
R Under development (unstable) (2024-09-11 r87117) -- "Unsuffered Consequences"
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> library(prabclus)
Loading required package: MASS
Loading required package: mclust
Package 'mclust' version 6.1.1
Type 'citation("mclust")' for citing this R package in publications.
> options(digits=4)
> 
> data(kykladspecreg)
> data(nb)
> set.seed(1234)
> x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb)
> 
> p1 <- prabtest(x, times=3, pd=0.35, ignore.richness=TRUE)
Simulation run  1 statistics value= 0.5811 
Simulation run  2 statistics value= 0.4761 
Simulation run  3 statistics value= 0.5022 
Data value:  0.408 
> p2 <- prabtest(x, times=3, pd=0.35, teststat="lcomponent")
Simulation run  1 statistics value= 9 
Simulation run  2 statistics value= 19 
Simulation run  3 statistics value= 43 
Data value:  8 
> p3 <- prabtest(x, times=3, pd=0.35, teststat="isovertice")
Simulation run  1 statistics value= 9 
Simulation run  2 statistics value= 9 
Simulation run  3 statistics value= 23 
Data value:  15 
> p4 <- prabtest(x, times=3, pd=0.35, teststat="nn", sf.sim=TRUE)
Simulation run  1 statistics value= 0.3598 
Simulation run  2 statistics value= 0.3635 
Simulation run  3 statistics value= 0.3441 
Data value:  0.3192 
> p5 <- prabtest(x, times=3, pd=0.35, teststat="inclusions")
Simulation run  1 statistics value= 470 
Simulation run  2 statistics value= 547 
Simulation run  3 statistics value= 365 
Data value:  602 
> summary(p1)
* Parametric bootstrap test for presence-absence data *

Test statistics:  distratio , Tuning constant= 0.25 
Distance:  kulczynski 
Simulation runs:  3 
Disjunction parameter for presence-absence pattern:  0.35 
Rows (regions) richness has been ignored by the null model.
Statistics value for original data:  0.408 
Mean for null data:  0.5198 , range:  0.4761 0.5811 
p=  0.25 
> summary(p2)
* Parametric bootstrap test for presence-absence data *

Test statistics:  lcomponent , Tuning constant= 60 
Distance:  kulczynski 
Simulation runs:  3 
Disjunction parameter for presence-absence pattern:  0.35 
Statistics value for original data:  8 
Mean for null data:  23.67 , range:  9 43 
p=  0.25 
> summary(p3)
* Parametric bootstrap test for presence-absence data *

Test statistics:  isovertice , Tuning constant= 80 
Distance:  kulczynski 
Simulation runs:  3 
Disjunction parameter for presence-absence pattern:  0.35 
Statistics value for original data:  15 
Mean for null data:  13.67 , range:  9 23 
p=  1 
> summary(p4)
* Parametric bootstrap test for presence-absence data *

Test statistics:  nn , Tuning constant= 4 
Distance:  kulczynski 
Simulation runs:  3 
Disjunction parameter for presence-absence pattern:  0.35 
Statistics value for original data:  0.3192 
Mean for null data:  0.3558 , range:  0.3441 0.3635 
p=  0.25 
> summary(p5)
* Parametric bootstrap test for presence-absence data *

Test statistics:  inclusions , Tuning constant= NA 
Distance:  kulczynski 
Simulation runs:  3 
Disjunction parameter for presence-absence pattern:  0.35 
Statistics value for original data:  602 
Mean for null data:  460.7 , range:  365 547 
p=  0.25 
> 
> data(veronica)
> vnb <- coord2dist(coordmatrix=veronica.coord[1:50,], cut=20,
+                   file.format="decimal2",neighbors=TRUE)
> vei <- prabinit(prabmatrix=veronica[1:50,],
+                 neighborhood=vnb$nblist,nbbetweenregions=FALSE,
+                 distance="jaccard")
> print(vei)
Presence-absence matrix object with
50  species and  583  regions,
including species/individuals neighborhoods and 
between-species distance matrix of type  jaccard .
> 
> # library(spdep)
> data(siskiyou)
> x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb,
+             distance="logkulczynski")
> build.nblist(x)
> a1 <- abundtest(x, times=5, p.nb=0.0465)
Simulation run  1 statistics value= 0.2931 
Simulation run  2 statistics value= 0.3109 
Simulation run  3 statistics value= 0.2936 
Simulation run  4 statistics value= 0.3119 
Simulation run  5 statistics value= 0.3048 
Data value:  0.2789 
> a2 <- abundtest(x, times=5, p.nb=0.0465, teststat="groups",
+                 groupvector=siskiyou.groups)
Simulation run  1 statistics value= 0.4916 
Simulation run  2 statistics value= 0.5779 
Simulation run  3 statistics value= 0.5337 
Simulation run  4 statistics value= 0.5873 
Simulation run  5 statistics value= 0.617 
Data value:  0.5864 
> # These settings are chosen to make the example execution
> # faster; usually you will use abundtest(x).
> summary(a1)
* Parametric bootstrap test for spatial abundance data *

Test statistics:  distratio , Tuning constant= 0.25 
Distance:  logkulczynski 
Simulation runs:  5 
Disjunction parameter for presence-absence pattern:  0.0465 
Neighborhood parameter lambda for SAR-model:  -0.05515 
Statistics value for original data:  0.2789 
Mean for null data:  0.3029 , range:  0.2931 0.3119 
p=  0.1667 
> summary(a2)
* Parametric bootstrap test for spatial abundance data *

Test statistics:  groups , Tuning constant= NA 
Distance:  logkulczynski 
Simulation runs:  5 
Disjunction parameter for presence-absence pattern:  0.0465 
Neighborhood parameter lambda for SAR-model:  -0.05515 
Mean within group distances for original data:  0.5864 
Mean of mean within group distances for null data:  0.5615 , range:  0.4916 0.617 
p=  0.5 
  Group  7  statistics value for original data:  0.7897 
  Group  7  mean for null data:  0.4531 , range:  0.4156 0.4727 
  p=  0.1667 
  Group  16  statistics value for original data:  0.2778 
  Group  16  mean for null data:  0.3438 , range:  0.2231 0.4107 
  p=  0.8333 
  Group  21  statistics value for original data:  1 
  Group  21  mean for null data:  0.6664 , range:  0.3384 1 
  p=  0.5 
  Group  24  statistics value for original data:  0.4815 
  Group  24  mean for null data:  0.3202 , range:  0.2768 0.3746 
  p=  0.1667 
  Group  27  statistics value for original data:  0.353 
  Group  27  mean for null data:  0.6447 , range:  0.3722 1 
  p=  1 
  Group  30  statistics value for original data:  0.2257 
  Group  30  mean for null data:  0.1347 , range:  0.06729 0.1949 
  p=  0.1667 
  Group  33  statistics value for original data:  0.7591 
  Group  33  mean for null data:  0.5463 , range:  0.4448 0.6791 
  p=  0.1667 
  Group  41  statistics value for original data:  0.5354 
  Group  41  mean for null data:  0.4502 , range:  0.3351 0.5298 
  p=  0.1667 
  Group  51  statistics value for original data:  0.7529 
  Group  51  mean for null data:  0.7072 , range:  0.6229 0.859 
  p=  0.5 
  Group  61  statistics value for original data:  0.4842 
  Group  61  mean for null data:  0.6812 , range:  0.4593 1 
  p=  0.5 
  Group  66  statistics value for original data:  0.9123 
  Group  66  mean for null data:  0.9564 , range:  0.8345 1 
  p=  0.8333 
  Group  74  statistics value for original data:  0.8442 
  Group  74  mean for null data:  0.546 , range:  0.3173 1 
  p=  0.3333 
  Group  80  statistics value for original data:  0.2595 
  Group  80  mean for null data:  0.3406 , range:  0.2541 0.3817 
  p=  0.8333 
  Group  88  statistics value for original data:  0.7163 
  Group  88  mean for null data:  0.6213 , range:  0.3689 0.7709 
  p=  0.5 
  Group  96  statistics value for original data:  0.4578 
  Group  96  mean for null data:  0.4455 , range:  0.417 0.4682 
  p=  0.5 
  Group  99  statistics value for original data:  0.4293 
  Group  99  mean for null data:  0.7328 , range:  0.5584 0.8119 
  p=  1 
  Group  103  statistics value for original data:  0.4432 
  Group  103  mean for null data:  0.6986 , range:  0.4646 1 
  p=  1 
  Group  107  statistics value for original data:  0.5259 
  Group  107  mean for null data:  0.808 , range:  0.5826 1 
  p=  1 
  Group  114  statistics value for original data:  0.4325 
  Group  114  mean for null data:  0.4392 , range:  0.3879 0.4892 
  p=  0.6667 
  Group  125  statistics value for original data:  0.6856 
  Group  125  mean for null data:  0.3745 , range:  0.268 0.4779 
  p=  0.1667 
  Group  127  statistics value for original data:  0.2832 
  Group  127  mean for null data:  0.1782 , range:  0.1558 0.2164 
  p=  0.1667 
  Group  140  statistics value for original data:  0.5829 
  Group  140  mean for null data:  0.5161 , range:  0.3774 0.6366 
  p=  0.3333 
> 
> options(digits=2)
> prab.sarestimate(x)
$sar
[1] TRUE

$intercept
(Intercept) 
        1.1 

$sigma
[1] 1

$regeffects
        region2 region3 region4 region5 region6 
  0.000  -0.056   0.517   0.682   0.597   0.834 

$speffects
             species2   species3   species4   species5   species6   species7 
    0.0000    -1.0986    -0.4055    -0.4055    -0.1746    -0.1746     0.5186 
  species8   species9  species10  species11  species12  species13  species14 
    0.1720    -0.3773     2.4585     2.4632     2.3148     0.7966    -0.5523 
 species15  species16  species17  species18  species19  species20  species21 
   -0.9997     2.8542     3.1175    -0.1624    -1.2164     0.8256     0.0025 
 species22  species23  species24  species25  species26  species27  species28 
   -2.0213    -0.6407     2.6430     4.3446     3.3794     2.7244     2.6713 
 species29  species30  species31  species32  species33  species34  species35 
    2.7303     1.5682     1.8833     2.9410     1.7488     2.9594     2.5965 
 species36  species37  species38  species39  species40  species41  species42 
    2.7883     1.0393     0.9960     1.4137     1.9796    -1.8561     1.6207 
 species43  species44  species45  species46  species47  species48  species49 
    1.7423     2.2793     2.0573     2.3259     1.0857     3.3730     3.2701 
 species50  species51  species52  species53  species54  species55  species56 
    2.7610     2.6443    -0.1027     1.9857     2.3219    -0.3492    -1.0423 
 species57  species58  species59  species60  species61  species62  species63 
   -1.0423    -1.0423    -0.3492     2.1940    -0.6358    -0.4330    -1.3289 
 species64  species65  species66  species67  species68  species69  species70 
   -2.2767    -2.3501    -1.7023     1.3365     1.9931     1.3771     0.7127 
 species71  species72  species73  species74  species75  species76  species77 
    2.1431    -1.6155    -0.9223    -1.6155     0.4640    -1.6155     1.0236 
 species78  species79  species80  species81  species82  species83  species84 
   -0.5168    -1.6979    -1.6979    -1.0047    -2.5026     1.7620     0.6432 
 species85  species86  species87  species88  species89  species90  species91 
   -2.1138     1.5735     1.3492     1.8530     0.3853     2.6159    -0.2037 
 species92  species93  species94  species95  species96  species97  species98 
   -0.4353     0.1952    -1.5037     0.5223    -0.3940    -1.7803    -1.0871 
 species99 species100 species101 species102 species103 species104 species105 
   -1.7803    -1.8497     0.5645    -0.0720    -0.1926    -0.3926     1.5433 
species106 species107 species108 species109 species110 species111 species112 
   -0.5872     0.3140     3.4881     1.3971     0.4806     3.2341    -0.4859 
species113 species114 species115 species116 species117 species118 species119 
    1.8359     0.2072    -0.7822     1.0575     1.9314     2.2148    -0.5973 
species120 species121 species122 species123 species124 species125 species126 
    0.0959     0.7020    -1.6959    -1.0028    -0.4950     0.0299     0.3509 
species127 species128 species129 species130 species131 species132 species133 
   -0.1855    -0.8416     0.5266    -0.6632    -0.4950    -1.4679    -0.8416 
species134 species135 species136 species137 species138 species139 species140 
    0.9045     3.1571     1.6834    -0.3166    -1.2400    -1.2400    -1.2400 
species141 species142 species143 species144 
    0.7749     1.2858    -0.3237    -1.2400 

$lambda
lambda 
-0.078 

$size
[1] 421

$nbweight
[1] 0.7

$lmobject

Call:
spatialreg::errorsarlm(formula = logabund ~ region + species, 
    data = abundreg, listw = nblistw, method = sarmethod, quiet = quiet, 
    zero.policy = TRUE)
Type: error 

Coefficients:
     lambda (Intercept)     region2     region3     region4     region5 
    -0.0784      1.0986     -0.0563      0.5168      0.6817      0.5973 
    region6    species2    species3    species4    species5    species6 
     0.8345     -1.0986     -0.4055     -0.4055     -0.1746     -0.1746 
   species7    species8    species9   species10   species11   species12 
     0.5186      0.1720     -0.3773      2.4585      2.4632      2.3148 
  species13   species14   species15   species16   species17   species18 
     0.7966     -0.5523     -0.9997      2.8542      3.1175     -0.1624 
  species19   species20   species21   species22   species23   species24 
    -1.2164      0.8256      0.0025     -2.0213     -0.6407      2.6430 
  species25   species26   species27   species28   species29   species30 
     4.3446      3.3794      2.7244      2.6713      2.7303      1.5682 
  species31   species32   species33   species34   species35   species36 
     1.8833      2.9410      1.7488      2.9594      2.5965      2.7883 
  species37   species38   species39   species40   species41   species42 
     1.0393      0.9960      1.4137      1.9796     -1.8561      1.6207 
  species43   species44   species45   species46   species47   species48 
     1.7423      2.2793      2.0573      2.3259      1.0857      3.3730 
  species49   species50   species51   species52   species53   species54 
     3.2701      2.7610      2.6443     -0.1027      1.9857      2.3219 
  species55   species56   species57   species58   species59   species60 
    -0.3492     -1.0423     -1.0423     -1.0423     -0.3492      2.1940 
  species61   species62   species63   species64   species65   species66 
    -0.6358     -0.4330     -1.3289     -2.2767     -2.3501     -1.7023 
  species67   species68   species69   species70   species71   species72 
     1.3365      1.9931      1.3771      0.7127      2.1431     -1.6155 
  species73   species74   species75   species76   species77   species78 
    -0.9223     -1.6155      0.4640     -1.6155      1.0236     -0.5168 
  species79   species80   species81   species82   species83   species84 
    -1.6979     -1.6979     -1.0047     -2.5026      1.7620      0.6432 
  species85   species86   species87   species88   species89   species90 
    -2.1138      1.5735      1.3492      1.8530      0.3853      2.6159 
  species91   species92   species93   species94   species95   species96 
    -0.2037     -0.4353      0.1952     -1.5037      0.5223     -0.3940 
  species97   species98   species99  species100  species101  species102 
    -1.7803     -1.0871     -1.7803     -1.8497      0.5645     -0.0720 
 species103  species104  species105  species106  species107  species108 
    -0.1926     -0.3926      1.5433     -0.5872      0.3140      3.4881 
 species109  species110  species111  species112  species113  species114 
     1.3971      0.4806      3.2341     -0.4859      1.8359      0.2072 
 species115  species116  species117  species118  species119  species120 
    -0.7822      1.0575      1.9314      2.2148     -0.5973      0.0959 
 species121  species122  species123  species124  species125  species126 
     0.7020     -1.6959     -1.0028     -0.4950      0.0299      0.3509 
 species127  species128  species129  species130  species131  species132 
    -0.1855     -0.8416      0.5266     -0.6632     -0.4950     -1.4679 
 species133  species134  species135  species136  species137  species138 
    -0.8416      0.9045      3.1571      1.6834     -0.3166     -1.2400 
 species139  species140  species141  species142  species143  species144 
    -1.2400     -1.2400      0.7749      1.2858     -0.3237     -1.2400 

Log likelihood: -597 

> regpop.sar(x, p.nb=0.046)
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]  [,9] [,10] [,11] [,12] [,13]
[1,]  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.00     0     0     0   0.0
[2,]  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.87     0     0     0   0.0
[3,]  0.0  0.0  0.0  0.0  0.0  5.5  0.0  1.5 20.79    25    12    34   0.0
[4,]  0.0  1.7  0.0  0.0  2.7 18.2  0.0  2.1  0.00    23     0   155  42.2
[5,]  4.5  0.0  4.1  0.0  5.7  0.0  3.7  0.0  0.00     0    29    99   9.1
[6,]  0.0  0.0  0.0  3.5  0.0  0.0 78.4  0.0  0.00     0     0     0  13.1
     [,14] [,15] [,16] [,17] [,18] [,19]  [,20] [,21] [,22] [,23] [,24] [,25]
[1,]   0.0  0.00    46     0   1.6   4.3   0.00   4.4 0.028  0.74    76     0
[2,]  17.3  0.00    79    66   0.9   2.0   0.00   1.4 0.187  2.97    44    75
[3,]   2.1  0.00    79    16   7.5  52.3   3.37  10.6 0.258  4.15    96  5493
[4,]  16.0  6.72    76   256   4.3   0.0   8.84   1.5 0.308  7.90    81    54
[5,]   0.0  5.52     0   237   0.0   0.0   0.71   0.0 0.000  0.00    19   414
[6,]   0.0  0.39     0     0   0.0   0.0 102.84   0.0 0.000  0.00     0   246
     [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37]
[1,]     0     0    11  10.0  14.1   0.0   0.0  59.2   230    84    90   0.0
[2,]   126    63    94   9.4  20.1  54.3 120.7   6.3   107    70    34   6.8
[3,]   232   509    26 210.3   3.2 173.4   8.6  39.3   303   225    23  11.6
[4,]    89    20    32  23.8  32.9  64.1  80.0  29.1   486   111    71  15.8
[5,]    51    13    25  81.2  42.1  33.3  84.2  13.1    19    18   206   9.5
[6,]  1378   265     0   0.0   0.0   4.7  72.7   0.0     0     0     0  17.0
     [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49]
[1,]   0.0     0    42  0.00  24.2     0    19    62    26  53.3   201    34
[2,]   8.6    33    16  0.00   3.6    23    55    18   128   3.9    34   214
[3,]   1.7    20    39  0.00  45.9    55    41    24   125  17.7  1103    63
[4,]   3.7    21    28  0.37 111.8    72   123    42   118   5.3   489    66
[5,]  47.7    22    86  1.40   3.5    17   135   232    60  11.7    51    16
[6,]   0.0    22     0  0.43  38.1    44    26    21   104   7.1   473   236
     [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61]
[1,]    68     0  2.45   5.3  47.2   0.0   0.0   0.0     0     0     0   0.0
[2,]    22    62  5.73  83.3   7.4   0.0   0.0   0.0     0     0     0   0.0
[3,]  1120    61  3.87  38.7  58.9   0.0   0.0   4.2     0    10     0   0.0
[4,]    69   443  3.29  49.2  32.5   0.0   0.0   0.0     0     0     0   0.0
[5,]    99    91  0.91  46.9 251.6   0.0   6.5   0.0     0     0    13   1.6
[6,]   260   136  3.70  57.1 113.2   1.8   0.0   0.0     4     0    32   2.3
     [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73]
[1,]   0.0  0.00  0.16  0.00  0.00     0     0   0.0   0.0    41     0   0.0
[2,]   0.0  0.00  1.18  0.00  0.00     0    24   5.4   4.2    17     0   3.4
[3,]   0.0  0.00  6.63  0.50  0.40    47    30  41.4  21.7   119     0   0.0
[4,]   3.1  0.00  0.00  0.10  3.74    22   126  83.3   6.7    54     0   0.0
[5,]   4.7  0.61  0.00  0.38  0.44    57    20  43.7  69.3    16     3   0.0
[6,]   0.0  1.69  0.00  0.00  0.00    53     0  19.5  32.3     0     0   0.0
     [,74] [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85]
[1,]  0.00     0     0     0   0.0   0.0  0.00   0.0  0.00  10.6   0.0  0.00
[2,]  0.00     0     0     0   0.0   1.2  0.00   0.0  0.00   9.7  13.4  0.00
[3,]  0.00    13    11     0   3.2   1.0  0.00   2.0  0.00   0.0   6.9  0.00
[4,]  0.87     0     0     0   0.0   0.0  0.99   3.5  0.26   0.0   0.0  0.51
[5,]  0.00     0     0     0   0.0   0.0  0.73   0.0  1.47   0.0   0.0  0.61
[6,]  0.00     0     0    41   0.0   0.0  0.00   0.0  0.00   0.0   0.0  0.00
     [,86] [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97]
[1,]  12.6     0     0   0.0     0   2.6   0.0   1.2  3.62   0.0  0.47  0.00
[2,]   3.2     0    25   0.0     0   0.9   0.0   3.6  0.39   0.0  0.00  0.00
[3,]   9.6     0    11   2.2     0   9.9   1.0  12.3  1.84   0.0  0.00  0.00
[4,]   0.0    22    18  19.1   230  10.4   7.7  18.8  1.42   0.0  0.00  0.93
[5,]   0.0    39    51   4.2    52   0.0  12.8   0.0  0.00   0.0  0.00  0.00
[6,]   0.0    26     0   4.4    98   0.0   1.5   0.0  0.00   3.9  0.00  0.00
     [,98] [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] [,108]
[1,]   0.0  0.00   0.00    0.0    0.0   18.4    1.1      0    0.0    0.0      0
[2,]   0.0  0.00   0.00    0.0    9.8    3.3    5.4      0    1.5    0.0      0
[3,]   0.0  0.00   0.00   36.2   25.5    0.0    2.8      0    2.9   12.2      0
[4,]   3.8  0.00   0.82    8.2    0.0    0.0    0.0     41   14.6   25.8    432
[5,]   0.0  0.98   0.84    0.0    0.0    0.0    0.0     28    0.0    5.4   3135
[6,]   0.0  0.00   0.00    0.0    0.0    0.0    0.0     80    0.0    0.0    524
     [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [,118]
[1,]    0.0    0.0      0    0.0      0    0.0    0.0    8.8   11.6     39
[2,]    7.8    0.0      0    2.1      0    0.0    0.0    8.6    0.0     90
[3,]   10.0    0.0      0    0.8      0    7.1    5.6    8.0   31.6     27
[4,]   30.3   22.9     30    3.7    184    3.5   14.9    0.0    5.3      0
[5,]    0.0    2.4    116    0.0     16   13.0    1.4    0.0    0.0      0
[6,]    0.0    3.8     76    0.0     65    0.0    0.0    0.0    0.0      0
     [,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127] [,128]
[1,]   0.00    0.0      0    0.0    0.0    0.0    0.0    0.0    0.0    0.7
[2,]   0.46    0.0      0    0.0    5.8    0.0    0.0    0.0    8.5    1.7
[3,]   0.00    0.0      0    0.0    0.0    0.0    0.0    3.2    0.0    0.0
[4,]   0.00    0.0      0    0.0    0.0    0.0    0.0    5.2    0.0    0.0
[5,]   0.00    8.6      0    1.1    0.0    6.8    9.6    0.0    2.1    0.0
[6,]   0.00    0.0     19    0.0    0.0    2.9    6.9    0.0    0.0    0.0
     [,129] [,130] [,131] [,132] [,133] [,134] [,135] [,136] [,137] [,138]
[1,]    0.0   0.00   0.00   0.00   0.00    0.0      0      0   0.77    0.0
[2,]    3.2   0.55   2.30   1.33   0.00    0.0      0      0   1.48    0.0
[3,]    1.7   2.04   0.52   0.64   0.00    0.0      0     27   0.00    0.0
[4,]    0.0   0.00   0.00   0.00   0.96    4.0      0     26   0.00    2.4
[5,]    0.0   0.00   0.00   0.00   0.93    1.7     43      0   0.00    0.0
[6,]    0.0   0.00   0.00   0.00   0.00    0.0     45      0   0.00    0.0
     [,139] [,140] [,141] [,142] [,143] [,144]
[1,]      0    0.0    0.0      0    1.8   0.00
[2,]      0    0.0    0.0     11    0.0   0.00
[3,]      4    0.0    0.0      0    0.0   0.55
[4,]      0    0.0    5.9      0    0.0   0.00
[5,]      0    3.6    0.0      0    0.0   0.00
[6,]      0    0.0    0.0      0    0.0   0.00
> options(digits=4)
> 
> x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb, distance="none",toprab=TRUE,toprabp=0.5)
> x2 <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb, distance="none",toprab=TRUE,toprabp=0)
> x$prab
      [,1]  [,2]  [,3]  [,4]  [,5]  [,6]  [,7]  [,8]  [,9] [,10] [,11] [,12]
[1,] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[2,] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
[4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
     [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
[1,]  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[2,]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
[3,]  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[4,] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
[6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
     [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36]
[1,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[2,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[3,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[4,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[5,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
[6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
     [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48]
[1,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
[2,]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
[3,]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[4,]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[5,]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
[6,] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
     [,49] [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60]
[1,]  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[2,]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
[3,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
[4,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[5,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[6,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
     [,61] [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72]
[1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[2,]  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
[3,]  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
[4,] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
[5,] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
[6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
     [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84]
[1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
[4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
[5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
     [,85] [,86] [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96]
[1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[3,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
[4,] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
[5,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
[6,] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
     [,97] [,98] [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107]
[1,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[2,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[3,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[4,] FALSE FALSE FALSE   TRUE   TRUE   TRUE   TRUE  FALSE   TRUE   TRUE   TRUE
[5,] FALSE FALSE FALSE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE
[6,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE   TRUE   TRUE   TRUE   TRUE
     [,108] [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117]
[1,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[2,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[3,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[4,]   TRUE   TRUE   TRUE  FALSE   TRUE   TRUE   TRUE   TRUE   TRUE  FALSE
[5,]   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE
[6,]   TRUE   TRUE   TRUE   TRUE   TRUE  FALSE   TRUE   TRUE   TRUE   TRUE
     [,118] [,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127]
[1,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[2,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[3,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[4,]   TRUE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[5,]   TRUE  FALSE  FALSE  FALSE  FALSE  FALSE   TRUE   TRUE   TRUE   TRUE
[6,]   TRUE  FALSE  FALSE  FALSE  FALSE  FALSE   TRUE   TRUE   TRUE   TRUE
     [,128] [,129] [,130] [,131] [,132] [,133] [,134] [,135] [,136] [,137]
[1,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[2,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[3,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[4,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[5,]   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE
[6,]   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE
     [,138] [,139] [,140] [,141] [,142] [,143] [,144]
[1,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[2,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[3,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[4,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[5,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[6,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
> x2$prab
      [,1]  [,2]  [,3]  [,4]  [,5]  [,6]  [,7]  [,8]  [,9] [,10] [,11] [,12]
[1,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[2,] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
[4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
     [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
[1,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[2,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
[3,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[4,] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
[6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
     [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36]
[1,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[2,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[3,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[4,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[5,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
     [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48]
[1,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[2,]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[3,]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[4,]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[5,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
[6,] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
     [,49] [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60]
[1,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[2,]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[3,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
[4,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[5,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[6,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
     [,61] [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72]
[1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[2,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
[3,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[4,] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
[5,] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
[6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
     [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84]
[1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[3,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[4,] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
     [,85] [,86] [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96]
[1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[3,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
[4,] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[5,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
[6,] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
     [,97] [,98] [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107]
[1,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[2,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[3,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[4,]  TRUE  TRUE  TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE
[5,] FALSE FALSE FALSE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE
[6,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE   TRUE   TRUE   TRUE   TRUE
     [,108] [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117]
[1,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[2,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[3,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[4,]   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE
[5,]   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE
[6,]   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE
     [,118] [,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127]
[1,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[2,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[3,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[4,]   TRUE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[5,]   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE
[6,]   TRUE  FALSE  FALSE  FALSE  FALSE  FALSE   TRUE   TRUE   TRUE   TRUE
     [,128] [,129] [,130] [,131] [,132] [,133] [,134] [,135] [,136] [,137]
[1,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[2,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[3,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[4,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[5,]   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE
[6,]   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE
     [,138] [,139] [,140] [,141] [,142] [,143] [,144]
[1,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[2,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[3,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[4,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[5,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[6,]   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE   TRUE
> 
> 
> 
> proc.time()
   user  system elapsed 
  10.43    0.10   10.53 
