R Under development (unstable) (2023-10-23 r85401 ucrt) -- "Unsuffered Consequences" Copyright (C) 2023 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 6.0.0 Type 'citation("mclust")' for citing this R package in publications. > options(digits=4) > > data(kykladspecreg) > data(nb) > set.seed(1234) > x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb) > > p1 <- prabtest(x, times=3, pd=0.35, ignore.richness=TRUE) Simulation run 1 statistics value= 0.5811 Simulation run 2 statistics value= 0.4761 Simulation run 3 statistics value= 0.5022 Data value: 0.408 > p2 <- prabtest(x, times=3, pd=0.35, teststat="lcomponent") Simulation run 1 statistics value= 9 Simulation run 2 statistics value= 19 Simulation run 3 statistics value= 43 Data value: 8 > p3 <- prabtest(x, times=3, pd=0.35, teststat="isovertice") Simulation run 1 statistics value= 9 Simulation run 2 statistics value= 9 Simulation run 3 statistics value= 23 Data value: 15 > p4 <- prabtest(x, times=3, pd=0.35, teststat="nn", sf.sim=TRUE) Simulation run 1 statistics value= 0.3598 Simulation run 2 statistics value= 0.3635 Simulation run 3 statistics value= 0.3441 Data value: 0.3192 > p5 <- prabtest(x, times=3, pd=0.35, teststat="inclusions") Simulation run 1 statistics value= 470 Simulation run 2 statistics value= 547 Simulation run 3 statistics value= 365 Data value: 602 > summary(p1) * Parametric bootstrap test for presence-absence data * Test statistics: distratio , Tuning constant= 0.25 Distance: kulczynski Simulation runs: 3 Disjunction parameter for presence-absence pattern: 0.35 Rows (regions) richness has been ignored by the null model. Statistics value for original data: 0.408 Mean for null data: 0.5198 , range: 0.4761 0.5811 p= 0.25 > summary(p2) * Parametric bootstrap test for presence-absence data * Test statistics: lcomponent , Tuning constant= 60 Distance: kulczynski Simulation runs: 3 Disjunction parameter for presence-absence pattern: 0.35 Statistics value for original data: 8 Mean for null data: 23.67 , range: 9 43 p= 0.25 > summary(p3) * Parametric bootstrap test for presence-absence data * Test statistics: isovertice , Tuning constant= 80 Distance: kulczynski Simulation runs: 3 Disjunction parameter for presence-absence pattern: 0.35 Statistics value for original data: 15 Mean for null data: 13.67 , range: 9 23 p= 1 > summary(p4) * Parametric bootstrap test for presence-absence data * Test statistics: nn , Tuning constant= 4 Distance: kulczynski Simulation runs: 3 Disjunction parameter for presence-absence pattern: 0.35 Statistics value for original data: 0.3192 Mean for null data: 0.3558 , range: 0.3441 0.3635 p= 0.25 > summary(p5) * Parametric bootstrap test for presence-absence data * Test statistics: inclusions , Tuning constant= NA Distance: kulczynski Simulation runs: 3 Disjunction parameter for presence-absence pattern: 0.35 Statistics value for original data: 602 Mean for null data: 460.7 , range: 365 547 p= 0.25 > > data(veronica) > vnb <- coord2dist(coordmatrix=veronica.coord[1:50,], cut=20, + file.format="decimal2",neighbors=TRUE) > vei <- prabinit(prabmatrix=veronica[1:50,], + neighborhood=vnb$nblist,nbbetweenregions=FALSE, + distance="jaccard") > print(vei) Presence-absence matrix object with 50 species and 583 regions, including species/individuals neighborhoods and between-species distance matrix of type jaccard . > > library(spdep) Loading required package: spData To access larger datasets in this package, install the spDataLarge package with: `install.packages('spDataLarge', repos='https://nowosad.github.io/drat/', type='source')` Loading required package: sf Linking to GEOS 3.11.2, GDAL 3.7.2, PROJ 9.3.0; sf_use_s2() is TRUE > data(siskiyou) > x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb, + distance="logkulczynski") > build.nblist(x) > a1 <- abundtest(x, times=5, p.nb=0.0465) Simulation run 1 statistics value= 0.2931 Simulation run 2 statistics value= 0.3109 Simulation run 3 statistics value= 0.2936 Simulation run 4 statistics value= 0.3119 Simulation run 5 statistics value= 0.3048 Data value: 0.2789 > a2 <- abundtest(x, times=5, p.nb=0.0465, teststat="groups", + groupvector=siskiyou.groups) Simulation run 1 statistics value= 0.4916 Simulation run 2 statistics value= 0.5779 Simulation run 3 statistics value= 0.5337 Simulation run 4 statistics value= 0.5873 Simulation run 5 statistics value= 0.617 Data value: 0.5864 > # These settings are chosen to make the example execution > # faster; usually you will use abundtest(x). > summary(a1) * Parametric bootstrap test for spatial abundance data * Test statistics: distratio , Tuning constant= 0.25 Distance: logkulczynski Simulation runs: 5 Disjunction parameter for presence-absence pattern: 0.0465 Neighborhood parameter lambda for SAR-model: -0.05515 Statistics value for original data: 0.2789 Mean for null data: 0.3029 , range: 0.2931 0.3119 p= 0.1667 > summary(a2) * Parametric bootstrap test for spatial abundance data * Test statistics: groups , Tuning constant= NA Distance: logkulczynski Simulation runs: 5 Disjunction parameter for presence-absence pattern: 0.0465 Neighborhood parameter lambda for SAR-model: -0.05515 Mean within group distances for original data: 0.5864 Mean of mean within group distances for null data: 0.5615 , range: 0.4916 0.617 p= 0.5 Group 7 statistics value for original data: 0.7897 Group 7 mean for null data: 0.4531 , range: 0.4156 0.4727 p= 0.1667 Group 16 statistics value for original data: 0.2778 Group 16 mean for null data: 0.3438 , range: 0.2231 0.4107 p= 0.8333 Group 21 statistics value for original data: 1 Group 21 mean for null data: 0.6664 , range: 0.3384 1 p= 0.5 Group 24 statistics value for original data: 0.4815 Group 24 mean for null data: 0.3202 , range: 0.2768 0.3746 p= 0.1667 Group 27 statistics value for original data: 0.353 Group 27 mean for null data: 0.6447 , range: 0.3722 1 p= 1 Group 30 statistics value for original data: 0.2257 Group 30 mean for null data: 0.1347 , range: 0.06729 0.1949 p= 0.1667 Group 33 statistics value for original data: 0.7591 Group 33 mean for null data: 0.5463 , range: 0.4448 0.6791 p= 0.1667 Group 41 statistics value for original data: 0.5354 Group 41 mean for null data: 0.4502 , range: 0.3351 0.5298 p= 0.1667 Group 51 statistics value for original data: 0.7529 Group 51 mean for null data: 0.7072 , range: 0.6229 0.859 p= 0.5 Group 61 statistics value for original data: 0.4842 Group 61 mean for null data: 0.6812 , range: 0.4593 1 p= 0.5 Group 66 statistics value for original data: 0.9123 Group 66 mean for null data: 0.9564 , range: 0.8345 1 p= 0.8333 Group 74 statistics value for original data: 0.8442 Group 74 mean for null data: 0.546 , range: 0.3173 1 p= 0.3333 Group 80 statistics value for original data: 0.2595 Group 80 mean for null data: 0.3406 , range: 0.2541 0.3817 p= 0.8333 Group 88 statistics value for original data: 0.7163 Group 88 mean for null data: 0.6213 , range: 0.3689 0.7709 p= 0.5 Group 96 statistics value for original data: 0.4578 Group 96 mean for null data: 0.4455 , range: 0.417 0.4682 p= 0.5 Group 99 statistics value for original data: 0.4293 Group 99 mean for null data: 0.7328 , range: 0.5584 0.8119 p= 1 Group 103 statistics value for original data: 0.4432 Group 103 mean for null data: 0.6986 , range: 0.4646 1 p= 1 Group 107 statistics value for original data: 0.5259 Group 107 mean for null data: 0.808 , range: 0.5826 1 p= 1 Group 114 statistics value for original data: 0.4325 Group 114 mean for null data: 0.4392 , range: 0.3879 0.4892 p= 0.6667 Group 125 statistics value for original data: 0.6856 Group 125 mean for null data: 0.3745 , range: 0.268 0.4779 p= 0.1667 Group 127 statistics value for original data: 0.2832 Group 127 mean for null data: 0.1782 , range: 0.1558 0.2164 p= 0.1667 Group 140 statistics value for original data: 0.5829 Group 140 mean for null data: 0.5161 , range: 0.3774 0.6366 p= 0.3333 > > options(digits=2) > prab.sarestimate(x) $sar [1] TRUE $intercept (Intercept) 1.1 $sigma [1] 1 $regeffects region2 region3 region4 region5 region6 0.000 -0.056 0.517 0.682 0.597 0.834 $speffects species2 species3 species4 species5 species6 species7 0.0000 -1.0986 -0.4055 -0.4055 -0.1746 -0.1746 0.5186 species8 species9 species10 species11 species12 species13 species14 0.1720 -0.3773 2.4585 2.4632 2.3148 0.7966 -0.5523 species15 species16 species17 species18 species19 species20 species21 -0.9997 2.8542 3.1175 -0.1624 -1.2164 0.8256 0.0025 species22 species23 species24 species25 species26 species27 species28 -2.0213 -0.6407 2.6430 4.3446 3.3794 2.7244 2.6713 species29 species30 species31 species32 species33 species34 species35 2.7303 1.5682 1.8833 2.9410 1.7488 2.9594 2.5965 species36 species37 species38 species39 species40 species41 species42 2.7883 1.0393 0.9960 1.4137 1.9796 -1.8561 1.6207 species43 species44 species45 species46 species47 species48 species49 1.7423 2.2793 2.0573 2.3259 1.0857 3.3730 3.2701 species50 species51 species52 species53 species54 species55 species56 2.7610 2.6443 -0.1027 1.9857 2.3219 -0.3492 -1.0423 species57 species58 species59 species60 species61 species62 species63 -1.0423 -1.0423 -0.3492 2.1940 -0.6358 -0.4330 -1.3289 species64 species65 species66 species67 species68 species69 species70 -2.2767 -2.3501 -1.7023 1.3365 1.9931 1.3771 0.7127 species71 species72 species73 species74 species75 species76 species77 2.1431 -1.6155 -0.9223 -1.6155 0.4640 -1.6155 1.0236 species78 species79 species80 species81 species82 species83 species84 -0.5168 -1.6979 -1.6979 -1.0047 -2.5026 1.7620 0.6432 species85 species86 species87 species88 species89 species90 species91 -2.1138 1.5735 1.3492 1.8530 0.3853 2.6159 -0.2037 species92 species93 species94 species95 species96 species97 species98 -0.4353 0.1952 -1.5037 0.5223 -0.3940 -1.7803 -1.0871 species99 species100 species101 species102 species103 species104 species105 -1.7803 -1.8497 0.5645 -0.0720 -0.1926 -0.3926 1.5433 species106 species107 species108 species109 species110 species111 species112 -0.5872 0.3140 3.4881 1.3971 0.4806 3.2341 -0.4859 species113 species114 species115 species116 species117 species118 species119 1.8359 0.2072 -0.7822 1.0575 1.9314 2.2148 -0.5973 species120 species121 species122 species123 species124 species125 species126 0.0959 0.7020 -1.6959 -1.0028 -0.4950 0.0299 0.3509 species127 species128 species129 species130 species131 species132 species133 -0.1855 -0.8416 0.5266 -0.6632 -0.4950 -1.4679 -0.8416 species134 species135 species136 species137 species138 species139 species140 0.9045 3.1571 1.6834 -0.3166 -1.2400 -1.2400 -1.2400 species141 species142 species143 species144 0.7749 1.2858 -0.3237 -1.2400 $lambda lambda -0.078 $size [1] 421 $nbweight [1] 0.7 $lmobject Call: spatialreg::errorsarlm(formula = logabund ~ region + species, data = abundreg, listw = nblistw, method = sarmethod, quiet = quiet, zero.policy = TRUE) Type: error Coefficients: lambda (Intercept) region2 region3 region4 region5 -0.0784 1.0986 -0.0563 0.5168 0.6817 0.5973 region6 species2 species3 species4 species5 species6 0.8345 -1.0986 -0.4055 -0.4055 -0.1746 -0.1746 species7 species8 species9 species10 species11 species12 0.5186 0.1720 -0.3773 2.4585 2.4632 2.3148 species13 species14 species15 species16 species17 species18 0.7966 -0.5523 -0.9997 2.8542 3.1175 -0.1624 species19 species20 species21 species22 species23 species24 -1.2164 0.8256 0.0025 -2.0213 -0.6407 2.6430 species25 species26 species27 species28 species29 species30 4.3446 3.3794 2.7244 2.6713 2.7303 1.5682 species31 species32 species33 species34 species35 species36 1.8833 2.9410 1.7488 2.9594 2.5965 2.7883 species37 species38 species39 species40 species41 species42 1.0393 0.9960 1.4137 1.9796 -1.8561 1.6207 species43 species44 species45 species46 species47 species48 1.7423 2.2793 2.0573 2.3259 1.0857 3.3730 species49 species50 species51 species52 species53 species54 3.2701 2.7610 2.6443 -0.1027 1.9857 2.3219 species55 species56 species57 species58 species59 species60 -0.3492 -1.0423 -1.0423 -1.0423 -0.3492 2.1940 species61 species62 species63 species64 species65 species66 -0.6358 -0.4330 -1.3289 -2.2767 -2.3501 -1.7023 species67 species68 species69 species70 species71 species72 1.3365 1.9931 1.3771 0.7127 2.1431 -1.6155 species73 species74 species75 species76 species77 species78 -0.9223 -1.6155 0.4640 -1.6155 1.0236 -0.5168 species79 species80 species81 species82 species83 species84 -1.6979 -1.6979 -1.0047 -2.5026 1.7620 0.6432 species85 species86 species87 species88 species89 species90 -2.1138 1.5735 1.3492 1.8530 0.3853 2.6159 species91 species92 species93 species94 species95 species96 -0.2037 -0.4353 0.1952 -1.5037 0.5223 -0.3940 species97 species98 species99 species100 species101 species102 -1.7803 -1.0871 -1.7803 -1.8497 0.5645 -0.0720 species103 species104 species105 species106 species107 species108 -0.1926 -0.3926 1.5433 -0.5872 0.3140 3.4881 species109 species110 species111 species112 species113 species114 1.3971 0.4806 3.2341 -0.4859 1.8359 0.2072 species115 species116 species117 species118 species119 species120 -0.7822 1.0575 1.9314 2.2148 -0.5973 0.0959 species121 species122 species123 species124 species125 species126 0.7020 -1.6959 -1.0028 -0.4950 0.0299 0.3509 species127 species128 species129 species130 species131 species132 -0.1855 -0.8416 0.5266 -0.6632 -0.4950 -1.4679 species133 species134 species135 species136 species137 species138 -0.8416 0.9045 3.1571 1.6834 -0.3166 -1.2400 species139 species140 species141 species142 species143 species144 -1.2400 -1.2400 0.7749 1.2858 -0.3237 -1.2400 Log likelihood: -597 > regpop.sar(x, p.nb=0.046) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [1,] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0 0 0 0.0 [2,] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.87 0 0 0 0.0 [3,] 0.0 0.0 0.0 0.0 0.0 5.5 0.0 1.5 20.79 25 12 34 0.0 [4,] 0.0 1.7 0.0 0.0 2.7 18.2 0.0 2.1 0.00 23 0 155 42.2 [5,] 4.5 0.0 4.1 0.0 5.7 0.0 3.7 0.0 0.00 0 29 99 9.1 [6,] 0.0 0.0 0.0 3.5 0.0 0.0 78.4 0.0 0.00 0 0 0 13.1 [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [1,] 0.0 0.00 46 0 1.6 4.3 0.00 4.4 0.028 0.74 76 0 [2,] 17.3 0.00 79 66 0.9 2.0 0.00 1.4 0.187 2.97 44 75 [3,] 2.1 0.00 79 16 7.5 52.3 3.37 10.6 0.258 4.15 96 5493 [4,] 16.0 6.72 76 256 4.3 0.0 8.84 1.5 0.308 7.90 81 54 [5,] 0.0 5.52 0 237 0.0 0.0 0.71 0.0 0.000 0.00 19 414 [6,] 0.0 0.39 0 0 0.0 0.0 102.84 0.0 0.000 0.00 0 246 [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [1,] 0 0 11 10.0 14.1 0.0 0.0 59.2 230 84 90 0.0 [2,] 126 63 94 9.4 20.1 54.3 120.7 6.3 107 70 34 6.8 [3,] 232 509 26 210.3 3.2 173.4 8.6 39.3 303 225 23 11.6 [4,] 89 20 32 23.8 32.9 64.1 80.0 29.1 486 111 71 15.8 [5,] 51 13 25 81.2 42.1 33.3 84.2 13.1 19 18 206 9.5 [6,] 1378 265 0 0.0 0.0 4.7 72.7 0.0 0 0 0 17.0 [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [1,] 0.0 0 42 0.00 24.2 0 19 62 26 53.3 201 34 [2,] 8.6 33 16 0.00 3.6 23 55 18 128 3.9 34 214 [3,] 1.7 20 39 0.00 45.9 55 41 24 125 17.7 1103 63 [4,] 3.7 21 28 0.37 111.8 72 123 42 118 5.3 489 66 [5,] 47.7 22 86 1.40 3.5 17 135 232 60 11.7 51 16 [6,] 0.0 22 0 0.43 38.1 44 26 21 104 7.1 473 236 [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [1,] 68 0 2.45 5.3 47.2 0.0 0.0 0.0 0 0 0 0.0 [2,] 22 62 5.73 83.3 7.4 0.0 0.0 0.0 0 0 0 0.0 [3,] 1120 61 3.87 38.7 58.9 0.0 0.0 4.2 0 10 0 0.0 [4,] 69 443 3.29 49.2 32.5 0.0 0.0 0.0 0 0 0 0.0 [5,] 99 91 0.91 46.9 251.6 0.0 6.5 0.0 0 0 13 1.6 [6,] 260 136 3.70 57.1 113.2 1.8 0.0 0.0 4 0 32 2.3 [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [1,] 0.0 0.00 0.16 0.00 0.00 0 0 0.0 0.0 41 0 0.0 [2,] 0.0 0.00 1.18 0.00 0.00 0 24 5.4 4.2 17 0 3.4 [3,] 0.0 0.00 6.63 0.50 0.40 47 30 41.4 21.7 119 0 0.0 [4,] 3.1 0.00 0.00 0.10 3.74 22 126 83.3 6.7 54 0 0.0 [5,] 4.7 0.61 0.00 0.38 0.44 57 20 43.7 69.3 16 3 0.0 [6,] 0.0 1.69 0.00 0.00 0.00 53 0 19.5 32.3 0 0 0.0 [,74] [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [1,] 0.00 0 0 0 0.0 0.0 0.00 0.0 0.00 10.6 0.0 0.00 [2,] 0.00 0 0 0 0.0 1.2 0.00 0.0 0.00 9.7 13.4 0.00 [3,] 0.00 13 11 0 3.2 1.0 0.00 2.0 0.00 0.0 6.9 0.00 [4,] 0.87 0 0 0 0.0 0.0 0.99 3.5 0.26 0.0 0.0 0.51 [5,] 0.00 0 0 0 0.0 0.0 0.73 0.0 1.47 0.0 0.0 0.61 [6,] 0.00 0 0 41 0.0 0.0 0.00 0.0 0.00 0.0 0.0 0.00 [,86] [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97] [1,] 12.6 0 0 0.0 0 2.6 0.0 1.2 3.62 0.0 0.47 0.00 [2,] 3.2 0 25 0.0 0 0.9 0.0 3.6 0.39 0.0 0.00 0.00 [3,] 9.6 0 11 2.2 0 9.9 1.0 12.3 1.84 0.0 0.00 0.00 [4,] 0.0 22 18 19.1 230 10.4 7.7 18.8 1.42 0.0 0.00 0.93 [5,] 0.0 39 51 4.2 52 0.0 12.8 0.0 0.00 0.0 0.00 0.00 [6,] 0.0 26 0 4.4 98 0.0 1.5 0.0 0.00 3.9 0.00 0.00 [,98] [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] [,108] [1,] 0.0 0.00 0.00 0.0 0.0 18.4 1.1 0 0.0 0.0 0 [2,] 0.0 0.00 0.00 0.0 9.8 3.3 5.4 0 1.5 0.0 0 [3,] 0.0 0.00 0.00 36.2 25.5 0.0 2.8 0 2.9 12.2 0 [4,] 3.8 0.00 0.82 8.2 0.0 0.0 0.0 41 14.6 25.8 432 [5,] 0.0 0.98 0.84 0.0 0.0 0.0 0.0 28 0.0 5.4 3135 [6,] 0.0 0.00 0.00 0.0 0.0 0.0 0.0 80 0.0 0.0 524 [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [,118] [1,] 0.0 0.0 0 0.0 0 0.0 0.0 8.8 11.6 39 [2,] 7.8 0.0 0 2.1 0 0.0 0.0 8.6 0.0 90 [3,] 10.0 0.0 0 0.8 0 7.1 5.6 8.0 31.6 27 [4,] 30.3 22.9 30 3.7 184 3.5 14.9 0.0 5.3 0 [5,] 0.0 2.4 116 0.0 16 13.0 1.4 0.0 0.0 0 [6,] 0.0 3.8 76 0.0 65 0.0 0.0 0.0 0.0 0 [,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127] [,128] [1,] 0.00 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.7 [2,] 0.46 0.0 0 0.0 5.8 0.0 0.0 0.0 8.5 1.7 [3,] 0.00 0.0 0 0.0 0.0 0.0 0.0 3.2 0.0 0.0 [4,] 0.00 0.0 0 0.0 0.0 0.0 0.0 5.2 0.0 0.0 [5,] 0.00 8.6 0 1.1 0.0 6.8 9.6 0.0 2.1 0.0 [6,] 0.00 0.0 19 0.0 0.0 2.9 6.9 0.0 0.0 0.0 [,129] [,130] [,131] [,132] [,133] [,134] [,135] [,136] [,137] [,138] [1,] 0.0 0.00 0.00 0.00 0.00 0.0 0 0 0.77 0.0 [2,] 3.2 0.55 2.30 1.33 0.00 0.0 0 0 1.48 0.0 [3,] 1.7 2.04 0.52 0.64 0.00 0.0 0 27 0.00 0.0 [4,] 0.0 0.00 0.00 0.00 0.96 4.0 0 26 0.00 2.4 [5,] 0.0 0.00 0.00 0.00 0.93 1.7 43 0 0.00 0.0 [6,] 0.0 0.00 0.00 0.00 0.00 0.0 45 0 0.00 0.0 [,139] [,140] [,141] [,142] [,143] [,144] [1,] 0 0.0 0.0 0 1.8 0.00 [2,] 0 0.0 0.0 11 0.0 0.00 [3,] 4 0.0 0.0 0 0.0 0.55 [4,] 0 0.0 5.9 0 0.0 0.00 [5,] 0 3.6 0.0 0 0.0 0.00 [6,] 0 0.0 0.0 0 0.0 0.00 > options(digits=4) > > x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb, distance="none",toprab=TRUE,toprabp=0.5) > x2 <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb, distance="none",toprab=TRUE,toprabp=0) > x$prab [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [1,] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [2,] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE [4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [1,] TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [2,] TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE [3,] TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [4,] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE [6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [1,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [2,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [3,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [4,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [5,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE [6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [1,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE [2,] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE [3,] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [4,] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [5,] TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE [6,] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE [,49] [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [1,] TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [2,] TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE [3,] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE [4,] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [5,] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [6,] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [,61] [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [2,] TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE [3,] TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE [4,] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE [5,] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE [6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE [4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE [5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [,85] [,86] [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [3,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE [4,] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE [5,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE [6,] FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE [,97] [,98] [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [4,] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE [5,] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE [,108] [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [4,] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE [5,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [6,] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE [,118] [,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127] [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [4,] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [5,] TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE [6,] TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE [,128] [,129] [,130] [,131] [,132] [,133] [,134] [,135] [,136] [,137] [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [5,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [6,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [,138] [,139] [,140] [,141] [,142] [,143] [,144] [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE [4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE [5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE [6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE > x2$prab [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [1,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [2,] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE [4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [1,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [2,] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE [3,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [4,] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE [6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [1,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [2,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [3,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [4,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [5,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [1,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [2,] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [3,] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [4,] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [5,] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE [6,] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [,49] [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [1,] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [2,] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [3,] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE [4,] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [5,] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [6,] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [,61] [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [2,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE [3,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [4,] FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE [5,] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE [6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [3,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [4,] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE [5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [,85] [,86] [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [3,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE [4,] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [5,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE [6,] FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE [,97] [,98] [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [4,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [5,] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE [,108] [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [4,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [5,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [6,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [,118] [,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127] [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [4,] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [5,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [6,] TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE [,128] [,129] [,130] [,131] [,132] [,133] [,134] [,135] [,136] [,137] [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [5,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [6,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [,138] [,139] [,140] [,141] [,142] [,143] [,144] [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE [4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE [5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE [6,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE > > > > proc.time() user system elapsed 7.04 0.51 7.53