Check results for 'siatclust'

using R Under development (unstable) (2012-02-27 r58511)
using platform: x86_64-pc-mingw32 (64-bit)
using session charset: ISO8859-1
checking for file 'siatclust/DESCRIPTION' ... OK
this is package 'siatclust' version '1.0.5'
checking package namespace information ... OK
checking package dependencies ... OK
checking if this is a source package ... OK
checking if there is a namespace ... OK
checking whether package 'siatclust' can be installed ... OK
checking installed package size ... OK
checking package directory ... OK
checking for portable file names ... 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
loading checks for arch 'i386'
** 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 whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
loading checks for arch 'x64'
** 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 whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace 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 ... NOTE
plot.ewkm: warning in heatmap(x, col = cm.colors(256), scale =
"column", RowSideColors = rc, ColSideColors = cc, margin = c(5, 10),
xlab = "Cluster", ...): partial argument match of 'margin' to
'margins'
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 for unstated dependencies in examples ... OK
checking contents of 'data' directory ... OK
checking data for non-ASCII characters ... OK
checking data for ASCII and uncompressed saves ... OK
checking line endings in C/C++/Fortran sources/headers ... OK
checking line endings in Makefiles ... OK
checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
checking compiled code ... NOTE
File 'd:/Rcompile/CRANpkg/lib/2.15/siatclust/libs/i386/siatclust.dll':
Found 'abort', possibly from 'abort' (C)
File 'd:/Rcompile/CRANpkg/lib/2.15/siatclust/libs/x64/siatclust.dll':
Found 'abort', possibly from 'abort' (C)

Compiled code should not call functions which might terminate R nor
write to stdout/stderr instead of to the console. The detected symbols
are linked into the code but might come from libraries and not actually
be called.

See 'Writing portable packages' in the 'Writing R Extensions' manual.
checking examples ...
** running examples for arch 'i386' ... OK
** running examples for arch 'x64' ... OK
checking for unstated dependencies in tests ... OK
checking tests ...
** running tests for arch 'i386' OK
Running 'ewkm01.R'
Comparing 'ewkm01.Rout' to 'ewkm01.Rout.save' ...12c12
< K-means clustering with 1 clusters of sizes 248
---
> K-means clustering with 5 clusters of sizes 91, 28, 36, 65, 28
16c16,20
< 1 7.145565 20.37218 1.377419 4.544355 8.082661 39.94355 9.818548
---
> 1 2.263736 15.95055 0.3648352 2.591209 7.453846 31.74725 6.637363
> 2 9.571429 18.25357 3.6928571 5.028571 6.942857 52.53571 20.464286
> 3 6.680556 20.77778 1.2333333 5.511111 10.097222 49.11111 14.500000
> 4 12.253846 28.78615 0.4123077 6.883077 10.607692 40.70769 6.815385
> 5 9.325000 16.80714 4.7785714 3.735714 2.814286 40.42857 10.464286
18c22,26
< 1 18.05645 71.47177 43.85887 1019.748 1016.979 3.689516
---
> 1 15.58242 76.75824 45.83516 1024.531 1021.507 3.494505
> 2 26.21429 70.50000 51.64286 1012.736 1011.496 5.000000
> 3 25.80556 59.30556 33.50000 1018.347 1015.625 2.638889
> 4 14.69231 65.64615 30.52308 1018.662 1015.166 2.553846
> 5 15.78571 84.42857 73.92857 1015.543 1013.693 7.000000
20c28,32
< 1 3.850806 12.26935 19.08105
---
> 1 3.692308 7.848352 14.92088
> 2 3.928571 13.096429 16.64643
> 3 3.416667 12.900000 19.65556
> 4 2.953846 17.790769 27.46308
> 5 6.928571 12.182143 14.83929
23,29c35,41
< [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
---
> [1] 3 3 5 1 5 1 1 1 1 5 4 5 3 4 4 1 3 3 1 1 1 3 2 3 5 1 4 1 3 1 4 1 4 2 5 4 1
> [38] 1 4 5 4 1 4 5 1 1 1 1 4 4 4 1 3 4 1 3 5 4 1 3 5 1 1 4 2 3 3 4 1 2 4 5 1 4
> [75] 1 3 1 5 3 1 1 1 4 4 1 4 1 1 1 1 3 5 4 4 2 3 1 1 1 4 1 4 1 1 1 5 1 1 3 4 4
> [112] 4 4 3 2 4 2 1 1 1 5 1 1 4 1 1 1 1 4 5 4 2 2 1 1 2 4 5 1 3 1 4 2 3 2 4 4 4
> [149] 1 2 4 4 3 2 1 1 5 4 1 2 5 3 1 1 3 3 4 4 4 3 1 4 3 1 1 4 3 5 1 1 1 1 2 2 4
> [186] 5 5 2 1 1 1 4 1 2 4 2 4 5 3 2 3 1 1 1 2 2 1 1 4 4 3 5 4 1 4 2 1 4 3 2 1 1
> [223] 2 1 5 1 3 2 3 4 1 4 4 1 3 5 4 1 4 4 3 4 4 5 5 4 2 1
40c52
< 100 10 137
---
> 100 10 145
45c57
< K-means clustering with 3 clusters of sizes 38, 55, 155
---
> K-means clustering with 2 clusters of sizes 88, 160
49,51c61,62
< 1 7.342105 15.99211 3.6789474 3.184211 3.547368 38.21053 9.315789
< 2 10.556364 22.28545 2.3018182 5.992727 9.218182 54.25455 17.527273
< 3 5.887097 20.76710 0.4851613 4.363871 8.791613 35.29032 7.206452
---
> 1 7.297727 16.94432 2.634091 3.752273 5.445455 42.60227 12.60227
> 2 7.061875 22.25750 0.686250 4.980000 9.533125 38.48125 8.28750
53,55c64,65
< 1 16.21053 85.52632 70.18421 1017.511 1015.376 6.421053
< 2 25.70909 64.20000 41.05455 1013.071 1011.078 3.727273
< 3 15.79355 70.60645 38.40000 1022.666 1019.465 3.006452
---
> 1 19.94318 78.32955 59.96591 1017.149 1015.022 5.443182
> 2 17.01875 67.70000 35.00000 1021.178 1018.055 2.725000
57,59c67,68
< 1 6.368421 10.51316 14.22632
< 2 3.400000 15.15636 20.78000
< 3 3.393548 11.67548 19.66839
---
> 1 5.261364 11.08182 15.36818
> 2 3.075000 12.92250 21.12313
62,68c71,77
< [1] 3 3 1 3 1 3 3 3 1 1 3 1 3 2 3 3 3 3 3 3 3 3 2 2 1 3 3 3 2 3 3 3 2 2 1 2 3
< [38] 3 3 1 3 3 3 1 3 3 3 3 3 3 3 3 3 2 3 3 1 3 3 3 1 3 1 3 2 3 3 2 3 2 2 1 3 3
< [75] 3 3 3 1 2 3 3 3 3 3 3 3 3 3 3 3 2 1 3 3 2 2 2 3 3 3 3 2 3 3 3 1 3 3 2 3 3
< [112] 3 3 2 2 2 2 3 3 3 1 1 3 3 3 3 3 3 3 1 3 2 3 3 3 2 3 1 3 3 3 2 2 2 2 3 2 3
< [149] 3 2 3 3 3 2 3 1 1 3 3 2 1 3 3 1 2 2 3 3 3 2 3 3 2 3 3 3 3 1 3 3 3 1 2 2 3
< [186] 1 1 2 3 3 3 3 3 2 3 2 2 1 3 2 2 3 3 3 2 2 3 3 3 3 2 1 3 3 3 3 3 3 2 3 3 1
< [223] 1 1 1 1 2 2 3 3 3 3 2 3 3 1 2 3 3 3 2 3 2 1 1 3 3 3
---
> [1] 2 2 1 2 1 1 2 1 1 1 2 1 2 2 2 2 2 2 2 2 2 2 1 2 1 1 2 2 2 1 2 2 2 1 1 1 2
> [38] 2 2 1 2 2 2 1 1 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 1 1 1 2 1 2 2 2 2 1 2 1 2 2
> [75] 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2 1 2 1 2 2 1 2 1 2 2 2 2 2 2 2 1 1 2 2 2 2 2
> [112] 2 2 2 1 2 1 2 2 2 1 1 2 2 2 1 2 2 2 1 2 1 1 2 2 1 2 1 1 2 2 2 2 2 1 2 2 2
> [149] 2 1 2 2 2 1 1 1 1 2 2 1 1 2 2 1 1 2 2 1 2 2 2 2 1 2 2 2 2 1 2 2 2 1 1 1 2
> [186] 1 1 1 2 2 1 2 2 1 2 1 2 1 2 1 2 2 1 2 1 1 2 2 2 2 2 1 2 2 2 1 1 2 2 1 2 1
> [223] 1 1 1 1 2 1 2 2 1 2 2 1 2 1 2 2 2 2 2 2 2 1 1 2 1 1
79c88
< 100 50 397
---
> 100 50 319
84c93
< K-means clustering with 10 clusters of sizes 32, 22, 24, 21, 15, 22, 31, 14, 40, 27
---
> K-means clustering with 10 clusters of sizes 40, 14, 14, 41, 20, 21, 37, 21, 19, 21
88,97c97,106
< 1 4.4375000 20.32500 0.69375000 3.975000 9.834375 32.78125
< 2 10.9000000 28.84545 0.09090909 7.509091 11.550000 50.31818
< 3 7.1708333 16.82500 0.27500000 3.566667 5.170833 37.79167
< 4 13.2238095 29.75238 0.00952381 7.504762 10.680952 39.66667
< 5 11.3200000 18.38000 2.56000000 4.746667 5.093333 49.00000
< 6 13.4181818 28.40000 1.30000000 6.290909 9.822727 39.68182
< 7 0.8612903 16.34516 0.13548387 2.593548 8.287097 31.61290
< 8 9.0000000 16.07143 7.18571429 3.200000 1.628571 32.35714
< 9 7.5225000 18.31500 3.32500000 5.045000 8.962500 54.42500
< 10 0.8111111 13.84815 0.21481481 2.029630 5.800000 29.33333
---
> 1 1.132500 16.07250 0.47000000 2.590000 8.017500 30.72500
> 2 2.971429 20.25000 0.00000000 4.742857 10.378571 43.21429
> 3 9.350000 17.53571 8.20000000 4.214286 7.007143 60.78571
> 4 6.221951 19.31463 0.47804878 3.863415 8.919512 34.63415
> 5 -0.230000 11.82500 0.19000000 1.810000 6.605000 39.80000
> 6 9.128571 21.10000 1.43809524 6.028571 9.600000 54.38095
> 7 12.383784 29.08108 0.62162162 6.562162 10.445946 37.02703
> 8 14.090476 21.13810 3.27619048 6.066667 3.219048 42.00000
> 9 12.852632 29.98421 0.07368421 8.389474 11.284211 54.31579
> 10 5.419048 15.20476 2.91428571 2.247619 3.452381 27.57143
99,108c108,117
< 1 6.125000 13.09375 69.50000 32.56250 1023.716 1020.322
< 2 12.045455 25.04545 51.13636 21.40909 1016.145 1012.923
< 3 13.041667 19.87500 70.12500 53.91667 1023.462 1021.246
< 4 6.619048 11.28571 65.42857 30.33333 1018.719 1015.229
< 5 16.266667 23.13333 76.66667 66.93333 1011.313 1010.000
< 6 5.772727 17.27273 75.00000 36.36364 1017.064 1013.418
< 7 4.258065 15.32258 77.74194 39.96774 1023.452 1020.032
< 8 8.571429 12.00000 89.71429 78.07143 1017.693 1015.714
< 9 19.275000 25.80000 63.75000 44.72500 1015.670 1013.990
< 10 4.740741 14.51852 85.29630 55.85185 1025.211 1022.244
---
> 1 5.200000 14.90000 78.87500 41.45000 1023.630 1020.475
> 2 7.500000 23.50000 61.35714 27.64286 1021.229 1017.371
> 3 23.214286 25.28571 71.50000 57.35714 1009.121 1008.314
> 4 10.317073 15.68293 67.48780 40.48780 1023.320 1020.468
> 5 8.050000 21.55000 79.55000 57.15000 1024.925 1022.480
> 6 15.904762 23.90476 63.23810 41.38095 1016.476 1014.048
> 7 5.756757 13.29730 70.32432 30.27027 1018.451 1014.824
> 8 12.857143 18.23810 78.47619 67.14286 1013.500 1011.900
> 9 12.789474 24.94737 51.05263 22.42105 1015.005 1011.942
> 10 7.285714 13.04762 85.90476 66.71429 1022.648 1020.143
110,119c119,128
< 1 2.281250 2.468750 10.546875 19.27188
< 2 1.090909 1.500000 18.922727 27.81364
< 3 5.250000 5.250000 12.054167 15.58750
< 4 1.761905 3.380952 18.519048 28.27619
< 5 6.466667 5.533333 14.273333 16.01333
< 6 4.090909 3.954545 17.604545 27.12273
< 7 2.935484 3.129032 6.896774 15.39677
< 8 7.357143 7.428571 11.600000 14.44286
< 9 3.875000 3.575000 12.140000 16.87250
< 10 4.407407 4.888889 5.466667 12.75185
---
> 1 3.050000 3.025000 6.865000 15.16000
> 2 2.285714 2.571429 10.578571 19.04286
> 3 4.928571 4.000000 12.835714 15.69286
> 4 3.121951 3.487805 11.836585 18.20732
> 5 3.750000 4.450000 4.900000 10.57000
> 6 3.904762 3.904762 13.323810 19.54762
> 7 2.702703 3.324324 17.572973 27.87568
> 8 7.047619 6.476190 16.738095 19.00952
> 9 1.315789 1.684211 20.226316 28.86316
> 10 6.380952 6.523810 9.109524 13.90476
122,131c131,140
< [1] 9 1 8 7 8 3 10 3 10 8 1 10 9 4 1 1 6 9 1 7 7 9 9 2 5
< [26] 10 6 3 9 3 4 10 6 9 9 6 1 7 1 10 4 1 6 5 3 10 3 7 1 1
< [51] 4 10 2 2 10 9 5 1 7 1 8 3 10 6 5 2 9 2 7 9 6 8 1 9 1
< [76] 1 1 8 4 3 7 3 6 6 3 4 7 9 7 10 9 5 4 6 9 9 3 7 7 6
< [101] 10 2 10 7 10 5 1 7 2 2 2 2 2 2 9 6 9 7 1 7 8 10 7 4 7
< [126] 10 1 7 2 8 6 9 3 7 7 9 2 5 3 9 7 6 9 9 5 2 6 2 7 9
< [151] 1 4 9 9 10 10 5 1 1 9 5 6 1 10 9 9 1 3 1 2 1 6 9 3 3
< [176] 4 1 5 7 10 3 10 6 5 4 8 8 9 1 7 3 4 7 9 4 9 4 10 9 9
< [201] 9 10 3 7 9 9 7 1 4 4 2 8 2 7 6 3 3 4 2 3 1 10 5 10 8
< [226] 10 9 5 1 4 3 4 6 3 1 5 4 7 4 2 9 6 6 8 8 2 7 10
---
> [1] 2 2 8 4 10 4 1 10 10 10 7 10 2 9 2 4 6 4 4 1 1 4 6 9 8
> [26] 10 7 5 6 5 7 1 7 3 3 6 4 1 7 10 7 4 4 8 5 1 5 1 4 4
> [51] 7 5 2 9 1 3 3 4 1 2 10 10 5 7 8 2 4 9 1 8 7 10 4 6 4
> [76] 7 4 8 6 1 1 4 7 6 10 7 1 4 1 5 6 8 7 7 4 6 5 1 1 7
> [101] 5 9 10 1 5 3 1 1 9 9 7 9 9 9 3 7 4 1 4 1 8 10 1 2 1
> [126] 1 4 1 9 10 7 4 8 1 1 3 7 8 1 2 1 7 3 6 8 9 6 2 1 3
> [151] 7 7 4 6 5 5 8 4 4 6 8 2 4 5 6 6 4 4 4 9 4 7 6 4 4
> [176] 7 4 10 1 5 5 10 8 8 7 10 10 6 1 1 1 7 1 3 7 3 9 5 4 6
> [201] 6 1 8 1 3 3 1 4 7 7 9 8 9 4 7 8 5 7 9 4 4 10 8 5 8
> [226] 10 2 3 2 7 1 7 6 4 2 5 7 1 7 9 6 7 7 10 8 9 4 5
142c151
< 100 900 4657
---
> 100 497 2615
151c160
< 1 7.145565 20.37218 1.377419 4.544355 8.082661 39.94355 9.818548
---
> 1 1772.1 5052.3 341.6 1127 2004.5 9906 2435
153c162
< 1 18.05645 71.47177 43.85887 1019.748 1016.979 3.689516
---
> 1 4478 17725 10877 252897.6 252210.7 915
155c164
< 1 3.850806 12.26935 19.08105
---
> 1 955 3042.8 4732.1
158,164c167,176
< [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
---
> [1] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [26] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [51] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [76] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [101] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [126] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [151] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [176] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [201] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [226] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
** running tests for arch 'x64' OK
Running 'ewkm01.R'
Comparing 'ewkm01.Rout' to 'ewkm01.Rout.save' ...12c12
< K-means clustering with 1 clusters of sizes 248
---
> K-means clustering with 5 clusters of sizes 91, 28, 36, 65, 28
16c16,20
< 1 7.145565 20.37218 1.377419 4.544355 8.082661 39.94355 9.818548
---
> 1 2.263736 15.95055 0.3648352 2.591209 7.453846 31.74725 6.637363
> 2 9.571429 18.25357 3.6928571 5.028571 6.942857 52.53571 20.464286
> 3 6.680556 20.77778 1.2333333 5.511111 10.097222 49.11111 14.500000
> 4 12.253846 28.78615 0.4123077 6.883077 10.607692 40.70769 6.815385
> 5 9.325000 16.80714 4.7785714 3.735714 2.814286 40.42857 10.464286
18c22,26
< 1 18.05645 71.47177 43.85887 1019.748 1016.979 3.689516
---
> 1 15.58242 76.75824 45.83516 1024.531 1021.507 3.494505
> 2 26.21429 70.50000 51.64286 1012.736 1011.496 5.000000
> 3 25.80556 59.30556 33.50000 1018.347 1015.625 2.638889
> 4 14.69231 65.64615 30.52308 1018.662 1015.166 2.553846
> 5 15.78571 84.42857 73.92857 1015.543 1013.693 7.000000
20c28,32
< 1 3.850806 12.26935 19.08105
---
> 1 3.692308 7.848352 14.92088
> 2 3.928571 13.096429 16.64643
> 3 3.416667 12.900000 19.65556
> 4 2.953846 17.790769 27.46308
> 5 6.928571 12.182143 14.83929
23,29c35,41
< [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
---
> [1] 3 3 5 1 5 1 1 1 1 5 4 5 3 4 4 1 3 3 1 1 1 3 2 3 5 1 4 1 3 1 4 1 4 2 5 4 1
> [38] 1 4 5 4 1 4 5 1 1 1 1 4 4 4 1 3 4 1 3 5 4 1 3 5 1 1 4 2 3 3 4 1 2 4 5 1 4
> [75] 1 3 1 5 3 1 1 1 4 4 1 4 1 1 1 1 3 5 4 4 2 3 1 1 1 4 1 4 1 1 1 5 1 1 3 4 4
> [112] 4 4 3 2 4 2 1 1 1 5 1 1 4 1 1 1 1 4 5 4 2 2 1 1 2 4 5 1 3 1 4 2 3 2 4 4 4
> [149] 1 2 4 4 3 2 1 1 5 4 1 2 5 3 1 1 3 3 4 4 4 3 1 4 3 1 1 4 3 5 1 1 1 1 2 2 4
> [186] 5 5 2 1 1 1 4 1 2 4 2 4 5 3 2 3 1 1 1 2 2 1 1 4 4 3 5 4 1 4 2 1 4 3 2 1 1
> [223] 2 1 5 1 3 2 3 4 1 4 4 1 3 5 4 1 4 4 3 4 4 5 5 4 2 1
40c52
< 100 10 137
---
> 100 10 145
45c57
< K-means clustering with 3 clusters of sizes 38, 55, 155
---
> K-means clustering with 2 clusters of sizes 88, 160
49,51c61,62
< 1 7.342105 15.99211 3.6789474 3.184211 3.547368 38.21053 9.315789
< 2 10.556364 22.28545 2.3018182 5.992727 9.218182 54.25455 17.527273
< 3 5.887097 20.76710 0.4851613 4.363871 8.791613 35.29032 7.206452
---
> 1 7.297727 16.94432 2.634091 3.752273 5.445455 42.60227 12.60227
> 2 7.061875 22.25750 0.686250 4.980000 9.533125 38.48125 8.28750
53,55c64,65
< 1 16.21053 85.52632 70.18421 1017.511 1015.376 6.421053
< 2 25.70909 64.20000 41.05455 1013.071 1011.078 3.727273
< 3 15.79355 70.60645 38.40000 1022.666 1019.465 3.006452
---
> 1 19.94318 78.32955 59.96591 1017.149 1015.022 5.443182
> 2 17.01875 67.70000 35.00000 1021.178 1018.055 2.725000
57,59c67,68
< 1 6.368421 10.51316 14.22632
< 2 3.400000 15.15636 20.78000
< 3 3.393548 11.67548 19.66839
---
> 1 5.261364 11.08182 15.36818
> 2 3.075000 12.92250 21.12313
62,68c71,77
< [1] 3 3 1 3 1 3 3 3 1 1 3 1 3 2 3 3 3 3 3 3 3 3 2 2 1 3 3 3 2 3 3 3 2 2 1 2 3
< [38] 3 3 1 3 3 3 1 3 3 3 3 3 3 3 3 3 2 3 3 1 3 3 3 1 3 1 3 2 3 3 2 3 2 2 1 3 3
< [75] 3 3 3 1 2 3 3 3 3 3 3 3 3 3 3 3 2 1 3 3 2 2 2 3 3 3 3 2 3 3 3 1 3 3 2 3 3
< [112] 3 3 2 2 2 2 3 3 3 1 1 3 3 3 3 3 3 3 1 3 2 3 3 3 2 3 1 3 3 3 2 2 2 2 3 2 3
< [149] 3 2 3 3 3 2 3 1 1 3 3 2 1 3 3 1 2 2 3 3 3 2 3 3 2 3 3 3 3 1 3 3 3 1 2 2 3
< [186] 1 1 2 3 3 3 3 3 2 3 2 2 1 3 2 2 3 3 3 2 2 3 3 3 3 2 1 3 3 3 3 3 3 2 3 3 1
< [223] 1 1 1 1 2 2 3 3 3 3 2 3 3 1 2 3 3 3 2 3 2 1 1 3 3 3
---
> [1] 2 2 1 2 1 1 2 1 1 1 2 1 2 2 2 2 2 2 2 2 2 2 1 2 1 1 2 2 2 1 2 2 2 1 1 1 2
> [38] 2 2 1 2 2 2 1 1 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 1 1 1 2 1 2 2 2 2 1 2 1 2 2
> [75] 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2 1 2 1 2 2 1 2 1 2 2 2 2 2 2 2 1 1 2 2 2 2 2
> [112] 2 2 2 1 2 1 2 2 2 1 1 2 2 2 1 2 2 2 1 2 1 1 2 2 1 2 1 1 2 2 2 2 2 1 2 2 2
> [149] 2 1 2 2 2 1 1 1 1 2 2 1 1 2 2 1 1 2 2 1 2 2 2 2 1 2 2 2 2 1 2 2 2 1 1 1 2
> [186] 1 1 1 2 2 1 2 2 1 2 1 2 1 2 1 2 2 1 2 1 1 2 2 2 2 2 1 2 2 2 1 1 2 2 1 2 1
> [223] 1 1 1 1 2 1 2 2 1 2 2 1 2 1 2 2 2 2 2 2 2 1 1 2 1 1
79c88
< 100 50 397
---
> 100 50 319
84c93
< K-means clustering with 10 clusters of sizes 48, 7, 43, 24, 11, 12, 22, 21, 24, 36
---
> K-means clustering with 10 clusters of sizes 40, 14, 14, 41, 20, 21, 37, 21, 19, 21
88,97c97,106
< 1 4.1541667 19.70833 0.62500000 3.683333 9.485417 35.35417
< 2 13.5428571 26.72857 1.51428571 5.628571 9.657143 46.42857
< 3 1.4348837 15.67907 0.30232558 2.260465 6.765116 26.25581
< 4 4.7250000 13.49167 0.95000000 3.116667 5.791667 49.91667
< 5 0.4636364 12.23636 1.56363636 1.490909 6.109091 37.63636
< 6 12.1333333 20.75000 0.28333333 5.583333 4.516667 32.50000
< 7 12.3090909 28.70909 0.66363636 7.163636 10.795455 35.54545
< 8 14.1809524 21.37143 6.92380952 4.809524 3.495238 43.61905
< 9 12.2666667 30.90000 0.09166667 7.750000 11.233333 50.00000
< 10 8.0305556 19.87778 2.28888889 5.855556 9.716667 51.52778
---
> 1 1.132500 16.07250 0.47000000 2.590000 8.017500 30.72500
> 2 2.971429 20.25000 0.00000000 4.742857 10.378571 43.21429
> 3 9.350000 17.53571 8.20000000 4.214286 7.007143 60.78571
> 4 6.221951 19.31463 0.47804878 3.863415 8.919512 34.63415
> 5 -0.230000 11.82500 0.19000000 1.810000 6.605000 39.80000
> 6 9.128571 21.10000 1.43809524 6.028571 9.600000 54.38095
> 7 12.383784 29.08108 0.62162162 6.562162 10.445946 37.02703
> 8 14.090476 21.13810 3.27619048 6.066667 3.219048 42.00000
> 9 12.852632 29.98421 0.07368421 8.389474 11.284211 54.31579
> 10 5.419048 15.20476 2.91428571 2.247619 3.452381 27.57143
99,108c108,117
< 1 5.937500 16.02083 72.39583 34.64583 1022.715 1019.335
< 2 9.000000 17.14286 76.57143 39.85714 1014.000 1011.057
< 3 5.302326 11.25581 81.58140 48.67442 1026.035 1022.814
< 4 17.375000 26.83333 69.91667 61.91667 1017.296 1015.821
< 5 4.818182 19.90909 88.00000 57.63636 1022.918 1019.845
< 6 12.000000 14.91667 68.16667 53.50000 1020.042 1018.267
< 7 6.409091 11.36364 65.04545 31.50000 1018.905 1015.373
< 8 12.142857 16.42857 84.14286 69.95238 1011.757 1010.033
< 9 7.416667 21.29167 60.37500 21.16667 1017.283 1013.771
< 10 18.638889 26.58333 58.19444 39.16667 1016.792 1014.656
---
> 1 5.200000 14.90000 78.87500 41.45000 1023.630 1020.475
> 2 7.500000 23.50000 61.35714 27.64286 1021.229 1017.371
> 3 23.214286 25.28571 71.50000 57.35714 1009.121 1008.314
> 4 10.317073 15.68293 67.48780 40.48780 1023.320 1020.468
> 5 8.050000 21.55000 79.55000 57.15000 1024.925 1022.480
> 6 15.904762 23.90476 63.23810 41.38095 1016.476 1014.048
> 7 5.756757 13.29730 70.32432 30.27027 1018.451 1014.824
> 8 12.857143 18.23810 78.47619 67.14286 1013.500 1011.900
> 9 12.789474 24.94737 51.05263 22.42105 1015.005 1011.942
> 10 7.285714 13.04762 85.90476 66.71429 1022.648 1020.143
110,119c119,128
< 1 2.541667 2.770833 10.058333 18.58958
< 2 6.714286 3.142857 16.442857 25.08571
< 3 4.000000 4.093023 6.902326 14.73256
< 4 4.500000 4.750000 9.504167 11.82500
< 5 4.636364 4.090909 4.209091 11.21818
< 6 6.166667 6.500000 15.800000 19.47500
< 7 1.954545 3.363636 17.836364 27.41364
< 8 7.142857 6.761905 16.652381 19.20952
< 9 1.875000 2.333333 19.354167 29.71667
< 10 2.861111 3.194444 13.263889 18.61389
---
> 1 3.050000 3.025000 6.865000 15.16000
> 2 2.285714 2.571429 10.578571 19.04286
> 3 4.928571 4.000000 12.835714 15.69286
> 4 3.121951 3.487805 11.836585 18.20732
> 5 3.750000 4.450000 4.900000 10.57000
> 6 3.904762 3.904762 13.323810 19.54762
> 7 2.702703 3.324324 17.572973 27.87568
> 8 7.047619 6.476190 16.738095 19.00952
> 9 1.315789 1.684211 20.226316 28.86316
> 10 6.380952 6.523810 9.109524 13.90476
122,131c131,140
< [1] 10 1 8 1 8 4 3 6 3 5 1 5 10 9 7 1 10 10 1 3 3 10 10 9 8
< [26] 3 9 3 10 4 7 3 1 4 8 2 1 1 1 6 7 1 6 8 4 3 4 3 1 1
< [51] 7 5 10 9 3 10 8 1 3 1 3 4 5 7 8 9 10 9 1 4 2 3 1 10 1
< [76] 1 1 8 10 4 3 6 7 10 6 7 1 10 1 3 10 4 9 9 4 10 4 1 1 1
< [101] 3 9 3 3 5 4 3 3 10 9 7 9 9 10 10 9 6 3 1 3 8 3 3 7 3
< [126] 3 1 1 9 8 9 10 6 3 3 2 9 8 3 1 3 2 10 10 8 9 2 9 1 4
< [151] 1 7 10 10 3 5 8 1 1 10 8 1 1 5 10 10 1 6 1 10 1 7 2 6 1
< [176] 7 1 4 3 3 5 3 8 4 7 8 3 10 1 3 5 7 3 8 7 4 9 4 10 10
< [201] 10 3 6 3 4 8 3 1 9 7 10 8 9 3 1 6 4 7 10 6 1 3 4 4 8
< [226] 3 10 4 1 7 1 7 2 4 1 4 7 1 7 9 10 7 9 5 8 9 1 5
---
> [1] 2 2 8 4 10 4 1 10 10 10 7 10 2 9 2 4 6 4 4 1 1 4 6 9 8
> [26] 10 7 5 6 5 7 1 7 3 3 6 4 1 7 10 7 4 4 8 5 1 5 1 4 4
> [51] 7 5 2 9 1 3 3 4 1 2 10 10 5 7 8 2 4 9 1 8 7 10 4 6 4
> [76] 7 4 8 6 1 1 4 7 6 10 7 1 4 1 5 6 8 7 7 4 6 5 1 1 7
> [101] 5 9 10 1 5 3 1 1 9 9 7 9 9 9 3 7 4 1 4 1 8 10 1 2 1
> [126] 1 4 1 9 10 7 4 8 1 1 3 7 8 1 2 1 7 3 6 8 9 6 2 1 3
> [151] 7 7 4 6 5 5 8 4 4 6 8 2 4 5 6 6 4 4 4 9 4 7 6 4 4
> [176] 7 4 10 1 5 5 10 8 8 7 10 10 6 1 1 1 7 1 3 7 3 9 5 4 6
> [201] 6 1 8 1 3 3 1 4 7 7 9 8 9 4 7 8 5 7 9 4 4 10 8 5 8
> [226] 10 2 3 2 7 1 7 6 4 2 5 7 1 7 9 6 7 7 10 8 9 4 5
142c151
< 100 276 1542
---
> 100 497 2615
151c160
< 1 7.145565 20.37218 1.377419 4.544355 8.082661 39.94355 9.818548
---
> 1 1772.1 5052.3 341.6 1127 2004.5 9906 2435
153c162
< 1 18.05645 71.47177 43.85887 1019.748 1016.979 3.689516
---
> 1 4478 17725 10877 252897.6 252210.7 915
155c164
< 1 3.850806 12.26935 19.08105
---
> 1 955 3042.8 4732.1
158,164c167,176
< [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
< [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
---
> [1] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [26] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [51] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [76] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [101] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [126] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [151] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [176] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [201] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
> [226] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
checking PDF version of manual ... OK
NOTE: There were 2 notes.
See
'd:/Rcompile/CRANpkg/local/2.15/siatclust.Rcheck/00check.log'
for details.