R Under development (unstable) (2024-09-23 r87189 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 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 an HTML browser interface to help. Type 'q()' to quit R. > library(prabclus) Loading required package: MASS Loading required package: mclust Package 'mclust' version 6.1.1 Type 'citation("mclust")' for citing this R package in publications. > > # example(prabclust) > 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)) * 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] 0 1 0 3 3 7 6 0 8 0 3 0 0 4 1 6 6 7 0 0 0 7 4 1 4 0 6 5 2 1 2 5 0 6 1 0 0 1 [39] 0 7 1 3 2 2 5 0 1 2 3 1 8 0 0 4 5 2 8 4 0 0 4 1 5 8 0 2 3 0 0 2 0 1 8 4 0 7 [77] 3 5 0 6 Clustering (N denotes noise or one-point components): [1] "N" "1" "N" "3" "3" "7" "6" "N" "8" "N" "3" "N" "N" "4" "1" "6" "6" "7" "N" [20] "N" "N" "7" "4" "1" "4" "N" "6" "5" "2" "1" "2" "5" "N" "6" "1" "N" "N" "1" [39] "N" "7" "1" "3" "2" "2" "5" "N" "1" "2" "3" "1" "8" "N" "N" "4" "5" "2" "8" [58] "4" "N" "N" "4" "1" "5" "8" "N" "2" "3" "N" "N" "2" "N" "1" "8" "4" "N" "7" [77] "3" "5" "N" "6" > > # 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)) * 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] 2 2 1 1 1 1 1 2 2 1 1 1 2 2 2 1 2 1 2 1 2 2 1 2 1 2 2 2 1 1 1 1 2 1 2 3 0 3 [39] 0 0 0 3 3 3 3 0 3 3 3 3 Clustering (N denotes noise or one-point components): [1] "2" "2" "1" "1" "1" "1" "1" "2" "2" "1" "1" "1" "2" "2" "2" "1" "2" "1" "2" [20] "1" "2" "2" "1" "2" "1" "2" "2" "2" "1" "1" "1" "1" "2" "1" "2" "3" "N" "3" [39] "N" "N" "N" "3" "3" "3" "3" "N" "3" "3" "3" "3" > > # 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)) initial value 28.163173 iter 5 value 20.897590 iter 10 value 19.154545 iter 15 value 18.814679 iter 20 value 18.493361 iter 20 value 18.475223 final value 18.228921 converged * Clustered presence-absence matrix * Clustered: 3 -dim. MDS result from method kruskal 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] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 [39] 1 0 0 1 1 1 1 1 1 0 1 0 Clustering (N denotes noise or one-point components): [1] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" [20] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "N" "1" "1" "1" "1" "1" "1" "1" "1" [39] "1" "N" "N" "1" "1" "1" "1" "1" "1" "N" "1" "N" > > # example(crmatrix) > options(digits=3) > data(kykladspecreg) > data(nb) > set.seed(1234) > x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb) > xc <- prabclust(x) > > crmatrix(x,xc) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [1,] 0 0 0 1 0 0 0 0 0 0 0 0 0 [2,] 0 0 0 0 0 0 0 0 0 1 2 2 2 [3,] 0 0 0 0 0 0 0 1 0 1 0 0 0 [4,] 0 0 0 0 1 6 2 0 1 1 0 0 0 [5,] 0 0 0 0 0 0 0 0 0 0 0 0 0 [6,] 0 0 0 0 0 0 0 0 0 0 0 0 0 [7,] 0 0 0 0 0 0 0 0 0 0 0 0 0 [8,] 5 4 1 0 0 0 0 0 2 0 0 0 0 [9,] 10 10 3 3 5 5 5 9 7 10 3 1 5 [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [1,] 0 0 0 0 0 0 0 0 0 0 0 0 [2,] 1 1 0 1 0 0 0 0 0 0 2 0 [3,] 1 2 1 1 0 0 5 1 2 5 2 2 [4,] 0 0 0 0 0 1 0 0 0 0 0 0 [5,] 0 0 0 0 0 0 0 0 0 0 0 0 [6,] 0 0 0 0 0 0 0 0 0 0 0 0 [7,] 0 0 0 0 0 0 0 0 0 0 0 0 [8,] 0 0 0 0 0 0 0 0 0 0 0 0 [9,] 9 8 7 6 6 9 11 3 5 9 6 8 [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [1,] 0 0 7 8 3 0 0 0 0 [2,] 2 0 0 0 0 0 0 0 0 [3,] 2 0 0 0 0 0 0 0 0 [4,] 0 0 0 0 0 0 0 0 0 [5,] 0 6 0 0 0 0 0 0 0 [6,] 0 0 0 0 3 4 2 6 0 [7,] 0 5 3 5 0 0 0 0 0 [8,] 0 0 0 0 0 0 0 0 0 [9,] 5 8 6 8 5 6 2 7 4 > crmatrix(x,xc, percentages=TRUE) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [1,] 0.0 0.0 0.00 0.0909 0.000 0.000 0.000 0.000 0.000 0.000 0.00 0.00 [2,] 0.0 0.0 0.00 0.0000 0.000 0.000 0.000 0.000 0.000 0.125 0.25 0.25 [3,] 0.0 0.0 0.00 0.0000 0.000 0.000 0.000 0.143 0.000 0.143 0.00 0.00 [4,] 0.0 0.0 0.00 0.0000 0.143 0.857 0.286 0.000 0.143 0.143 0.00 0.00 [5,] 0.0 0.0 0.00 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.00 0.00 [6,] 0.0 0.0 0.00 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.00 0.00 [7,] 0.0 0.0 0.00 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.00 0.00 [8,] 1.0 0.8 0.20 0.0000 0.000 0.000 0.000 0.000 0.400 0.000 0.00 0.00 [9,] 0.4 0.4 0.12 0.1200 0.200 0.200 0.200 0.360 0.280 0.400 0.12 0.04 [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [1,] 0.00 0.000 0.000 0.000 0.000 0.00 0.000 0.000 0.000 0.000 0.000 0.000 [2,] 0.25 0.125 0.125 0.000 0.125 0.00 0.000 0.000 0.000 0.000 0.000 0.250 [3,] 0.00 0.143 0.286 0.143 0.143 0.00 0.000 0.714 0.143 0.286 0.714 0.286 [4,] 0.00 0.000 0.000 0.000 0.000 0.00 0.143 0.000 0.000 0.000 0.000 0.000 [5,] 0.00 0.000 0.000 0.000 0.000 0.00 0.000 0.000 0.000 0.000 0.000 0.000 [6,] 0.00 0.000 0.000 0.000 0.000 0.00 0.000 0.000 0.000 0.000 0.000 0.000 [7,] 0.00 0.000 0.000 0.000 0.000 0.00 0.000 0.000 0.000 0.000 0.000 0.000 [8,] 0.00 0.000 0.000 0.000 0.000 0.00 0.000 0.000 0.000 0.000 0.000 0.000 [9,] 0.20 0.360 0.320 0.280 0.240 0.24 0.360 0.440 0.120 0.200 0.360 0.240 [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [1,] 0.000 0.000 0.00 0.636 0.727 0.273 0.000 0.000 0.00 0.00 [2,] 0.000 0.250 0.00 0.000 0.000 0.000 0.000 0.000 0.00 0.00 [3,] 0.286 0.286 0.00 0.000 0.000 0.000 0.000 0.000 0.00 0.00 [4,] 0.000 0.000 0.00 0.000 0.000 0.000 0.000 0.000 0.00 0.00 [5,] 0.000 0.000 1.00 0.000 0.000 0.000 0.000 0.000 0.00 0.00 [6,] 0.000 0.000 0.00 0.000 0.000 0.500 0.667 0.333 1.00 0.00 [7,] 0.000 0.000 1.00 0.600 1.000 0.000 0.000 0.000 0.00 0.00 [8,] 0.000 0.000 0.00 0.000 0.000 0.000 0.000 0.000 0.00 0.00 [9,] 0.320 0.200 0.32 0.240 0.320 0.200 0.240 0.080 0.28 0.16 > > > # example(lociplots) > 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) $locfreq [1] 0.13333 0.26667 0.13333 0.20000 0.13333 0.13333 0.40000 0.20000 0.13333 [10] 0.13333 0.06667 0.00000 0.00000 0.20000 0.00000 0.00000 0.06667 0.00000 [19] 0.00000 0.00000 0.06667 0.00000 0.06667 0.00000 0.00000 0.00000 0.13333 [28] 0.00000 0.00000 0.26667 0.26667 0.33333 0.06667 0.13333 0.13333 0.20000 [37] 0.13333 0.13333 0.20000 0.00000 0.00000 0.06667 0.00000 0.06667 0.06667 [46] 0.06667 0.06667 0.33333 0.20000 0.13333 $locfreqmin [1] 0 0 0 0 $locfreqmax [1] 0.33333 0.40000 0.26667 0.06667 $locfreqmean [1] 0.14167 0.14510 0.10769 0.02778 > > > # Results: > > # R version 3.1.2 (2014-10-31) -- "Pumpkin Helmet" > # Copyright (C) 2014 The R Foundation for Statistical Computing > # Platform: x86_64-pc-linux-gnu (64-bit) > # > # 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 an HTML browser interface to help. > # Type 'q()' to quit R. > # > # > library(prabclus) > # Loading required package: MASS > # Loading required package: mclust > # Package 'mclust' version 4.4 > # Type 'citation("mclust")' for citing this R package in publications. > # > > # > # example(prabclust) > # > 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)) > # * 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] 0 1 0 2 2 8 6 0 7 0 2 0 0 4 1 6 6 8 4 0 0 0 4 1 4 0 6 5 3 1 3 5 0 6 1 0 0 1 > # [39] 0 8 1 2 3 3 5 0 1 3 2 1 7 0 0 4 5 3 7 4 0 0 4 1 5 7 0 3 2 0 2 3 0 1 7 4 0 0 > # [77] 2 5 0 6 > # > # Clustering (N denotes noise or one-point components): > # [1] "N" "1" "N" "2" "2" "8" "6" "N" "7" "N" "2" "N" "N" "4" "1" "6" "6" "8" "4" > # [20] "N" "N" "N" "4" "1" "4" "N" "6" "5" "3" "1" "3" "5" "N" "6" "1" "N" "N" "1" > # [39] "N" "8" "1" "2" "3" "3" "5" "N" "1" "3" "2" "1" "7" "N" "N" "4" "5" "3" "7" > # [58] "4" "N" "N" "4" "1" "5" "7" "N" "3" "2" "N" "2" "3" "N" "1" "7" "4" "N" "N" > # [77] "2" "5" "N" "6" > # > > # > # 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)) > # * 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] 2 2 1 1 1 1 1 2 2 1 1 1 2 2 2 1 2 1 2 1 2 2 1 2 1 2 2 2 1 1 1 1 2 1 2 3 0 3 > # [39] 0 0 0 3 3 3 3 0 3 3 3 3 > # > # Clustering (N denotes noise or one-point components): > # [1] "2" "2" "1" "1" "1" "1" "1" "2" "2" "1" "1" "1" "2" "2" "2" "1" "2" "1" "2" > # [20] "1" "2" "2" "1" "2" "1" "2" "2" "2" "1" "1" "1" "1" "2" "1" "2" "3" "N" "3" > # [39] "N" "N" "N" "3" "3" "3" "3" "N" "3" "3" "3" "3" > # > > # > # 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)) > # initial value 28.163173 > # iter 5 value 20.897590 > # iter 10 value 19.154545 > # iter 15 value 18.814679 > # iter 20 value 18.493361 > # iter 20 value 18.475223 > # final value 18.228921 > # converged > # * Clustered presence-absence matrix * > # > # Clustered: 3 -dim. MDS result from method kruskal > # > # 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] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 > # [39] 1 0 0 1 1 1 1 1 1 0 1 0 > # > # Clustering (N denotes noise or one-point components): > # [1] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" > # [20] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "N" "1" "1" "1" "1" "1" "1" "1" "1" > # [39] "1" "N" "N" "1" "1" "1" "1" "1" "1" "N" "1" "N" > # > > # > # example(crmatrix) > # > options(digits=3) > # > data(kykladspecreg) > # > data(nb) > # > set.seed(1234) > # > x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb) > # > xc <- prabclust(x) > # > > # > crmatrix(x,xc) > # [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] > # [1,] 0 0 0 1 0 0 0 0 0 0 0 0 0 > # [2,] 0 0 0 0 0 0 0 1 0 1 0 0 0 > # [3,] 0 0 0 0 0 0 0 0 0 1 2 2 2 > # [4,] 1 0 0 0 2 7 3 0 1 2 0 0 1 > # [5,] 0 0 0 0 0 0 0 0 0 0 0 0 0 > # [6,] 0 0 0 0 0 0 0 0 0 0 0 0 0 > # [7,] 5 4 1 0 0 0 0 0 2 0 0 0 0 > # [8,] 0 0 0 0 0 0 0 0 0 0 0 0 0 > # [9,] 9 10 3 3 4 4 4 9 7 9 3 1 4 > # [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] > # [1,] 0 0 0 0 0 0 0 0 0 0 0 0 > # [2,] 2 3 2 2 0 1 6 1 3 6 2 2 > # [3,] 1 1 0 1 0 0 0 0 0 0 2 0 > # [4,] 0 0 0 0 0 1 0 0 0 1 0 0 > # [5,] 0 0 0 0 0 0 0 0 0 0 0 0 > # [6,] 0 0 0 0 0 0 0 0 0 0 0 0 > # [7,] 0 0 0 0 0 0 0 0 0 0 0 0 > # [8,] 0 0 0 0 0 0 0 0 0 0 0 0 > # [9,] 8 7 6 5 6 8 10 3 4 7 6 8 > # [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] > # [1,] 0 0 7 8 3 0 0 0 0 > # [2,] 2 0 0 0 0 0 0 0 0 > # [3,] 2 0 0 0 0 0 0 0 0 > # [4,] 0 0 0 0 0 0 0 0 0 > # [5,] 0 6 0 0 0 0 0 0 0 > # [6,] 0 0 0 0 3 4 2 6 0 > # [7,] 0 0 0 0 0 0 0 0 0 > # [8,] 0 3 3 3 0 0 0 0 0 > # [9,] 5 10 6 10 5 6 2 7 4 > # > crmatrix(x,xc, percentages=TRUE) > # [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] > # [1,] 0.000 0.0 0.00 0.0909 0.00 0.000 0.000 0.000 0.000 0.000 0.00 0.00 > # [2,] 0.000 0.0 0.00 0.0000 0.00 0.000 0.000 0.125 0.000 0.125 0.00 0.00 > # [3,] 0.000 0.0 0.00 0.0000 0.00 0.000 0.000 0.000 0.000 0.125 0.25 0.25 > # [4,] 0.125 0.0 0.00 0.0000 0.25 0.875 0.375 0.000 0.125 0.250 0.00 0.00 > # [5,] 0.000 0.0 0.00 0.0000 0.00 0.000 0.000 0.000 0.000 0.000 0.00 0.00 > # [6,] 0.000 0.0 0.00 0.0000 0.00 0.000 0.000 0.000 0.000 0.000 0.00 0.00 > # [7,] 1.000 0.8 0.20 0.0000 0.00 0.000 0.000 0.000 0.400 0.000 0.00 0.00 > # [8,] 0.000 0.0 0.00 0.0000 0.00 0.000 0.000 0.000 0.000 0.000 0.00 0.00 > # [9,] 0.360 0.4 0.12 0.1200 0.16 0.160 0.160 0.360 0.280 0.360 0.12 0.04 > # [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] > # [1,] 0.000 0.000 0.000 0.00 0.000 0.00 0.000 0.00 0.000 0.000 0.000 0.00 > # [2,] 0.000 0.250 0.375 0.25 0.250 0.00 0.125 0.75 0.125 0.375 0.750 0.25 > # [3,] 0.250 0.125 0.125 0.00 0.125 0.00 0.000 0.00 0.000 0.000 0.000 0.25 > # [4,] 0.125 0.000 0.000 0.00 0.000 0.00 0.125 0.00 0.000 0.000 0.125 0.00 > # [5,] 0.000 0.000 0.000 0.00 0.000 0.00 0.000 0.00 0.000 0.000 0.000 0.00 > # [6,] 0.000 0.000 0.000 0.00 0.000 0.00 0.000 0.00 0.000 0.000 0.000 0.00 > # [7,] 0.000 0.000 0.000 0.00 0.000 0.00 0.000 0.00 0.000 0.000 0.000 0.00 > # [8,] 0.000 0.000 0.000 0.00 0.000 0.00 0.000 0.00 0.000 0.000 0.000 0.00 > # [9,] 0.160 0.320 0.280 0.24 0.200 0.24 0.320 0.40 0.120 0.160 0.280 0.24 > # [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] > # [1,] 0.00 0.00 0.0 0.636 0.727 0.273 0.000 0.000 0.00 0.00 > # [2,] 0.25 0.25 0.0 0.000 0.000 0.000 0.000 0.000 0.00 0.00 > # [3,] 0.00 0.25 0.0 0.000 0.000 0.000 0.000 0.000 0.00 0.00 > # [4,] 0.00 0.00 0.0 0.000 0.000 0.000 0.000 0.000 0.00 0.00 > # [5,] 0.00 0.00 1.0 0.000 0.000 0.000 0.000 0.000 0.00 0.00 > # [6,] 0.00 0.00 0.0 0.000 0.000 0.500 0.667 0.333 1.00 0.00 > # [7,] 0.00 0.00 0.0 0.000 0.000 0.000 0.000 0.000 0.00 0.00 > # [8,] 0.00 0.00 1.0 1.000 1.000 0.000 0.000 0.000 0.00 0.00 > # [9,] 0.32 0.20 0.4 0.240 0.400 0.200 0.240 0.080 0.28 0.16 > # > > # > > # > # example(lociplots) > # > 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) > # $locfreq > # [1] 0.4737 0.3684 0.4737 0.5263 0.3684 0.4211 0.3684 0.5263 0.5263 0.5263 > # [11] 0.6842 0.4211 0.4211 0.5263 0.4211 0.2632 0.4211 0.4737 0.5263 0.4211 > # [21] 0.4737 0.4211 0.5263 0.4211 0.6316 0.5263 0.4211 0.4737 0.4211 0.3684 > # [31] 0.5263 0.4737 0.4737 0.4211 0.4737 0.5789 0.5263 0.5263 0.4211 0.3684 > # [41] 0.2632 0.4211 0.3684 0.4737 0.5263 0.4211 0.4211 0.2632 0.5263 0.5263 > # > # $locfreqmin > # [1] 0.2632 0.3684 0.2632 0.4211 > # > # $locfreqmax > # [1] 0.5263 0.5263 0.5789 0.6842 > # > # $locfreqmean > # [1] 0.3947 0.4520 0.4575 0.5044 > # > # > > # > proc.time() > # user system elapsed > # 1.708 0.020 1.729 > > proc.time() user system elapsed 2.10 0.25 2.34