Check results for 'prabclus'

using log directory '/data/gannet/tmp/ripley/R/packages/tests-Sun/prabclus.Rcheck'
using R version 2.11.0 Under development (unstable) (2010-02-03 r51097)
using session charset: UTF-8
checking for file 'prabclus/DESCRIPTION' ... OK
this is package 'prabclus' version '2.1-4'
checking package dependencies ... OK
checking if this is a source package ... OK
checking for executable files ... OK
checking whether package 'prabclus' can be installed ... OK
checking package directory ... OK
checking for portable file names ... OK
checking for sufficient/correct file permissions ... OK
checking DESCRIPTION meta-information ... OK
checking top-level files ... OK
checking index information ... OK
checking package subdirectories ... OK
checking R files for non-ASCII characters ... OK
checking R files for syntax errors ... OK
checking whether the package can be loaded ... OK
checking whether the package can be loaded with stated dependencies ... OK
checking whether the package can be unloaded cleanly ... OK
checking for unstated dependencies in R code ... OK
checking S3 generic/method consistency ... OK
checking replacement functions ... OK
checking foreign function calls ... OK
checking R code for possible problems ... OK
checking Rd files ... OK
checking Rd metadata ... OK
checking Rd cross-references ... OK
checking for missing documentation entries ... OK
checking for code/documentation mismatches ... OK
checking Rd \usage sections ... OK
checking Rd contents ... OK
checking data for non-ASCII characters ... OK
checking examples ... OK
checking tests ...
Running ‘examples.R’
Comparing ‘examples.Rout’ to ‘examples.Rout.save’ ...14c14
< atcnst> ax <- autoconst(x,nperp=2,step1=0.3,twostep=FALSE)
---
> atcnst> ax <- autoconst(x, nperp = 2)
16a17,18
> Estimating disj. parameter: Simulations for p= 0.1
> Estimating disj. parameter: Simulations for p= 0.2
17a20,21
> Estimating disj. parameter: Simulations for p= 0.4
> Estimating disj. parameter: Simulations for p= 0.5
18a23,24
> Estimating disj. parameter: Simulations for p= 0.7
> Estimating disj. parameter: Simulations for p= 0.8
20c26,39
< Estimated disjunction parameter = 0.3659619
---
> 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
27,28c46,47
< clspp.> cluspop.nb(nb, p.nb=0.1, n.species=10, clus.specs=9, reg.group=1:17,
< clspp.+ vector.species=c(10))
---
> clspp.> cluspop.nb(nb, p.nb = 0.1, n.species = 10, clus.specs = 9,
> reg.group = 1:17, vector.species = c(10))
82,84c101,102
< cn.cmp> for(i in 1:20)
< cn.cmp+ for(j in 1:20)
< cn.cmp+ m[i,j] <- abs(x[i]-x[j])
---
> cn.cmp> for (i in 1:20) for (j in 1:20) m[i, j] <- abs(x[i] -
> x[j])
90c108
< cn.cmp> max(cc) # number of connectivity components
---
> cn.cmp> max(cc)
101c119
< cn.rgm+ reg.group=1:17,vector.species=c(10))
---
> reg.group = 1:17, vector.species = c(10))
180c198
< [1] 602
---
> [1] 524
2152,2154c2170,2185
< by using mclust, or by using any other package that invokes mclust,
< you accept the license agreement in the mclust LICENSE file
< and at http://www.stat.washington.edu/mclust/license.txt
---
> Warning: this is the 2002 version of mclust.
>
> It is not backwards compatible with the previous version,
> so old scripts will no longer work. The previous version
> of mclust is still available as mclust1998.
>
> Since mclust1998 is not actively supported any more,
> please change to the new version.
>
>
> Attaching package 'mclust':
>
>
> The following object(s) are masked from package:base :
>
> density
2159a2191,2195
> [1] -33087.29
> [1] -33249.03
> [1] -33338.22
> [1] -33381.63
> [1] -33401.38
2190c2226,2227
< pop.sm> pop.sim(t(kykladspecreg), nb, times=5, h0c=0.35, teststat="nn", testc=3)
---
> pop.sm> pop.sim(t(kykladspecreg), nb, times = 20, h0c = 0.35,
> teststat = "nn", testc = 3)
2195a2233,2247
> 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
2198c2250,2252
< [1] 0.3411064 0.3215575 0.3327210 0.3312433 0.3323695
---
> [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
2204c2258
< [1] 0.1666667
---
> [1] 0.04761905
2222,2224d2275
< prbcls> # If you want to use your own ASCII data files, use
< prbcls> # x <- prabinit(file="path/prabmatrixfile",
< prbcls> # neighborhood="path/neighborhoodfile")
2226c2277,2278
< [1] 9
---
> [1] 1460.070
> [1] 1460.747
2235,2237c2287,2289
< [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
---
> [1] 9 3 5 2 2 5 6 8 1 8 2 6 9 1 3 6 6 5 9 9 9 5 1 3 1 7 6 7 4 3 4 7 9 6 3 4 9 3
> [39] 4 5 3 2 4 4 7 9 3 2 2 3 1 1 8 1 7 4 1 1 2 5 1 3 7 1 2 4 2 4 2 2 4 3 1 1 5 5
> [77] 2 7 4 6
2240,2244c2292,2296
< [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"
---
> [1] "N" "3" "5" "2" "2" "5" "6" "8" "1" "8" "2" "6" "N" "1" "3" "6" "6" "5" "N"
> [20] "N" "N" "5" "1" "3" "1" "7" "6" "7" "4" "3" "4" "7" "N" "6" "3" "4" "N" "3"
> [39] "4" "5" "3" "2" "4" "4" "7" "N" "3" "2" "2" "3" "1" "1" "8" "1" "7" "4" "1"
> [58] "1" "2" "5" "1" "3" "7" "1" "2" "4" "2" "4" "2" "2" "4" "3" "1" "1" "5" "5"
> [77] "2" "7" "4" "6"
2255,2258c2307
< prbtst> # If you want to use your own ASCII data files, use
< prbtst> # x <- prabinit(file="path/prabmatrixfile",
< prbtst> # neighborhood="path/neighborhoodfile")
< prbtst> prabtest(x, times=5, pd=0.35)
---
> prbtst> prabtest(x, times = 100, pd = 0.35)
2263a2313,2407
> 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
2266c2410,2424
< [1] 0.5082451 0.4583253 0.4407105 0.4422233 0.5477648
---
> [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
2272c2430
< [1] 0.1666667
---
> [1] 0.00990099
2289,2291d2446
< $gtf
< [1] 0.1
<
2293c2448
< [1] 5
---
> [1] 100
2306c2461,2462
< rndpp.> randpop.nb(nb, p.nb=0.1, n.species=5, vector.species=c(1,10,20,30,34))
---
> rndpp.> randpop.nb(nb, p.nb = 0.1, n.species = 5, vector.species = c(1,
> 10, 20, 30, 34))
2347,3434c2503
< > # example(summary.prabtest)
< > example(geco)
<
< geco> data(kykladspecreg)
<
< geco> data(waterdist)
<
< geco> geco(t(kykladspecreg),waterdist)
< [,1] [,2] [,3] [,4] [,5] [,6]
< [1,] 0.00000000 0.67309671 0.50128601 0.4740226 0.4256687 0.69804527
< [2,] 0.67309671 0.00000000 0.44444444 1.0000000 1.0000000 0.27083333
< [3,] 0.50128601 0.44444444 0.00000000 1.0000000 1.0000000 0.23611111
< [4,] 0.47402263 1.00000000 1.00000000 0.0000000 1.0000000 1.00000000
< [5,] 0.42566872 1.00000000 1.00000000 1.0000000 0.0000000 1.00000000
< [6,] 0.69804527 0.27083333 0.23611111 1.0000000 1.0000000 0.00000000
< [7,] 0.42541152 0.32870370 0.38194444 1.0000000 1.0000000 0.75000000
< [8,] 0.26195988 1.00000000 1.00000000 0.4637346 0.4093364 1.00000000
< [9,] 0.45859053 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [10,] 0.32947531 1.00000000 1.00000000 1.0000000 0.4000000 1.00000000
< [11,] 0.62217078 1.00000000 1.00000000 0.3333333 1.0000000 1.00000000
< [12,] 0.49094650 0.61481481 0.54537037 1.0000000 1.0000000 0.79537037
< [13,] 0.16215395 0.61268939 0.52398990 0.4681187 0.4087753 0.43181818
< [14,] 0.41820988 1.00000000 1.00000000 1.0000000 0.5625000 1.00000000
< [15,] 0.68184156 0.33333333 0.17361111 1.0000000 1.0000000 0.27083333
< [16,] 0.42155350 0.35763889 0.36168981 1.0000000 1.0000000 0.73148148
< [17,] 0.44598765 0.54861111 0.19675926 1.0000000 1.0000000 0.57175926
< [18,] 0.69804527 0.27083333 0.23611111 1.0000000 1.0000000 0.00000000
< [19,] 0.42636684 1.00000000 1.00000000 0.4285714 1.0000000 1.00000000
< [20,] 0.21135266 0.62318841 0.65187198 0.4782609 0.4396135 0.56068841
< [21,] 0.17255658 0.37951389 0.40277778 0.4750000 0.4597222 0.40798611
< [22,] 0.56610082 0.48958333 0.06944444 1.0000000 1.0000000 0.16666667
< [23,] 0.47659465 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [24,] 1.00000000 0.33333333 1.00000000 1.0000000 1.0000000 0.33333333
< [25,] 0.47530864 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [26,] 0.42155350 0.39814815 0.37037037 1.0000000 1.0000000 0.58796296
< [27,] 0.42541152 0.32870370 0.38194444 1.0000000 1.0000000 0.75000000
< [28,] 0.47659465 0.75000000 0.23148148 1.0000000 1.0000000 0.27083333
< [29,] 0.48148148 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [30,] 1.00000000 0.33333333 1.00000000 1.0000000 1.0000000 0.33333333
< [31,] 0.44444444 1.00000000 1.00000000 1.0000000 0.3472222 1.00000000
< [32,] 0.47659465 0.75000000 0.23148148 1.0000000 1.0000000 0.27083333
< [33,] 0.44212963 0.54861111 0.40509259 1.0000000 1.0000000 0.74537037
< [34,] 0.44598765 0.54861111 0.19675926 1.0000000 1.0000000 0.57175926
< [35,] 0.67309671 0.00000000 0.44444444 1.0000000 1.0000000 0.27083333
< [36,] 0.07091532 0.42469618 0.43923611 0.4780816 0.4372830 0.44249132
< [37,] 0.53883745 0.58333333 0.42361111 1.0000000 1.0000000 0.52083333
< [38,] 0.68184156 0.33333333 0.17361111 1.0000000 1.0000000 0.27083333
< [39,] 0.48148148 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [40,] 0.69804527 0.27083333 0.23611111 1.0000000 1.0000000 0.00000000
< [41,] 0.68492798 0.33333333 0.50694444 1.0000000 1.0000000 0.27083333
< [42,] 0.52186214 1.00000000 1.00000000 0.3368056 0.3107639 1.00000000
< [43,] 0.48148148 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [44,] 0.43004115 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [45,] 0.47659465 0.75000000 0.23148148 1.0000000 1.0000000 0.27083333
< [46,] 0.32986111 1.00000000 1.00000000 1.0000000 0.6777778 1.00000000
< [47,] 0.68184156 0.33333333 0.17361111 1.0000000 1.0000000 0.27083333
< [48,] 1.00000000 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [49,] 0.36368313 1.00000000 1.00000000 1.0000000 0.3715278 1.00000000
< [50,] 1.00000000 0.33333333 1.00000000 1.0000000 1.0000000 0.33333333
< [51,] 0.46373457 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [52,] 0.44032922 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [53,] 0.36574074 1.00000000 1.00000000 1.0000000 0.3750000 1.00000000
< [54,] 0.45859053 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [55,] 0.47659465 0.75000000 0.23148148 1.0000000 1.0000000 0.27083333
< [56,] 0.94830247 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [57,] 0.43647119 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [58,] 0.47659465 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [59,] 0.39043210 1.00000000 1.00000000 0.3750000 0.2378472 1.00000000
< [60,] 0.60892490 0.23437500 0.12500000 1.0000000 1.0000000 0.05208333
< [61,] 0.43955761 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [62,] 0.68184156 0.33333333 0.17361111 1.0000000 1.0000000 0.27083333
< [63,] 0.47659465 0.75000000 0.23148148 1.0000000 1.0000000 0.27083333
< [64,] 0.44225823 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [65,] 0.67566872 1.00000000 1.00000000 1.0000000 0.2500000 1.00000000
< [66,] 0.94830247 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [67,] 0.40715021 1.00000000 1.00000000 0.2500000 0.2500000 1.00000000
< [68,] 0.48148148 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [69,] 0.31995885 1.00000000 1.00000000 0.4184028 0.3263889 1.00000000
< [70,] 1.00000000 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [71,] 0.43518519 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [72,] 0.67309671 0.00000000 0.44444444 1.0000000 1.0000000 0.27083333
< [73,] 0.45859053 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [74,] 0.46064815 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [75,] 0.52932099 0.09837963 0.17592593 1.0000000 1.0000000 0.19328704
< [76,] 0.56610082 0.48958333 0.06944444 1.0000000 1.0000000 0.16666667
< [77,] 0.26208848 1.00000000 1.00000000 0.4362374 0.3686869 1.00000000
< [78,] 0.47659465 0.75000000 0.23148148 1.0000000 1.0000000 0.27083333
< [79,] 0.48148148 1.00000000 1.00000000 1.0000000 1.0000000 1.00000000
< [80,] 0.42412551 0.42129630 0.31250000 1.0000000 1.0000000 0.68750000
< [,7] [,8] [,9] [,10] [,11] [,12] [,13]
< [1,] 0.42541152 0.2619599 0.45859053 0.3294753 0.6221708 0.4909465 0.1621539
< [2,] 0.32870370 1.0000000 1.00000000 1.0000000 1.0000000 0.6148148 0.6126894
< [3,] 0.38194444 1.0000000 1.00000000 1.0000000 1.0000000 0.5453704 0.5239899
< [4,] 1.00000000 0.4637346 1.00000000 1.0000000 0.3333333 1.0000000 0.4681187
< [5,] 1.00000000 0.4093364 1.00000000 0.4000000 1.0000000 1.0000000 0.4087753
< [6,] 0.75000000 1.0000000 1.00000000 1.0000000 1.0000000 0.7953704 0.4318182
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< [16,] 1.0000000 1.0000000 0.35763889 1.00000000 1.0000000 0.20775463 0.66319444
< [17,] 1.0000000 1.0000000 0.54861111 1.00000000 1.0000000 0.27430556 0.50347222
< [18,] 1.0000000 1.0000000 0.27083333 1.00000000 1.0000000 0.19328704 0.16666667
< [19,] 1.0000000 0.7549603 1.00000000 0.46329365 0.3551587 1.00000000 1.00000000
< [20,] 0.4782609 0.4202899 0.62318841 0.44836957 0.4559179 0.70189211 0.63451087
< [21,] 0.4750000 0.6041667 0.37951389 0.45000000 0.9732639 0.35000000 0.43298611
< [22,] 1.0000000 1.0000000 0.48958333 1.00000000 1.0000000 0.27662037 0.00000000
< [23,] 1.0000000 1.0000000 1.00000000 1.00000000 0.1909722 1.00000000 1.00000000
< [24,] 1.0000000 1.0000000 0.33333333 1.00000000 1.0000000 0.41666667 1.00000000
< [25,] 1.0000000 1.0000000 1.00000000 1.00000000 0.1909722 1.00000000 1.00000000
< [26,] 1.0000000 1.0000000 0.39814815 1.00000000 1.0000000 0.17592593 0.47337963
< [27,] 1.0000000 1.0000000 0.32870370 1.00000000 1.0000000 0.18171296 0.68171296
< [28,] 1.0000000 1.0000000 0.75000000 1.00000000 1.0000000 0.36574074 0.15625000
< [29,] 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [30,] 1.0000000 1.0000000 0.33333333 1.00000000 1.0000000 0.41666667 1.00000000
< [31,] 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [32,] 1.0000000 1.0000000 0.75000000 1.00000000 1.0000000 0.36574074 0.15625000
< [33,] 1.0000000 1.0000000 0.54861111 1.00000000 1.0000000 0.39583333 0.71180556
< [34,] 1.0000000 1.0000000 0.54861111 1.00000000 1.0000000 0.27430556 0.50347222
< [35,] 1.0000000 1.0000000 0.00000000 1.00000000 1.0000000 0.09837963 0.48958333
< [36,] 0.4843750 0.4411892 0.42469618 0.45920139 0.4667969 0.40625000 0.45811632
< [37,] 1.0000000 1.0000000 0.58333333 0.28472222 1.0000000 0.57870370 0.40625000
< [38,] 1.0000000 1.0000000 0.33333333 1.00000000 1.0000000 0.32870370 0.15625000
< [39,] 1.0000000 0.3333333 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [40,] 1.0000000 1.0000000 0.27083333 1.00000000 1.0000000 0.19328704 0.16666667
< [41,] 1.0000000 1.0000000 0.33333333 0.87731481 1.0000000 0.41203704 0.48958333
< [42,] 0.3750000 1.0000000 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [43,] 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [44,] 1.0000000 0.2708333 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [45,] 1.0000000 1.0000000 0.75000000 1.00000000 1.0000000 0.36574074 0.15625000
< [46,] 1.0000000 0.5368056 1.00000000 0.40000000 0.9708333 1.00000000 1.00000000
< [47,] 1.0000000 1.0000000 0.33333333 1.00000000 1.0000000 0.32870370 0.15625000
< [48,] 0.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [49,] 1.0000000 0.8871528 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [50,] 1.0000000 1.0000000 0.33333333 1.00000000 1.0000000 0.41666667 1.00000000
< [51,] 1.0000000 1.0000000 1.00000000 0.03472222 1.0000000 1.00000000 1.00000000
< [52,] 1.0000000 1.0000000 1.00000000 0.28472222 0.4409722 1.00000000 1.00000000
< [53,] 1.0000000 1.0000000 1.00000000 0.25000000 1.0000000 1.00000000 1.00000000
< [54,] 1.0000000 1.0000000 1.00000000 1.00000000 0.4340278 1.00000000 1.00000000
< [55,] 1.0000000 1.0000000 0.75000000 1.00000000 1.0000000 0.36574074 0.15625000
< [56,] 1.0000000 0.9236111 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [57,] 1.0000000 0.9675926 1.00000000 0.16666667 0.9594907 1.00000000 1.00000000
< [58,] 1.0000000 1.0000000 1.00000000 1.00000000 0.1909722 1.00000000 1.00000000
< [59,] 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [60,] 1.0000000 1.0000000 0.23437500 1.00000000 1.0000000 0.09259259 0.17708333
< [61,] 1.0000000 0.6504630 1.00000000 1.00000000 0.5081019 1.00000000 1.00000000
< [62,] 1.0000000 1.0000000 0.33333333 1.00000000 1.0000000 0.32870370 0.15625000
< [63,] 1.0000000 1.0000000 0.75000000 1.00000000 1.0000000 0.36574074 0.15625000
< [64,] 1.0000000 0.9716435 1.00000000 0.20312500 0.9635417 1.00000000 1.00000000
< [65,] 0.2500000 1.0000000 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [66,] 1.0000000 0.9236111 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [67,] 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [68,] 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [69,] 1.0000000 0.7456597 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [70,] 0.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [71,] 1.0000000 0.0000000 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [72,] 1.0000000 1.0000000 0.00000000 1.00000000 1.0000000 0.09837963 0.48958333
< [73,] 1.0000000 1.0000000 1.00000000 0.00000000 1.0000000 1.00000000 1.00000000
< [74,] 1.0000000 1.0000000 1.00000000 1.00000000 0.0000000 1.00000000 1.00000000
< [75,] 1.0000000 1.0000000 0.09837963 1.00000000 1.0000000 0.00000000 0.27662037
< [76,] 1.0000000 1.0000000 0.48958333 1.00000000 1.0000000 0.27662037 0.00000000
< [77,] 1.0000000 0.3465909 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [78,] 1.0000000 1.0000000 0.75000000 1.00000000 1.0000000 0.36574074 0.15625000
< [79,] 1.0000000 0.3333333 1.00000000 1.00000000 1.0000000 1.00000000 1.00000000
< [80,] 1.0000000 1.0000000 0.42129630 1.00000000 1.0000000 0.22800926 0.61921296
< [,77] [,78] [,79] [,80]
< [1,] 0.2620885 0.4765947 0.4814815 0.42412551
< [2,] 1.0000000 0.7500000 1.0000000 0.42129630
< [3,] 1.0000000 0.2314815 1.0000000 0.31250000
< [4,] 0.4362374 1.0000000 1.0000000 1.00000000
< [5,] 0.3686869 1.0000000 1.0000000 1.00000000
< [6,] 1.0000000 0.2708333 1.0000000 0.68750000
< [7,] 1.0000000 0.8425926 1.0000000 0.05787037
< [8,] 0.3368757 1.0000000 1.0000000 1.00000000
< [9,] 1.0000000 1.0000000 1.0000000 1.00000000
< [10,] 0.4680556 1.0000000 1.0000000 1.00000000
< [11,] 0.5574495 1.0000000 0.3333333 1.00000000
< [12,] 0.8545455 0.8583333 0.4000000 0.33472222
< [13,] 0.2080177 0.4687500 0.4772727 0.80545034
< [14,] 0.7986111 1.0000000 1.0000000 1.00000000
< [15,] 1.0000000 0.6250000 1.0000000 0.53240741
< [16,] 1.0000000 0.8489583 1.0000000 0.01736111
< [17,] 1.0000000 0.7638889 1.0000000 0.11574074
< [18,] 1.0000000 0.2708333 1.0000000 0.68750000
< [19,] 0.7409812 1.0000000 1.0000000 1.00000000
< [20,] 0.2952899 0.8043478 0.4782609 0.81139291
< [21,] 0.3876263 0.4597222 1.0000000 0.44270833
< [22,] 1.0000000 0.1562500 1.0000000 0.61921296
< [23,] 1.0000000 1.0000000 1.0000000 1.00000000
< [24,] 1.0000000 1.0000000 1.0000000 1.00000000
< [25,] 1.0000000 1.0000000 1.0000000 1.00000000
< [26,] 1.0000000 0.3333333 1.0000000 0.27777778
< [27,] 1.0000000 0.8425926 1.0000000 0.05787037
< [28,] 1.0000000 0.0000000 1.0000000 0.83796296
< [29,] 0.4545455 1.0000000 1.0000000 1.00000000
< [30,] 1.0000000 1.0000000 1.0000000 1.00000000
< [31,] 0.3989899 1.0000000 1.0000000 1.00000000
< [32,] 1.0000000 0.0000000 1.0000000 0.83796296
< [33,] 1.0000000 0.9722222 1.0000000 0.08101852
< [34,] 1.0000000 0.7638889 1.0000000 0.11574074
< [35,] 1.0000000 0.7500000 1.0000000 0.42129630
< [36,] 0.2951389 0.4748264 0.4843750 0.47721354
< [37,] 1.0000000 0.7187500 1.0000000 0.67824074
< [38,] 1.0000000 0.6250000 1.0000000 0.53240741
< [39,] 0.4545455 1.0000000 0.0000000 1.00000000
< [40,] 1.0000000 0.2708333 1.0000000 0.68750000
< [41,] 1.0000000 0.7500000 1.0000000 0.72685185
< [42,] 0.4229798 1.0000000 1.0000000 1.00000000
< [43,] 0.4545455 1.0000000 1.0000000 1.00000000
< [44,] 0.3623737 1.0000000 1.0000000 1.00000000
< [45,] 1.0000000 0.0000000 1.0000000 0.83796296
< [46,] 0.4847854 1.0000000 1.0000000 1.00000000
< [47,] 1.0000000 0.6250000 1.0000000 0.53240741
< [48,] 1.0000000 1.0000000 1.0000000 1.00000000
< [49,] 0.3041351 1.0000000 1.0000000 1.00000000
< [50,] 1.0000000 1.0000000 1.0000000 1.00000000
< [51,] 1.0000000 1.0000000 1.0000000 1.00000000
< [52,] 1.0000000 1.0000000 1.0000000 1.00000000
< [53,] 0.5732323 1.0000000 1.0000000 1.00000000
< [54,] 1.0000000 1.0000000 1.0000000 1.00000000
< [55,] 1.0000000 0.0000000 1.0000000 0.83796296
< [56,] 0.9438131 1.0000000 1.0000000 1.00000000
< [57,] 0.9793771 1.0000000 1.0000000 1.00000000
< [58,] 1.0000000 1.0000000 1.0000000 1.00000000
< [59,] 0.4360795 1.0000000 1.0000000 1.00000000
< [60,] 1.0000000 0.2986111 1.0000000 0.38831019
< [61,] 0.7716751 1.0000000 1.0000000 1.00000000
< [62,] 1.0000000 0.6250000 1.0000000 0.53240741
< [63,] 1.0000000 0.0000000 1.0000000 0.83796296
< [64,] 0.9834280 1.0000000 1.0000000 1.00000000
< [65,] 0.6186869 1.0000000 1.0000000 1.00000000
< [66,] 0.9438131 1.0000000 1.0000000 1.00000000
< [67,] 0.3232323 1.0000000 1.0000000 1.00000000
< [68,] 0.4545455 1.0000000 1.0000000 1.00000000
< [69,] 0.2390309 1.0000000 1.0000000 1.00000000
< [70,] 1.0000000 1.0000000 1.0000000 1.00000000
< [71,] 0.3465909 1.0000000 0.3333333 1.00000000
< [72,] 1.0000000 0.7500000 1.0000000 0.42129630
< [73,] 1.0000000 1.0000000 1.0000000 1.00000000
< [74,] 1.0000000 1.0000000 1.0000000 1.00000000
< [75,] 1.0000000 0.3657407 1.0000000 0.22800926
< [76,] 1.0000000 0.1562500 1.0000000 0.61921296
< [77,] 0.0000000 1.0000000 0.4545455 1.00000000
< [78,] 1.0000000 0.0000000 1.0000000 0.83796296
< [79,] 0.4545455 1.0000000 0.0000000 1.00000000
< [80,] 1.0000000 0.8379630 1.0000000 0.00000000
< > example(geo2neighbor)
<
< g2nghb> data(waterdist)
<
< g2nghb> geo2neighbor(waterdist)
< [[1]]
< [1] 2 3 4
<
< [[2]]
< [1] 1 3 4
<
< [[3]]
< [1] 1 2 4
<
< [[4]]
< [1] 1 2 3
<
< [[5]]
< [1] 6
<
< [[6]]
< [1] 5 7
<
< [[7]]
< [1] 6 9
<
< [[8]]
< numeric(0)
<
< [[9]]
< [1] 7 10
<
< [[10]]
< [1] 9 11 12 13
<
< [[11]]
< [1] 10 12 13
<
< [[12]]
< [1] 10 11 13
<
< [[13]]
< [1] 10 11 12
<
< [[14]]
< [1] 15 16
<
< [[15]]
< [1] 14 16 17
<
< [[16]]
< [1] 14 15 17 20
<
< [[17]]
< [1] 15 16 18 19 20 21 22
<
< [[18]]
< [1] 17 19 20 21 22
<
< [[19]]
< [1] 17 18 20 21 22
<
< [[20]]
< [1] 16 17 18 19 21 22
<
< [[21]]
< [1] 17 18 19 20 22 23
<
< [[22]]
< [1] 17 18 19 20 21 23
<
< [[23]]
< [1] 21 22
<
< [[24]]
< numeric(0)
<
< [[25]]
< numeric(0)
<
< [[26]]
< numeric(0)
<
< [[27]]
< [1] 29 32 33
<
< [[28]]
< numeric(0)
<
< [[29]]
< [1] 27 31 32 33
<
< [[30]]
< [1] 31 32 33
<
< [[31]]
< [1] 29 30 32 33
<
< [[32]]
< [1] 27 29 30 31 33
<
< [[33]]
< [1] 27 29 30 31 32
<
< [[34]]
< numeric(0)
<
< > example(hprabclust)
---
> > example(summary.prabtest)
3436c2505
< hprbcl> data(kykladspecreg)
---
> smmry.> data(kykladspecreg)
3438c2507
< hprbcl> data(nb)
---
> smmry.> data(nb)
3440,4891c2509
< hprbcl> data(waterdist)
<
< hprbcl> x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb,
< hprbcl+ geodist=waterdist, distance="geco")
<
< hprbcl> hprabclust(x,mdsplot=FALSE)
< * Clustered presence-absence matrix *
<
< Clustered by hclust with noise detection, method=: average
<
< Distance value to cut tree: 0.4
< Minimum cluster size (below is noise): 3
< Call:
< hprabclust(prabobj = x, mdsplot = FALSE)
<
< Cluster memberships:
< [1] 2 1 1 0 9 1 3 2 5 10 0 3 2 6 1 3 3 1 0 2 2 1 6 7 0
< [26] 4 3 4 0 7 0 4 3 3 1 2 5 1 0 1 7 8 0 0 4 2 1 8 2 7
< [51] 5 0 10 6 4 0 5 6 9 1 0 1 4 5 8 0 9 10 2 8 0 1 5 6 1
< [76] 1 2 4 0 3
<
< > example(piecewiselin)
<
< pcwsln> data(waterdist)
<
< pcwsln> piecewiselin(waterdist)
< V1 V2 V3 V4 V5 V6 V7 V8
< 1 0.0000000 0.1388889 0.7638889 0.9027778 1.0000000 1.0000000 1.0000000 1
< 2 0.1388889 0.0000000 0.6250000 0.7638889 1.0000000 1.0000000 1.0000000 1
< 3 0.7638889 0.6250000 0.0000000 0.1388889 1.0000000 1.0000000 1.0000000 1
< 4 0.9027778 0.7638889 0.1388889 0.0000000 1.0000000 1.0000000 1.0000000 1
< 5 1.0000000 1.0000000 1.0000000 1.0000000 0.0000000 0.9722222 1.0000000 1
< 6 1.0000000 1.0000000 1.0000000 1.0000000 0.9722222 0.0000000 0.7638889 1
< 7 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.7638889 0.0000000 1
< 8 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0
< 9 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9027778 1
< 10 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 11 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 12 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 13 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 14 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 15 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 16 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 17 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 18 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 19 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 20 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 21 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 22 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 23 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 24 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 25 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 26 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 27 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 28 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 29 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 30 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 31 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 32 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 33 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< 34 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1
< V9 V10 V11 V12 V13 V14 V15
< 1 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 2 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 3 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 4 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 5 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 6 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 7 0.9027778 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 8 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 9 0.0000000 0.9027778 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 10 0.9027778 0.0000000 0.83333333 0.9722222 0.90277778 1.0000000 1.0000000
< 11 1.0000000 0.8333333 0.00000000 0.1388889 0.06944444 1.0000000 1.0000000
< 12 1.0000000 0.9722222 0.13888889 0.0000000 0.20833333 1.0000000 1.0000000
< 13 1.0000000 0.9027778 0.06944444 0.2083333 0.00000000 1.0000000 1.0000000
< 14 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 0.0000000 0.6250000
< 15 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 0.6250000 0.0000000
< 16 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 0.9722222 0.3472222
< 17 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 0.9722222
< 18 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 19 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 20 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 21 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 22 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 23 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 24 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 25 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 26 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 27 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 28 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 29 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 30 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 31 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 32 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 33 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< 34 1.0000000 1.0000000 1.00000000 1.0000000 1.00000000 1.0000000 1.0000000
< V16 V17 V18 V19 V20 V21 V22
< 1 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 2 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 3 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 4 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 5 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 6 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 7 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 8 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 9 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 10 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 11 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 12 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 13 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 14 0.9722222 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 15 0.3472222 0.9722222 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 16 0.0000000 0.6250000 1.00000000 1.00000000 0.9722222 1.0000000 1.0000000
< 17 0.6250000 0.0000000 0.83333333 0.76388889 0.3472222 0.6250000 0.8333333
< 18 1.0000000 0.8333333 0.00000000 0.06944444 0.4861111 0.7638889 0.9722222
< 19 1.0000000 0.7638889 0.06944444 0.00000000 0.4166667 0.6944444 0.9027778
< 20 0.9722222 0.3472222 0.48611111 0.41666667 0.0000000 0.2777778 0.4861111
< 21 1.0000000 0.6250000 0.76388889 0.69444444 0.2777778 0.0000000 0.2083333
< 22 1.0000000 0.8333333 0.97222222 0.90277778 0.4861111 0.2083333 0.0000000
< 23 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 0.9027778 0.6944444
< 24 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 25 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 26 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 27 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 28 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 29 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 30 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 31 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 32 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 33 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< 34 1.0000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000
< V23 V24 V25 V26 V27 V28 V29 V30 V31 V32
< 1 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 2 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 3 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 4 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 5 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 6 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 7 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 8 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 9 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 10 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 11 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 12 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 13 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 14 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 15 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 16 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 17 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 18 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 19 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 20 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 21 0.9027778 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 22 0.6944444 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 23 0.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 24 1.0000000 0 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 25 1.0000000 1 0 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 26 1.0000000 1 1 0 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< 27 1.0000000 1 1 1 0.0000000 1 0.6250000 1.0000000 1.0000000 0.9722222
< 28 1.0000000 1 1 1 1.0000000 0 1.0000000 1.0000000 1.0000000 1.0000000
< 29 1.0000000 1 1 1 0.6250000 1 0.0000000 1.0000000 0.9027778 0.6250000
< 30 1.0000000 1 1 1 1.0000000 1 1.0000000 0.0000000 0.1388889 0.4166667
< 31 1.0000000 1 1 1 1.0000000 1 0.9027778 0.1388889 0.0000000 0.2777778
< 32 1.0000000 1 1 1 0.9722222 1 0.6250000 0.4166667 0.2777778 0.0000000
< 33 1.0000000 1 1 1 0.7638889 1 0.4166667 0.6250000 0.4861111 0.2083333
< 34 1.0000000 1 1 1 1.0000000 1 1.0000000 1.0000000 1.0000000 1.0000000
< V33 V34
< 1 1.0000000 1
< 2 1.0000000 1
< 3 1.0000000 1
< 4 1.0000000 1
< 5 1.0000000 1
< 6 1.0000000 1
< 7 1.0000000 1
< 8 1.0000000 1
< 9 1.0000000 1
< 10 1.0000000 1
< 11 1.0000000 1
< 12 1.0000000 1
< 13 1.0000000 1
< 14 1.0000000 1
< 15 1.0000000 1
< 16 1.0000000 1
< 17 1.0000000 1
< 18 1.0000000 1
< 19 1.0000000 1
< 20 1.0000000 1
< 21 1.0000000 1
< 22 1.0000000 1
< 23 1.0000000 1
< 24 1.0000000 1
< 25 1.0000000 1
< 26 1.0000000 1
< 27 0.7638889 1
< 28 1.0000000 1
< 29 0.4166667 1
< 30 0.6250000 1
< 31 0.4861111 1
< 32 0.2083333 1
< 33 0.0000000 1
< 34 1.0000000 0
< > example(qkulczynski)
<
< qklczy> data(kykladspecreg)
<
< qklczy> qkulczynski(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
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< [28,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
< [29,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
< [30,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
< [31,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.4375000 1.0000000
< [32,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 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 1.0000000 1.0000000 1.0000000
< [35,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
< [36,] 0.4375000 0.4687500 0.6354167 0.4687500 0.4843750 0.3750000 0.4843750
< [37,] 0.6250000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
< [38,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
< [39,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
< [40,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
< [41,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
< [42,] 1.0000000 0.2500000 1.0000000 0.2500000 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
< > example(regpop.sar)
<
< rgpp.s> data(siskiyou)
< Read 144 items
<
< rgpp.s> set.seed(1234)
<
< rgpp.s> x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb,
< rgpp.s+ distance="none")
<
< rgpp.s> # Not run; this needs package spdep.
< rgpp.s> # regpop.sar(x, p.nb=0.046)
< rgpp.s> regpop.sar(x, p.nb=0.046, sarestimate=prab.sarestimate(x,sar=FALSE))
< [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
< [1,] 4.423809 0.000000 0.8044818 0.000000 0.000000 0.7319343 0.000000 0.00000
< [2,] 0.000000 0.000000 0.0000000 0.000000 8.045755 0.8970204 0.000000 0.00000
< [3,] 0.000000 0.000000 0.0000000 0.000000 1.917338 0.0000000 0.000000 0.00000
< [4,] 0.000000 7.641616 0.0000000 7.431033 0.000000 0.0000000 1.226005 2.50203
< [5,] 0.000000 0.000000 0.0000000 0.000000 0.000000 0.0000000 4.246199 133.43226
< [6,] 0.000000 0.000000 0.0000000 0.000000 0.000000 0.0000000 0.000000 0.00000
< [,9] [,10] [,11] [,12] [,13] [,14] [,15]
< [1,] 0.000000 0.00000 0.00000 16.114352 1.067697 0.0000000 2.4621550
< [2,] 0.000000 0.00000 76.90005 3.669480 10.747353 0.0000000 1.2479732
< [3,] 5.008095 0.00000 210.25975 11.510450 10.088479 0.8249231 0.0000000
< [4,] 4.127893 11.49375 0.00000 0.000000 0.000000 1.0273839 0.7723558
< [5,] 0.000000 132.25835 0.00000 0.000000 0.000000 0.7983795 0.0000000
< [6,] 0.000000 0.00000 0.00000 0.000000 0.000000 0.0000000 0.0000000
< [,16] [,17] [,18] [,19] [,20] [,21] [,22]
< [1,] 404.77006 0.00000 0.000000 14.293932 0.000000 0.0000000 0.0000000
< [2,] 18.37914 0.00000 0.000000 34.194179 18.529776 0.5065730 0.0000000
< [3,] 629.79230 111.85943 2.408593 1.659012 27.030585 0.4346852 9.3162361
< [4,] 24.21707 74.13104 3.245327 0.000000 27.118610 9.7971664 1.3382692
< [5,] 0.00000 189.14433 10.625716 0.000000 7.639151 13.3715840 0.4080339
< [6,] 0.00000 117.29613 75.761545 0.000000 0.000000 0.0000000 0.9696681
< [,23] [,24] [,25] [,26] [,27] [,28] [,29]
< [1,] 3.742722 117.846797 0.00000 17.746354 0.00000 46.90037 180.60396
< [2,] 9.335207 8.234485 2049.42600 5.588148 112.79785 297.62772 12.07152
< [3,] 4.942101 84.624630 411.04391 328.606127 36.18205 22.85135 49.92621
< [4,] 11.273176 191.336873 204.17389 414.441155 44.73360 95.55711 30.24864
< [5,] 0.000000 73.912533 62.82606 454.084705 261.20638 414.46889 491.24911
< [6,] 0.000000 0.000000 227.11787 0.000000 76.67068 0.00000 0.00000
< [,30] [,31] [,32] [,33] [,34] [,35] [,36]
< [1,] 0.000000 47.30188 0.00000 97.87547 222.36210 0.00000 0.00000
< [2,] 110.090093 21.13939 10.61622 17.21054 53.76702 233.63942 1656.27034
< [3,] 24.489433 17.95486 152.77406 17.69959 22.70054 108.81513 178.63860
< [4,] 18.483465 458.77657 39.91524 25.12831 184.98906 521.93070 39.44583
< [5,] 2.666152 13.99799 21.90943 77.66741 61.61892 96.62585 107.92777
< [6,] 181.893560 0.00000 27.83839 0.00000 0.00000 386.97618 69.13117
< [,37] [,38] [,39] [,40] [,41] [,42] [,43]
< [1,] 0.0000000 0.000000 0.000000 0.000000 0.0000000 42.552969 27.020252
< [2,] 2.0647670 0.000000 1.461478 8.302922 0.0000000 49.356417 10.292344
< [3,] 0.2479373 24.991796 71.286942 205.565205 0.0000000 25.260545 20.218030
< [4,] 12.2535634 4.423816 79.126488 121.704707 1.5342782 45.695176 96.290125
< [5,] 16.1390567 35.053947 62.037275 4.293593 2.0138717 8.033447 1.742639
< [6,] 39.5206010 9.291958 205.161449 19.097061 0.7266735 61.728008 0.000000
< [,44] [,45] [,46] [,47] [,48] [,49] [,50]
< [1,] 39.19175 173.65178 100.57784 0.9178039 690.78643 174.87200 9.387567
< [2,] 47.29697 12.34866 97.86322 56.5982909 17.88326 99.51760 1.777608
< [3,] 90.15853 98.50766 29.27570 114.4679393 73.83470 115.62320 1155.097033
< [4,] 13.39846 287.37243 19.61378 2.2788405 100.57182 35.04471 55.116843
< [5,] 16.41217 73.69507 335.14243 17.1360259 14.84251 223.42515 1332.808286
< [6,] 77.36659 30.34135 41.82828 9.8310081 183.61397 215.05959 6.160425
< [,51] [,52] [,53] [,54] [,55] [,56] [,57]
< [1,] 0.00000 0.09669243 8.171236 37.41091 0.000000 0.7199853 0.0000000
< [2,] 5.44301 1.18816242 79.494097 23.09044 0.000000 0.0000000 0.0000000
< [3,] 84.26024 26.49854856 68.599066 38.35432 0.000000 0.0000000 0.7297678
< [4,] 72.36569 14.82499277 114.493929 20.40709 0.000000 0.0000000 0.0000000
< [5,] 24.67681 21.91348340 69.902641 96.19975 3.581301 0.0000000 0.0000000
< [6,] 100.32752 19.85900594 93.736155 44.97300 0.000000 0.0000000 0.0000000
< [,58] [,59] [,60] [,61] [,62] [,63] [,64]
< [1,] 0.7816822 0.000000 0.00000 0.000000 0.00000 0.0000000 1.6928078
< [2,] 0.0000000 0.000000 0.00000 0.000000 0.00000 0.1576566 3.7789406
< [3,] 0.0000000 1.813345 0.00000 0.000000 0.00000 3.3496028 0.5398905
< [4,] 0.0000000 0.000000 0.00000 5.108075 8.01893 0.0000000 0.0000000
< [5,] 0.0000000 0.000000 88.43011 2.593398 43.36609 0.0000000 0.0000000
< [6,] 0.0000000 0.000000 21.42454 0.000000 0.00000 0.0000000 0.0000000
< [,65] [,66] [,67] [,68] [,69] [,70]
< [1,] 0.0000000 0.00000000 0.00000 0.000000 1.009815 0.000000
< [2,] 0.0000000 0.05427758 15.90610 3.422164 2.168591 43.230869
< [3,] 0.0000000 0.05835279 103.75869 481.296663 6.161695 30.240892
< [4,] 0.1205254 2.36815218 118.47107 118.624070 15.612821 1.419593
< [5,] 0.5609956 0.00000000 11.97633 33.775231 120.410479 9.954951
< [6,] 1.2115378 0.00000000 0.00000 0.000000 0.000000 21.422813
< [,71] [,72] [,73] [,74] [,75] [,76] [,77]
< [1,] 1308.6496123 0.000000 0.00000 9.318974 0.000000 0.0000000 21.22854
< [2,] 0.7524565 0.000000 0.00000 0.000000 4.296438 0.0000000 0.00000
< [3,] 13.9906766 0.000000 0.00000 0.000000 0.000000 0.0000000 0.00000
< [4,] 111.5255860 0.000000 0.00000 0.000000 0.000000 0.0000000 0.00000
< [5,] 358.6049888 7.329645 36.35061 0.000000 0.000000 0.0000000 0.00000
< [6,] 0.0000000 0.000000 0.00000 0.000000 0.000000 0.2721411 0.00000
< [,78] [,79] [,80] [,81] [,82] [,83] [,84]
< [1,] 0.000000 0.000000 0.000000 0.0000000 0.0000000 0.000000 0.000000
< [2,] 0.000000 0.000000 0.000000 0.0000000 0.1548590 0.000000 0.000000
< [3,] 0.000000 2.206035 0.000000 1.7816330 0.7675381 0.994798 0.000000
< [4,] 0.000000 0.958365 0.000000 0.5883424 0.0000000 64.694774 0.000000
< [5,] 0.000000 0.000000 4.073145 0.0000000 0.0000000 0.000000 5.592687
< [6,] 1.388640 0.000000 2.232838 0.0000000 0.0000000 0.000000 5.749486
< [,85] [,86] [,87] [,88] [,89] [,90] [,91]
< [1,] 0.0000000 4.114957 0.000000 0.00000 5.709805 42.39022 0.0000000
< [2,] 0.0000000 119.898059 0.000000 12.88441 8.800875 31.64993 0.9771071
< [3,] 0.0000000 62.177392 144.153967 764.64149 16.274348 0.00000 1.0832240
< [4,] 0.7237579 0.000000 39.056608 22.09675 0.000000 47.00413 3.4273383
< [5,] 0.2872582 0.000000 4.721707 19.18309 1.444741 0.00000 0.0000000
< [6,] 0.0000000 0.000000 0.000000 0.00000 0.000000 0.00000 6.3784003
< [,92] [,93] [,94] [,95] [,96] [,97] [,98]
< [1,] 0.000000 0.0000000 2.13847796 0.00000 0.000000 0.000000 0.00000
< [2,] 0.000000 0.0000000 0.56435938 19.81987 0.000000 0.000000 0.00000
< [3,] 1.128364 2.1739064 0.96561250 0.00000 0.918697 1.009141 0.00000
< [4,] 2.121247 12.0661980 0.00000000 0.00000 0.000000 0.000000 0.00000
< [5,] 20.916254 0.4321756 0.00000000 0.00000 0.000000 0.000000 10.57284
< [6,] 6.226726 4.5634477 0.07314588 0.00000 0.000000 0.000000 0.00000
< [,99] [,100] [,101] [,102] [,103] [,104] [,105]
< [1,] 0.000000 0.000000 0.000000 0.00000 0.00000 20.839191 95.213457
< [2,] 0.000000 0.000000 0.000000 0.00000 0.00000 1.886432 16.094048
< [3,] 2.634697 1.225024 2.627345 0.00000 0.00000 4.057996 8.075579
< [4,] 0.000000 1.247676 15.378599 57.85269 19.39789 0.000000 0.000000
< [5,] 0.000000 0.000000 0.000000 19.39938 28.51305 0.000000 0.000000
< [6,] 0.000000 0.000000 0.000000 0.00000 0.00000 0.000000 0.000000
< [,106] [,107] [,108] [,109] [,110] [,111] [,112]
< [1,] 0.0000000 0.000000 0.0000 0.00000 2.477603 0.0000 1.426660
< [2,] 2.3226696 0.000000 0.0000 0.00000 4.015401 134.4282 2.171732
< [3,] 0.3911095 0.000000 0.0000 0.00000 0.000000 199.3773 10.125075
< [4,] 1.9842398 4.952285 179.5948 22.80712 0.000000 482.6783 0.000000
< [5,] 0.0000000 25.439071 270.5018 106.15868 2.054041 0.0000 0.000000
< [6,] 0.0000000 5.020272 274.2270 38.39652 0.000000 0.0000 0.000000
< [,113] [,114] [,115] [,116] [,117] [,118] [,119]
< [1,] 0.00000 0.000000 0.000000 0.000000 4.531725 0.000000 0.00000
< [2,] 0.00000 1.360849 0.000000 5.104558 5.262544 0.000000 0.00000
< [3,] 14.07446 1.361295 0.000000 38.983152 10.434349 0.000000 0.00000
< [4,] 89.43248 0.000000 7.147122 11.642397 0.000000 4.930563 12.11852
< [5,] 75.67227 1.501258 5.822955 0.000000 0.000000 68.353075 0.00000
< [6,] 0.00000 0.000000 10.483027 0.000000 0.000000 17.950195 0.00000
< [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127]
< [1,] 0.00000 0.000000 0.000000 0.0000000 0.000000 0.000000 6.527887 1.093191
< [2,] 0.00000 0.000000 0.000000 0.0000000 5.520493 0.000000 1.258643 0.000000
< [3,] 0.00000 0.000000 0.000000 0.0000000 5.380504 0.000000 0.000000 0.000000
< [4,] 0.00000 0.000000 0.451558 0.0000000 0.000000 0.000000 0.000000 0.000000
< [5,] 0.00000 0.000000 0.000000 0.0000000 0.000000 17.847963 0.000000 10.652646
< [6,] 14.78211 4.114816 0.000000 0.8339799 0.000000 5.015895 0.000000 0.000000
< [,128] [,129] [,130] [,131] [,132] [,133] [,134] [,135]
< [1,] 0.000000 1.617859 0.0000000 0.000000 0.0000000 0.000000 0.000000 0.00000
< [2,] 0.000000 1.670227 0.0000000 0.000000 0.0000000 0.000000 0.000000 0.00000
< [3,] 0.000000 0.000000 0.0000000 0.000000 0.0000000 7.442317 8.463187 0.00000
< [4,] 0.000000 0.000000 0.0000000 0.000000 0.0000000 11.516873 9.638161 0.00000
< [5,] 2.862917 0.000000 0.2387701 2.056186 0.9576457 0.000000 0.000000 36.26947
< [6,] 1.827326 0.000000 1.5242211 1.874754 1.7932307 0.000000 0.000000 32.01422
< [,136] [,137] [,138] [,139] [,140] [,141] [,142] [,143]
< [1,] 0.00000 6.7065902 0.000000 0.000000 0.000000 0.00000 0.00000 0.000000
< [2,] 0.00000 0.4898873 0.000000 0.000000 0.000000 0.00000 0.00000 3.211786
< [3,] 0.00000 0.0000000 0.000000 0.000000 0.000000 0.00000 0.00000 0.000000
< [4,] 23.27923 0.0000000 1.908978 1.234937 0.000000 42.65477 0.00000 0.000000
< [5,] 559.43808 0.0000000 0.000000 0.000000 1.459762 0.00000 23.20276 0.000000
< [6,] 0.00000 0.0000000 0.000000 0.000000 0.000000 0.00000 0.00000 0.000000
< [,144]
< [1,] 0.0000000
< [2,] 0.0000000
< [3,] 0.0000000
< [4,] 0.4583918
< [5,] 0.0000000
< [6,] 0.0000000
< > # regpop.sar(x, p.nb=0.046)
< > example(toprab)
<
< toprab> data(siskiyou)
< Read 144 items
<
< toprab> x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb,
< toprab+ distance="none")
<
< toprab> toprab(x)
< [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
< V1 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V2 FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
< V4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V5 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
< V1 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V2 TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
< V3 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V4 FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V5 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
< V6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36]
< V1 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V2 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V3 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V4 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V5 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48]
< V1 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V2 TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V3 TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V4 TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V5 TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
< V6 FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< [,49] [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60]
< V1 TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
< V2 TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V3 TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
< V4 TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
< V5 TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
< V6 TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
< [,61] [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72]
< V1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V2 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
< V3 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V4 FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE
< V5 FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
< V6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
< [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84]
< V1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V3 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V4 FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
< V5 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< [,85] [,86] [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96]
< V1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V3 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
< V4 FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V5 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
< V6 FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE
< [,97] [,98] [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107]
< V1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V4 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V5 FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
< [,108] [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [,118]
< V1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V4 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V5 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V6 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< [,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127] [,128] [,129]
< V1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V5 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< V6 FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
< [,130] [,131] [,132] [,133] [,134] [,135] [,136] [,137] [,138] [,139] [,140]
< V1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
< V5 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
< V6 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
< [,141] [,142] [,143] [,144]
< V1 FALSE FALSE FALSE FALSE
< V2 FALSE FALSE FALSE FALSE
< V3 FALSE FALSE FALSE FALSE
< V4 FALSE FALSE FALSE FALSE
< V5 FALSE FALSE FALSE FALSE
< V6 TRUE TRUE TRUE TRUE
< > example(prab.sarestimate)
<
< prb.sr> data(siskiyou)
< Read 144 items
<
< prb.sr> x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb,
< prb.sr+ distance="none")
<
< prb.sr> # Not run; this needs package spdep
< prb.sr> # prab.sarestimate(x)
< prb.sr> prab.sarestimate(x, sar=FALSE)
< $sar
< [1] FALSE
---
> smmry.> set.seed(5678)
4893,5065c2511,2576
< $intercept
< (Intercept)
< 1.098612
<
< $sigma
< [1] 1.246732
<
< $regeffects
< region2 region3 region4 region5 region6
< 0.0000000 -0.0735519 0.4943188 0.6816018 0.6024660 0.7939467
<
< $speffects
< species2 species3 species4 species5 species6
< 0.00000000 -1.09861229 -0.40546511 -0.40546511 -0.16595660 -0.16595660
< species7 species8 species9 species10 species11 species12
< 0.52719058 0.18061699 -0.36868916 2.46711263 2.47182359 2.30603240
< species13 species14 species15 species16 species17 species18
< 0.80632038 -0.54572073 -1.00781885 2.85032035 3.11413912 -0.14604073
< species19 species20 species21 species22 species23 species24
< -1.25953674 0.83430696 0.08007334 -2.00563661 -0.65161151 2.63456369
< species25 species26 species27 species28 species29 species30
< 4.32595607 3.37032020 2.71941959 2.62770794 2.74066893 1.55139695
< species31 species32 species33 species34 species35 species36
< 1.84436460 2.92939591 1.70624986 2.91887665 2.58579034 2.74787823
< species37 species38 species39 species40 species41 species42
< 1.04336308 0.96178662 1.40338976 1.94121161 -1.83206513 1.64400594
< species43 species44 species45 species46 species47 species48
< 1.66445642 2.26605941 2.04572645 2.30031149 1.10246471 3.38612119
< species49 species50 species51 species52 species53 species54
< 3.26380915 2.73126184 2.55094955 -0.12882218 1.97614461 2.30456320
< species55 species56 species57 species58 species59 species60
< -0.33191320 -1.02506039 -1.02506039 -1.02506039 -0.33191320 2.21389256
< species61 species62 species63 species64 species65 species66
< -0.61584854 -0.41311599 -1.30899573 -2.30797805 -2.30797805 -1.74512017
< species67 species68 species69 species70 species71 species72
< 1.28949372 1.95713873 1.33181909 0.67159978 2.09370698 -1.59293106
< species73 species74 species75 species76 species77 species78
< -0.89978388 -1.59293106 0.48651048 -1.59293106 1.04612626 -0.49431878
< species79 species80 species81 species82 species83 species84
< -1.68657256 -1.68657256 -0.99342538 -2.49129151 1.77326937 0.65449306
< species85 species86 species87 species88 species89 species90
< -2.10515006 1.52821384 1.33911113 1.82842667 0.39980024 2.59714826
< species91 species92 species93 species94 species95 species96
< -0.17442153 -0.42344571 0.15601743 -1.52282842 0.52237104 -0.39391969
< species97 species98 species99 species100 species101 species102
< -1.78021405 -1.08706687 -1.78021405 -1.85221796 0.56193891 -0.07454393
< species103 species104 species105 species106 species107 species108
< -0.19512496 -0.41023887 1.53796182 -0.56165729 0.29332583 3.47629445
< species109 species110 species111 species112 species113 species114
< 1.38763698 0.44999370 3.22454930 -0.48727611 1.82138657 0.17443418
< species115 species116 species117 species118 species119 species120
< -0.77644297 1.03934135 1.90288679 2.20716676 -0.60246603 0.09068115
< species121 species122 species123 species124 species125 species126
< 0.69681695 -1.70107832 -1.00793114 -0.47728998 0.04762109 0.36854803
< species127 species128 species129 species130 species131 species132
< -0.16777037 -0.82386357 0.54424697 -0.64552610 -0.47728998 -1.45024505
< species133 species134 species135 species136 species137 species138
< -0.82386357 0.92222101 3.17482387 1.70106444 -0.29895251 -1.19941178
< species139 species140 species141 species142 species143 species144
< -1.19941178 -1.19941178 0.81549124 1.32631686 -0.28312105 -1.19941178
<
< $lmobject
<
< Call:
< lm(formula = logabund ~ region + species, data = abundreg)
<
< Coefficients:
< (Intercept) region2 region3 region4 region5 region6
< 1.09861 -0.07355 0.49432 0.68160 0.60247 0.79395
< species2 species3 species4 species5 species6 species7
< -1.09861 -0.40547 -0.40547 -0.16596 -0.16596 0.52719
< species8 species9 species10 species11 species12 species13
< 0.18062 -0.36869 2.46711 2.47182 2.30603 0.80632
< species14 species15 species16 species17 species18 species19
< -0.54572 -1.00782 2.85032 3.11414 -0.14604 -1.25954
< species20 species21 species22 species23 species24 species25
< 0.83431 0.08007 -2.00564 -0.65161 2.63456 4.32596
< species26 species27 species28 species29 species30 species31
< 3.37032 2.71942 2.62771 2.74067 1.55140 1.84436
< species32 species33 species34 species35 species36 species37
< 2.92940 1.70625 2.91888 2.58579 2.74788 1.04336
< species38 species39 species40 species41 species42 species43
< 0.96179 1.40339 1.94121 -1.83207 1.64401 1.66446
< species44 species45 species46 species47 species48 species49
< 2.26606 2.04573 2.30031 1.10246 3.38612 3.26381
< species50 species51 species52 species53 species54 species55
< 2.73126 2.55095 -0.12882 1.97614 2.30456 -0.33191
< species56 species57 species58 species59 species60 species61
< -1.02506 -1.02506 -1.02506 -0.33191 2.21389 -0.61585
< species62 species63 species64 species65 species66 species67
< -0.41312 -1.30900 -2.30798 -2.30798 -1.74512 1.28949
< species68 species69 species70 species71 species72 species73
< 1.95714 1.33182 0.67160 2.09371 -1.59293 -0.89978
< species74 species75 species76 species77 species78 species79
< -1.59293 0.48651 -1.59293 1.04613 -0.49432 -1.68657
< species80 species81 species82 species83 species84 species85
< -1.68657 -0.99343 -2.49129 1.77327 0.65449 -2.10515
< species86 species87 species88 species89 species90 species91
< 1.52821 1.33911 1.82843 0.39980 2.59715 -0.17442
< species92 species93 species94 species95 species96 species97
< -0.42345 0.15602 -1.52283 0.52237 -0.39392 -1.78021
< species98 species99 species100 species101 species102 species103
< -1.08707 -1.78021 -1.85222 0.56194 -0.07454 -0.19512
< species104 species105 species106 species107 species108 species109
< -0.41024 1.53796 -0.56166 0.29333 3.47629 1.38764
< species110 species111 species112 species113 species114 species115
< 0.44999 3.22455 -0.48728 1.82139 0.17443 -0.77644
< species116 species117 species118 species119 species120 species121
< 1.03934 1.90289 2.20717 -0.60247 0.09068 0.69682
< species122 species123 species124 species125 species126 species127
< -1.70108 -1.00793 -0.47729 0.04762 0.36855 -0.16777
< species128 species129 species130 species131 species132 species133
< -0.82386 0.54425 -0.64553 -0.47729 -1.45025 -0.82386
< species134 species135 species136 species137 species138 species139
< 0.92222 3.17482 1.70106 -0.29895 -1.19941 -1.19941
< species140 species141 species142 species143 species144
< -1.19941 0.81549 1.32632 -0.28312 -1.19941
<
<
< > # prab.sarestimate(x)
< > example(specgroups)
<
< spcgrp> data(siskiyou)
< Read 144 items
<
< spcgrp> x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb,
< spcgrp+ distance="logkulczynski")
<
< spcgrp> groupvector <- as.factor(siskiyou.groups)
<
< spcgrp> ng <- length(levels(groupvector))
<
< spcgrp> lg <- levels(groupvector)
<
< spcgrp> nsg <- numeric(0)
<
< spcgrp> for (i in 1:ng) nsg[i] <- sum(groupvector==lg[i])
<
< spcgrp> groupinfo <- list(lg=lg,ng=ng,nsg=nsg)
<
< spcgrp> specgroups(x$distmat,groupvector,groupinfo)
< $overall
< [1] 0.5864469
<
< $gr
< [1] NaN NaN NaN NaN NaN NaN 0.7896545
< [8] NaN NaN NaN NaN NaN NaN NaN
< [15] 0.2777786 NaN NaN NaN 1.0000000 NaN 0.4815124
< [22] NaN 0.3530351 NaN 0.2257266 NaN 0.7590897 NaN
< [29] NaN NaN NaN 0.5353901 NaN NaN NaN
< [36] NaN NaN NaN NaN 0.7529284 NaN NaN
< [43] NaN NaN NaN NaN 0.4841911 NaN NaN
< [50] NaN 0.9123321 NaN NaN NaN NaN NaN
< [57] NaN 0.8442210 NaN NaN NaN NaN 0.2595207
< [64] NaN NaN NaN NaN NaN NaN 0.7163128
< [71] NaN NaN NaN NaN 0.4578068 NaN 0.4292842
< [78] 0.4431906 NaN NaN 0.5259369 NaN NaN NaN
< [85] NaN NaN 0.4325096 NaN NaN NaN NaN
< [92] NaN NaN NaN 0.6856382 0.2832452 NaN NaN
< [99] NaN NaN NaN NaN NaN NaN NaN
< [106] NaN NaN 0.5829038 NaN
<
< > # example(abundtest)
< > set.seed(1234)
< > # This is done in example(specgroups):
< > # x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb,
< > # distance="logkulczynski")
< > # a1 <- abundtest(x, times=3, p.nb=0.0465)
< > # a2 <- abundtest(x, times=3, p.nb=0.0465, teststat="groups",
< > # groupvector=siskiyou.groups)
< > # summary(a1)
< > # summary(a2)
< > # build.nblist(x)
---
> smmry.> kprab <- prabinit(prabmatrix = kykladspecreg, neighborhood = nb)
>
> smmry.> 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
>
> smmry.> 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
OK
checking PDF version of manual ... OK

Time 1:21.71, 50.94 + 3.98