.0000000 1.0000000 [43,] 1.0000000 1.0000000 1.0000000 [44,] 1.0000000 1.0000000 1.0000000 [45,] 0.0000000 1.0000000 1.0000000 [46,] 1.0000000 1.0000000 1.0000000 [47,] 1.0000000 1.0000000 1.0000000 [48,] 1.0000000 1.0000000 1.0000000 [49,] 1.0000000 1.0000000 1.0000000 [50,] 1.0000000 1.0000000 1.0000000 [51,] 1.0000000 1.0000000 1.0000000 [52,] 1.0000000 1.0000000 1.0000000 [53,] 1.0000000 1.0000000 1.0000000 [54,] 1.0000000 1.0000000 1.0000000 [55,] 0.0000000 1.0000000 1.0000000 [56,] 1.0000000 1.0000000 1.0000000 [57,] 1.0000000 1.0000000 1.0000000 [58,] 1.0000000 1.0000000 1.0000000 [59,] 1.0000000 1.0000000 1.0000000 [60,] 0.3750000 1.0000000 0.7083333 [61,] 1.0000000 1.0000000 1.0000000 [62,] 1.0000000 1.0000000 1.0000000 [63,] 0.0000000 1.0000000 1.0000000 [64,] 1.0000000 1.0000000 1.0000000 [65,] 1.0000000 1.0000000 1.0000000 [66,] 1.0000000 1.0000000 1.0000000 [67,] 1.0000000 1.0000000 1.0000000 [68,] 1.0000000 1.0000000 1.0000000 [69,] 1.0000000 1.0000000 1.0000000 [70,] 1.0000000 1.0000000 1.0000000 [71,] 1.0000000 0.3333333 1.0000000 [72,] 1.0000000 1.0000000 1.0000000 [73,] 1.0000000 1.0000000 1.0000000 [74,] 1.0000000 1.0000000 1.0000000 [75,] 0.4166667 1.0000000 0.5000000 [76,] 0.2500000 1.0000000 1.0000000 [77,] 1.0000000 0.4545455 1.0000000 [78,] 0.0000000 1.0000000 1.0000000 [79,] 1.0000000 0.0000000 1.0000000 [80,] 1.0000000 1.0000000 0.0000000 > > > > 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) > # Note: If you do not use the installed package, replace this by > # kykladspecreg <- read.table("(path/)kykladspecreg.dat") > > > > cleanEx(); ..nameEx <- "lcomponent" > > ### * lcomponent > > flush(stderr()); flush(stdout()) > > ### Name: lcomponent > ### Title: Largest connectivity component > ### Aliases: lcomponent > ### Keywords: cluster > > ### ** Examples > > data(kykladspecreg) > # Note: If you do not use the installed package, replace this by > # kykladspecreg <- read.table("(path/)kykladspecreg.dat") > j <- jaccard(t(kykladspecreg)) > lcomponent(j) $lc [1] 8 $ne [1] 60 > > > > cleanEx(); ..nameEx <- "nb" > > ### * nb > > flush(stderr()); flush(stdout()) > > ### Name: nb > ### Title: Neighborhood list for Aegean islands > ### Aliases: nb > ### Keywords: datasets > > ### ** Examples > > data(nb) > # Note: If you do not use the installed package, replace this by > # nb <- list() > # for (i in 1:34) > # nb <- c(nb,list(scan(file="(path/)nb.dat", > # skip=i-1,nlines=1))) > > > > cleanEx(); ..nameEx <- "nbtest" > > ### * nbtest > > flush(stderr()); flush(stdout()) > > ### Name: nbtest > ### Title: Test of neighborhood list > ### Aliases: nbtest > ### Keywords: spatial > > ### ** Examples > > data(nb) > # Note: If you do not use the installed package, replace this by > # nb <- list() > # for (i in 1:34) > # nb <- c(nb,list(scan(file="(path/)nb.dat", > # skip=i-1,nlines=1))) > 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) > # Note: If you do not use the installed package, replace this by > # kykladspecreg <- read.table("(path/)kykladspecreg.dat") > j <- jaccard(t(kykladspecreg)) > nn(j,4) [1] 0.5726795 > > > > 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 > > data(kykladspecreg) > # Note: If you do not use the installed package, replace this by > # kykladspecreg <- read.table("(path/)kykladspecreg.dat") > data(nb) > # Note: If you do not use the installed package, replace this by > # nb <- list() > # for (i in 1:34) > # nb <- c(nb,list(scan(file="(path/)nb.dat", > # skip=i-1,nlines=1))) > set.seed(1234) > pop.sim(t(kykladspecreg), nb, times=20, h0c=0.35, teststat="nn", testc=3) Simulation run 1 statistics value= 0.3411064 Simulation run 2 statistics value= 0.3215575 Simulation run 3 statistics value= 0.3327210 Simulation run 4 statistics value= 0.3312433 Simulation run 5 statistics value= 0.3323695 Simulation run 6 statistics value= 0.3428508 Simulation run 7 statistics value= 0.3612398 Simulation run 8 statistics value= 0.3653844 Simulation run 9 statistics value= 0.3240324 Simulation run 10 statistics value= 0.3271095 Simulation run 11 statistics value= 0.3352148 Simulation run 12 statistics value= 0.3113246 Simulation run 13 statistics value= 0.3486394 Simulation run 14 statistics value= 0.3289710 Simulation run 15 statistics value= 0.3276323 Simulation run 16 statistics value= 0.3207483 Simulation run 17 statistics value= 0.3272729 Simulation run 18 statistics value= 0.3446936 Simulation run 19 statistics value= 0.3414531 Simulation run 20 statistics value= 0.3127668 Data value: 0.271529 $results [1] 0.3411064 0.3215575 0.3327210 0.3312433 0.3323695 0.3428508 0.3612398 [8] 0.3653844 0.3240324 0.3271095 0.3352148 0.3113246 0.3486394 0.3289710 [15] 0.3276323 0.3207483 0.3272729 0.3446936 0.3414531 0.3127668 $p.above [1] 1 $p.below [1] 0.04761905 $datac [1] 0.271529 $testc [1] 3 > > > > cleanEx(); ..nameEx <- "prabclust" > > ### * prabclust > > flush(stderr()); flush(stdout()) > > ### Name: prabclust > ### Title: Clustering of species ranges from presence-absence matrices > ### Aliases: prabclust print.prabclust > ### Keywords: cluster spatial > > ### ** Examples > > data(kykladspecreg) > # Note: If you do not use the installed package, replace this by > # kykladspecreg <- read.table("(path/)kykladspecreg.dat") > data(nb) > # Note: If you do not use the installed package, replace this by > # nb <- list() > # for (i in 1:34) > # nb <- c(nb,list(scan(file="(path/)nb.dat", > # skip=i-1,nlines=1))) > set.seed(1234) > x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb) > # If you want to use your own ASCII data files, use > # x <- prabinit(file="path/prabmatrixfile", > # neighborhood="path/neighborhoodfile") > print(prabclust(x)) Loading required package: mva Warning: package 'mva' has been merged into 'stats' Loading required package: MASS Loading required package: mva Warning: package 'mva' has been merged into 'stats' [1] 1460.070 [1] 1460.747 * Clustered presence-absence matrix * Clustered: 4 -dim. MDS result from method classical Noise-detector NNclean has been used with k= 2 NNclean is explained in S. Byers and A. E. Raftery, JASA 95 (1998), 781-794 A Normal mixture model with noise component (mclust) has been used. Mixture component memberships: [1] 8 4 5 3 3 5 6 8 2 1 3 6 8 2 4 6 6 5 8 8 8 5 2 4 2 7 6 7 1 4 1 7 8 6 4 1 8 4 [39] 1 5 4 3 1 1 7 8 4 3 3 4 2 2 1 2 7 1 2 2 3 5 2 4 7 2 3 1 3 1 3 3 1 4 2 2 5 5 [77] 3 7 1 6 Clustering (N denotes noise or one-point components): [1] "N" "4" "5" "3" "3" "5" "6" "N" "2" "1" "3" "6" "N" "2" "4" "6" "6" "5" "N" [20] "N" "N" "5" "2" "4" "2" "7" "6" "7" "1" "4" "1" "7" "N" "6" "4" "1" "N" "4" [39] "1" "5" "4" "3" "1" "1" "7" "N" "4" "3" "3" "4" "2" "2" "1" "2" "7" "1" "2" [58] "2" "3" "5" "2" "4" "7" "2" "3" "1" "3" "1" "3" "3" "1" "4" "2" "2" "5" "5" [77] "3" "7" "1" "6" > > > > cleanEx(); ..nameEx <- "prabinit" > > ### * prabinit > > flush(stderr()); flush(stdout()) > > ### Name: prabinit > ### Title: Presence-absence 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) > # Note: If you do not use the installed package, replace this by > # kykladspecreg <- read.table("(path/)kykladspecreg.dat") > data(nb) > # Note: If you do not use the installed package, replace this by > # nb <- list() > # for (i in 1:34) > # nb <- c(nb,list(scan(file="(path/)nb.dat", > # skip=i-1,nlines=1))) > 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 > ### Keywords: cluster spatial > > ### ** Examples > > data(kykladspecreg) > # Note: If you do not use the installed package, replace this by > # kykladspecreg <- read.table("(path/)kykladspecreg.dat") > data(nb) > # Note: If you do not use the installed package, replace this by > # nb <- list() > # for (i in 1:34) > # nb <- c(nb,list(scan(file="(path/)nb.dat", > # skip=i-1,nlines=1))) > set.seed(1234) > x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb) > # If you want to use your own ASCII data files, use > # x <- prabinit(file="path/prabmatrixfile", > # neighborhood="path/neighborhoodfile") > prabtest(x, times=100, pd=0.35) Simulation run 1 statistics value= 0.5082451 Simulation run 2 statistics value= 0.4583253 Simulation run 3 statistics value= 0.4407105 Simulation run 4 statistics value= 0.4422233 Simulation run 5 statistics value= 0.5477648 Simulation run 6 statistics value= 0.5036965 Simulation run 7 statistics value= 0.4857275 Simulation run 8 statistics value= 0.5188247 Simulation run 9 statistics value= 0.4318439 Simulation run 10 statistics value= 0.4705234 Simulation run 11 statistics value= 0.5024472 Simulation run 12 statistics value= 0.5326384 Simulation run 13 statistics value= 0.5042554 Simulation run 14 statistics value= 0.4416565 Simulation run 15 statistics value= 0.5384527 Simulation run 16 statistics value= 0.4552227 Simulation run 17 statistics value= 0.5855886 Simulation run 18 statistics value= 0.6080748 Simulation run 19 statistics value= 0.4730674 Simulation run 20 statistics value= 0.5075364 Simulation run 21 statistics value= 0.4767544 Simulation run 22 statistics value= 0.465216 Simulation run 23 statistics value= 0.4790903 Simulation run 24 statistics value= 0.5676513 Simulation run 25 statistics value= 0.4816648 Simulation run 26 statistics value= 0.5100886 Simulation run 27 statistics value= 0.4566734 Simulation run 28 statistics value= 0.5890561 Simulation run 29 statistics value= 0.598125 Simulation run 30 statistics value= 0.4147987 Simulation run 31 statistics value= 0.4853367 Simulation run 32 statistics value= 0.4530979 Simulation run 33 statistics value= 0.4809851 Simulation run 34 statistics value= 0.4435944 Simulation run 35 statistics value= 0.5830108 Simulation run 36 statistics value= 0.5868847 Simulation run 37 statistics value= 0.4704585 Simulation run 38 statistics value= 0.6751904 Simulation run 39 statistics value= 0.4717389 Simulation run 40 statistics value= 0.4709758 Simulation run 41 statistics value= 0.481888 Simulation run 42 statistics value= 0.5438561 Simulation run 43 statistics value= 0.443477 Simulation run 44 statistics value= 0.4538485 Simulation run 45 statistics value= 0.6332742 Simulation run 46 statistics value= 0.4969168 Simulation run 47 statistics value= 0.5108473 Simulation run 48 statistics value= 0.4456959 Simulation run 49 statistics value= 0.4797755 Simulation run 50 statistics value= 0.5139444 Simulation run 51 statistics value= 0.4855973 Simulation run 52 statistics value= 0.4714731 Simulation run 53 statistics value= 0.6090841 Simulation run 54 statistics value= 0.4558777 Simulation run 55 statistics value= 0.4375941 Simulation run 56 statistics value= 0.4118111 Simulation run 57 statistics value= 0.4274849 Simulation run 58 statistics value= 0.4316389 Simulation run 59 statistics value= 0.5891565 Simulation run 60 statistics value= 0.4388004 Simulation run 61 statistics value= 0.4885663 Simulation run 62 statistics value= 0.4592186 Simulation run 63 statistics value= 0.4857339 Simulation run 64 statistics value= 0.4884962 Simulation run 65 statistics value= 0.4498898 Simulation run 66 statistics value= 0.5207072 Simulation run 67 statistics value= 0.4423902 Simulation run 68 statistics value= 0.4392979 Simulation run 69 statistics value= 0.4865507 Simulation run 70 statistics value= 0.4973701 Simulation run 71 statistics value= 0.4197669 Simulation run 72 statistics value= 0.4490222 Simulation run 73 statistics value= 0.5218745 Simulation run 74 statistics value= 0.4624398 Simulation run 75 statistics value= 0.4351112 Simulation run 76 statistics value= 0.4605416 Simulation run 77 statistics value= 0.6166526 Simulation run 78 statistics value= 0.5370947 Simulation run 79 statistics value= 0.4621636 Simulation run 80 statistics value= 0.5188707 Simulation run 81 statistics value= 0.56458 Simulation run 82 statistics value= 0.5242099 Simulation run 83 statistics value= 0.4947542 Simulation run 84 statistics value= 0.5436907 Simulation run 85 statistics value= 0.5186871 Simulation run 86 statistics value= 0.4734551 Simulation run 87 statistics value= 0.4464198 Simulation run 88 statistics value= 0.447379 Simulation run 89 statistics value= 0.5325688 Simulation run 90 statistics value= 0.4552645 Simulation run 91 statistics value= 0.4835952 Simulation run 92 statistics value= 0.4493236 Simulation run 93 statistics value= 0.4883008 Simulation run 94 statistics value= 0.5071422 Simulation run 95 statistics value= 0.4442326 Simulation run 96 statistics value= 0.4406416 Simulation run 97 statistics value= 0.4833874 Simulation run 98 statistics value= 0.5618164 Simulation run 99 statistics value= 0.5520812 Simulation run 100 statistics value= 0.5555809 Data value: 0.4080257 $results [1] 0.5082451 0.4583253 0.4407105 0.4422233 0.5477648 0.5036965 0.4857275 [8] 0.5188247 0.4318439 0.4705234 0.5024472 0.5326384 0.5042554 0.4416565 [15] 0.5384527 0.4552227 0.5855886 0.6080748 0.4730674 0.5075364 0.4767544 [22] 0.4652160 0.4790903 0.5676513 0.4816648 0.5100886 0.4566734 0.5890561 [29] 0.5981250 0.4147987 0.4853367 0.4530979 0.4809851 0.4435944 0.5830108 [36] 0.5868847 0.4704585 0.6751904 0.4717389 0.4709758 0.4818880 0.5438561 [43] 0.4434770 0.4538485 0.6332742 0.4969168 0.5108473 0.4456959 0.4797755 [50] 0.5139444 0.4855973 0.4714731 0.6090841 0.4558777 0.4375941 0.4118111 [57] 0.4274849 0.4316389 0.5891565 0.4388004 0.4885663 0.4592186 0.4857339 [64] 0.4884962 0.4498898 0.5207072 0.4423902 0.4392979 0.4865507 0.4973701 [71] 0.4197669 0.4490222 0.5218745 0.4624398 0.4351112 0.4605416 0.6166526 [78] 0.5370947 0.4621636 0.5188707 0.5645799 0.5242099 0.4947542 0.5436907 [85] 0.5186871 0.4734551 0.4464198 0.4473790 0.5325688 0.4552645 0.4835952 [92] 0.4493236 0.4883008 0.5071422 0.4442326 0.4406416 0.4833874 0.5618164 [99] 0.5520812 0.5555809 $datac [1] 0.4080257 $p.value [1] 0.00990099 $tuning [1] 0.25 $pd [1] 0.35 $reg [1] NA $teststat [1] "distratio" $distance [1] "kulczynski" $times [1] 100 $pdfnb [1] FALSE attr(,"class") [1] "prabtest" > # These settings are chosen to make the example execution > # a bit faster; usually you will use prabtest(kprab). > > > > 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) > # Note: If you do not use the installed package, replace this by > # nb <- list() > # for (i in 1:34) > # nb <- c(nb,list(scan(file="(path/)nb.dat", > # skip=i-1,nlines=1))) > set.seed(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 <- "summary.prabtest" > > ### * summary.prabtest > > flush(stderr()); flush(stdout()) > > ### Name: summary.prabtest > ### Title: Print and summary method for prabtest > ### Aliases: summary.prabtest print.summary.prabtest > ### Keywords: cluster spatial > > ### ** Examples > > data(kykladspecreg) > # Note: If you do not use the installed package, replace this by > # kykladspecreg <- read.table("(path/)kykladspecreg.dat") > data(nb) > # Note: If you do not use the installed package, replace this by > # nb <- list() > # for (i in 1:34) > # nb <- c(nb,list(scan(file="(path/)nb.dat", > # skip=i-1,nlines=1))) > set.seed(5678) > kprab <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb) > kpt <- prabtest(kprab, times=50, pd=0.35, teststat="lcomponent") Simulation run 1 statistics value= 16 Simulation run 2 statistics value= 21 Simulation run 3 statistics value= 15 Simulation run 4 statistics value= 13 Simulation run 5 statistics value= 10 Simulation run 6 statistics value= 10 Simulation run 7 statistics value= 13 Simulation run 8 statistics value= 59 Simulation run 9 statistics value= 15 Simulation run 10 statistics value= 9 Simulation run 11 statistics value= 9 Simulation run 12 statistics value= 8 Simulation run 13 statistics value= 62 Simulation run 14 statistics value= 18 Simulation run 15 statistics value= 11 Simulation run 16 statistics value= 11 Simulation run 17 statistics value= 14 Simulation run 18 statistics value= 18 Simulation run 19 statistics value= 54 Simulation run 20 statistics value= 24 Simulation run 21 statistics value= 14 Simulation run 22 statistics value= 12 Simulation run 23 statistics value= 15 Simulation run 24 statistics value= 12 Simulation run 25 statistics value= 11 Simulation run 26 statistics value= 17 Simulation run 27 statistics value= 11 Simulation run 28 statistics value= 17 Simulation run 29 statistics value= 11 Simulation run 30 statistics value= 16 Simulation run 31 statistics value= 57 Simulation run 32 statistics value= 67 Simulation run 33 statistics value= 10 Simulation run 34 statistics value= 28 Simulation run 35 statistics value= 18 Simulation run 36 statistics value= 8 Simulation run 37 statistics value= 17 Simulation run 38 statistics value= 15 Simulation run 39 statistics value= 8 Simulation run 40 statistics value= 10 Simulation run 41 statistics value= 16 Simulation run 42 statistics value= 7 Simulation run 43 statistics value= 10 Simulation run 44 statistics value= 67 Simulation run 45 statistics value= 8 Simulation run 46 statistics value= 11 Simulation run 47 statistics value= 11 Simulation run 48 statistics value= 12 Simulation run 49 statistics value= 16 Simulation run 50 statistics value= 11 Data value: 8 > # The parameters times and > # pd are only given to fasten the example execution. > summary(kpt) * Monte Carlo test for presence-absence data * Test statistics: lcomponent , Tuning constant= 60 Distance: kulczynski Simulation runs: 50 Disjunction parameter: 0.35 Neighbor-based correction of region probabilities was not used. Statistics value for original data: 8 Mean for null data: 19.06 , range: 7 67 p= 0.1176471 > > > > ### *