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>         
> ############################### CLR ####################################
> # Calculates clr-transformation. Should be equal.
> 
> # Test data
> # data(varespec)
> testdata <- matrix(round(runif(1000, 0, 100)), nrow=20)
> testdata <- testdata - 50
> testdata[testdata < 0] <- 0
> rownames(testdata) <- paste0("row", seq_len(nrow(testdata)))
> colnames(testdata) <- paste0("col", seq_len(ncol(testdata)))
> testdata.with.pseudo <- testdata + 1
> 
> # Calculates relative abundance table
> relative <- vegan::decostand(testdata, "total")
> relative.with.pseudo <- vegan::decostand(testdata+1, "total")
> 
> # CLR data
> x.clr <- vegan::decostand(testdata+1, method = "clr")
> x.rclr <- vegan::decostand(testdata, method = "rclr")
> x.clr.pseudo <- vegan::decostand(testdata, method = "clr", pseudocount=1)
> 
> max(abs(x.clr - x.clr.pseudo))<1e-6
[1] TRUE
> max(abs(vegan::decostand(testdata+1, method = "clr", pseudocount=0)-x.clr.pseudo))<1e-6
[1] TRUE
> 
> # Tests clr
> alt.clr <- t(apply(as.matrix(relative.with.pseudo), 1, FUN=function(x){
+ log(x) - mean(log(x))}))
> max(abs(x.clr-alt.clr)) < 1e-6      
[1] TRUE
> all((x.rclr==0) == (testdata==0))
[1] TRUE
> 
> # Expect that error does not occur
> vegan::decostand(testdata, method = "rclr")
            col1        col2        col3       col4       col5        col6
row1   0.0000000 -0.30833276  0.38481442  0.0000000  0.0000000  0.94443020
row2   0.5236026  0.00000000  0.00000000  0.6380130  0.0000000  0.00000000
row3   0.0000000  0.00000000  0.03461515 -0.1564401  0.7703219  0.58800039
row4   1.0243134  1.11132477 -0.56265167  0.0000000  0.4589996  0.67411096
row5   0.0000000  0.00000000  0.65879553  0.0000000  0.5913542 -0.46179567
row6   0.0000000  0.00000000  0.45496726  0.5947292  0.0000000  0.00000000
row7   0.2233837  0.81936715 -0.07672088  0.0000000  0.0000000  0.00000000
row8   0.0000000  0.00000000  0.36107377 -0.3320734  0.3610738  0.92068956
row9   0.7371682  0.00000000  0.06701058 -0.9445903  0.0000000  0.00000000
row10  0.8972155  0.00000000  0.00000000  0.0000000  0.0000000  0.74306485
row11  0.0000000  0.00000000  0.00000000  0.2397049  0.0000000  0.00000000
row12  0.0000000  0.00000000  0.00000000  0.2452028  0.0000000 -0.06845475
row13  0.0000000  0.00000000  0.78980000 -0.3088123  0.0000000  0.81511781
row14  0.4810868 -2.73778901  0.00000000 -0.9460295 -1.1283511  0.00000000
row15  0.0000000  0.00000000  0.00000000 -1.9477014  0.0000000  0.00000000
row16  0.0000000  0.07145693  0.00000000  0.0000000  0.0000000  0.01083231
row17  0.0000000  0.00000000  0.89184263  0.2756565  0.7864821  0.07086208
row18  0.0000000  0.00000000  0.00000000  0.0000000  0.6091441  0.00000000
row19 -0.1045501 -1.55146909 -2.93776345 -0.4528568  0.0000000  0.00000000
row20 -1.4876215  0.00000000  0.00000000  0.0000000  0.0809944  0.00000000
             col7         col8        col9      col10       col11      col12
row1   0.00000000  0.230663737 -2.94739009  0.4538073  0.00000000 -0.8679486
row2   0.20514887 -1.026994811  0.20514887  0.0000000 -0.02846598 -0.5750097
row3  -0.90365449  0.425481456  0.53670709  0.0000000  0.00000000  0.0000000
row4   0.00000000  0.331166208 -0.68043470  0.0000000 -0.36198097  0.0000000
row5   0.59135425  0.295890034  0.56782375  0.5913542  0.00000000 -0.1253234
row6  -0.09841798  0.000000000  0.00000000  0.0000000  0.00000000  0.4296495
row7  -0.43339582  0.000000000  0.00000000  0.0000000 -0.58754650  0.0000000
row8   0.00000000  0.000000000  0.30978048  0.0000000  1.19398289  0.0000000
row9   0.00000000  0.000000000  0.71363775 -1.6377375  0.00000000  0.0000000
row10  0.00000000  0.140889443  0.89721553  0.8765962  0.00000000 -0.5096981
row11  0.00000000 -1.328911013  0.31974761  0.0000000  0.00000000  0.0000000
row12  0.00000000  0.000000000  0.00000000  0.2452028  0.00000000  0.0000000
row13  0.00000000  0.000000000  0.00000000  0.0000000  0.45844287  0.0000000
row14  0.00000000  0.899797151  0.52030753  1.1334120 -1.12835110  0.0000000
row15  0.00000000 -0.001791288 -0.27372500  0.0000000 -0.33826352  0.0000000
row16 -1.37546205  0.000000000  0.60553942  0.0000000  0.00000000  0.2827660
row17  0.00000000  0.535167688 -1.63388601  0.0000000  0.34711546  0.0000000
row18 -1.27792555  0.000000000  0.05707552  0.5783724  0.82620861  0.5783724
row19 -0.74053887  0.000000000  0.00000000  0.6457555  0.00000000  0.8908779
row20 -0.15262045 -1.487621517 -0.38900923 -1.4876215 -0.45800210  0.0000000
           col13       col14       col15       col16        col17        col18
row1   0.0000000  0.00000000  0.09713234  0.00000000 -1.337952181  0.000000000
row2   0.0000000  0.00000000  0.32293191  0.35929955  0.000000000  0.000000000
row3  -0.6159724  0.00000000  0.00000000  0.63679055 -0.461821739 -0.267665725
row4   0.0000000  0.00000000 -0.12081892 -0.36198097  0.705859658  0.705859658
row5  -2.0712336  0.00000000  0.00000000  0.00000000  0.658795528  0.008207962
row6   0.0000000  0.61623541  0.00000000  0.00000000  0.479659876 -0.594854861
row7   0.8193671  0.00000000 -0.99301161  0.00000000  0.000000000 -0.128014172
row8   0.1985548  0.00000000  0.00000000  0.00000000  0.000000000  0.799328702
row9   0.0000000  0.84716914  0.00000000  0.00000000  0.308172640 -1.925419581
row10 -1.0486946 -0.42965542  0.00000000 -0.59670950  0.812057717 -0.222016051
row11  0.0000000  0.05738335 -1.32891101  0.64517001  0.000000000 -0.741124348
row12  0.0000000  0.00000000 -1.06698358  0.00000000  0.000000000  0.000000000
row13  0.9548798  0.07067734  0.86390798  0.00000000  0.000000000  0.560225562
row14  0.3977052  0.84572993  0.00000000 -0.09873168  0.999880609  0.000000000
row15 -1.4368758  0.00000000  0.38767348 -0.21310038 -0.001791288  0.509034336
row16 -1.3754621 -0.27684976  0.00000000  0.00000000  0.639440969 -0.815846264
row17  0.7174892  0.00000000  0.00000000  0.66869908  0.000000000  0.000000000
row18  0.0000000  0.00000000 -0.69013888  0.00000000 -0.114774739  0.000000000
row19  0.0000000  0.00000000  0.00000000  0.00000000  0.846426184  0.000000000
row20  0.0000000  0.70960306  0.00000000  0.19877744  0.000000000  0.270236401
             col19       col20      col21        col22      col23      col24
row1   0.518345810  0.00000000  0.0000000 -0.462483443  0.6079580  0.0000000
row2   0.322931906  0.00000000  0.7406671  0.638012952  0.0000000 -0.4879983
row3   0.000000000  0.70578342  0.6833106 -0.903654491  0.0000000  0.4826399
row4   0.000000000 -0.36198097  1.0243134  1.046786245  0.0000000  0.0000000
row5   0.000000000  0.63681662  0.7013551  0.000000000  0.3565147 -1.3780864
row6   0.000000000 -0.66896283  0.0000000  0.000000000  0.0000000  0.0000000
row7  -1.973840863  0.68874696  0.0000000  0.565133008  0.0000000  0.0000000
row8  -0.236763230  0.73263733  0.0000000  0.000000000  0.4098639  0.0000000
row9   0.000000000  0.00000000 -1.4145940  0.343263960  0.0000000 -0.7214468
row10  0.000000000  0.00000000  0.0000000  0.000000000  0.0000000  0.4711311
row11  0.006090054  0.00000000  0.9534714  0.006090054  0.3574879  0.0000000
row12  0.000000000  0.00000000  0.0000000 -0.933452183 -0.1796804 -1.2211343
row13  0.170760795  0.00000000  0.0000000  0.458442868  0.9548798  0.5274357
row14  0.000000000  0.00000000  0.0000000  1.154031289  0.0000000 -1.1283511
row15  0.000000000  0.00000000  0.5912724  0.211782813  0.0000000  0.0000000
row16  0.821762525  0.00000000 -0.9699969 -1.152318501  0.0000000  0.0000000
row17  0.000000000  0.31202414  0.7410197  0.535167688  0.5061802  0.0000000
row18  0.000000000  0.00000000  0.0000000 -0.941453312  0.2036790 -0.9414533
row19  0.000000000  0.00000000  0.0000000  0.000000000  0.0000000  0.0000000
row20  0.000000000 -0.05253699  0.5665022  0.731581967  0.0809944  0.0000000
            col25       col26      col27      col28        col29       col30
row1   0.00000000  0.00000000  0.0000000  0.0000000  0.000000000  0.00000000
row2   0.00000000  0.00000000  0.0000000  0.4610822  0.000000000  0.00000000
row3   0.00000000 -0.05635663  0.0000000  0.0000000  0.000000000  0.00000000
row4   0.00000000  0.00000000  0.0000000 -1.3735819  0.000000000  0.00000000
row5   0.08825067  0.00000000  0.0000000  0.0000000 -0.867260775  0.46774029
row6   0.59472921  0.16728519 -0.4006988  0.0000000  0.000000000  0.02418435
row7   0.00000000  0.68874696  0.0000000  0.8193671  0.000000000 -1.12654300
row8   0.00000000  0.00000000  0.8618491  0.6234380 -1.025220590  0.00000000
row9   0.00000000  0.71363775  0.4724757  0.0000000  0.847169141  0.00000000
row10  0.00000000  0.00000000 -1.6083104 -0.4296554 -0.692019680  0.00000000
row11  0.58801160  0.00000000  0.0000000 -0.6357638  0.006090054  0.00000000
row12  0.00000000  0.00000000  0.0000000  0.0000000  0.000000000  0.00000000
row13  0.00000000  0.26173257  0.0000000  0.0000000  0.000000000 -1.77514935
row14  0.25794326  0.15258275  0.0000000 -0.9460295  1.090852387 -0.33989374
row15  0.21178281  0.00000000  0.0000000  0.3876735 -0.213100381  0.69135589
row16 -0.45917132  0.00000000  0.3292860  0.0000000  0.793591648  0.79359165
row17  0.38101701  0.00000000  0.0000000 -1.6338860  0.829967228  0.00000000
row18  0.00000000 -0.32241410  0.0000000  0.3315124  0.000000000  0.60914410
row19  0.72579820  0.00000000 -1.3283255 -0.2297132  0.933437561 -1.32832554
row20  0.79476087  0.00000000  0.0809944  0.4293011  0.000000000  0.51385848
           col31       col32       col33        col34       col35      col36
row1   0.2714857 -1.00147994  0.31070644  0.000000000  0.27148573  0.8138100
row2   0.0000000 -0.40795560 -0.02846598  0.000000000 -1.58661060  0.0000000
row3   0.4826399  0.00000000 -0.79829398  0.000000000 -0.70298380 -0.7982940
row4  -1.6612640  0.49822029  0.60741958  0.000000000 -0.45729115  0.0000000
row5   0.0000000  0.16235864  0.00000000  0.441072044  0.00000000  0.0000000
row6   0.0000000  0.09829232 -0.52586199  0.657908107  0.37700572  0.0000000
row7   0.0000000  0.00000000  0.32874423  0.259751359 -0.12801417  0.0000000
row8   0.0000000 -0.23676323  0.00000000  0.000000000 -0.43743393  0.8618491
row9   0.7136377  0.61355429  0.00000000  0.000000000  0.02049057  0.0000000
row10  0.0000000  0.00000000  0.83403662  0.001127501  0.00000000  0.5019028
row11  0.6725690  0.00000000  0.00000000 -1.146589456  0.00000000  0.1971453
row12  0.7712959  0.00000000  0.70067834  0.452842178 -0.44794437  0.6759857
row13  0.0000000  0.00000000  0.00000000  0.000000000  0.00000000  0.0000000
row14  0.0000000  0.00000000  0.00000000  0.951090445  0.00000000  0.7885715
row15  0.4800468  0.00000000  0.00000000  0.000000000  0.00000000  0.7378759
row16  0.0000000  0.00000000 -1.37546205  0.999443703 -2.76175641  1.0883912
row17 -0.0244481  0.00000000 -0.18696703  0.000000000  0.00000000  0.0000000
row18  0.0000000  0.98383755 -1.27792555  0.000000000  0.00000000  0.0000000
row19  0.0000000  0.39444106  0.61758461  0.645755489  0.91238415  0.0000000
row20  0.0000000  0.00000000  0.00000000  0.000000000  0.00000000  0.0000000
           col37       col38      col39      col40      col41       col42
row1  -0.2393399  0.51834581  0.0000000 -0.1141767  0.0000000  0.34844677
row2  -0.7756804  0.00000000  0.0000000  0.0000000  0.0000000  0.00000000
row3   0.7909412  0.00000000  0.3003183  0.0000000  0.0000000  0.00000000
row4   0.0000000  0.00000000 -2.0667291  0.0000000  0.0000000  0.95369582
row5   0.1623586 -2.47669869  0.5678237  0.0000000  0.0000000  0.00000000
row6   0.0000000 -0.05585836 -1.4421527 -1.4421527  0.0000000 -0.14286974
row7   0.0000000  0.68874696  0.5384648  0.0000000  0.0000000  0.00000000
row8  -1.5360462  0.00000000 -0.5552170  0.0000000  0.4098639 -1.94151132
row9   0.8677884 -0.53912522  0.0000000  0.0000000 -0.3159817 -1.07812172
row10 -0.5967095  0.00000000  0.0000000  0.0000000  0.0000000  0.00000000
row11  0.0000000  0.86831356  0.7252127 -0.3733996  0.0000000  0.23970491
row12  0.0000000  0.28294314  0.0000000  0.3193108  0.0000000  0.00000000
row13  0.2617326 -2.18061446 -0.7943201 -1.2643237  0.0000000  0.00000000
row14  1.1540313  0.66340837  0.0000000 -0.2528824  0.0000000 -1.63917672
row15  0.0000000  0.00000000  0.7603488  0.0000000  0.8455066 -0.64841845
row16  0.9271230  1.10944460 -0.2768498 -1.3754621  0.7935916 -0.56453184
row17 -0.3811230  0.23791616 -0.3121302 -1.2284209  0.0000000  0.07086208
row18  0.0000000  0.05707552  0.0000000 -0.2483061  0.0000000 -0.80792192
row19  0.0000000  0.00000000  0.0000000  0.0000000  0.8464262  0.00000000
row20  0.0000000  0.61651264  0.6641407  0.7530882  0.0000000 -0.10132716
           col43      col44       col45      col46       col47      col48
row1   0.0000000  0.0000000  0.00000000  0.0000000  0.76618197  0.0000000
row2   0.0000000  0.0000000  0.39439087  0.0000000  0.11813749 -0.7756804
row3   0.0000000 -0.1564401  0.00000000  0.0000000  0.00000000  0.0000000
row4  -0.9681168 -0.3619810  0.01271248  0.4589996  0.00000000  0.0000000
row5  -1.0904043  0.0000000  0.65879553  0.0000000  0.00000000  0.0000000
row6  -0.1893898  0.0000000  0.63728882  0.2000750  0.06192468  0.0000000
row7   0.0000000 -0.1820814  0.00000000 -0.6745579  0.53846476  0.0000000
row8  -2.6346585  0.8917020  0.00000000  0.0000000  0.00000000  0.0000000
row9  -0.2514431  0.0000000  0.00000000  0.7371682  0.82611573  0.0000000
row10 -0.1613914  0.0000000 -0.69201968 -0.5967095  0.00000000  0.0000000
row11  0.1971453  0.9328521  0.00000000  0.0000000  0.00000000 -0.9924388
row12  0.0000000  0.0000000  0.00000000  0.0000000  0.00000000  0.0000000
row13  0.0000000  0.7638245  0.00000000  0.0000000  0.00000000  0.5919743
row14  0.0000000  0.0000000  0.00000000  0.0000000  0.89979715  0.0000000
row15  0.8455066  0.0000000 -3.04631373  0.5090343  0.00000000  0.4194222
row16  0.0000000  0.9518157  0.97591321  0.0000000  0.00000000  1.0449061
row17  0.0000000  0.0000000  0.00000000  0.0000000  0.00000000 -0.4552310
row18  0.0000000  0.4448410  0.00000000  0.0000000  0.91929903  0.8262086
row19  0.8688990  0.0000000  0.00000000 -1.3283255  0.69982271  0.9742596
row20  0.0000000  0.6165126 -0.89983485 -0.3890092 -0.32447071  0.0000000
            col49      col50
row1   0.74148936  0.0000000
row2   0.02282731  0.7406671
row3  -0.61597242  0.0000000
row4  -0.27496960  0.0000000
row5   0.26414134  0.0000000
row6   0.00000000  0.1672852
row7   0.00000000 -0.6745579
row8   0.00000000  0.0000000
row9   0.00000000  0.0000000
row10  0.53175575  0.8765962
row11 -0.85890738  0.3938556
row12 -0.37383639  0.5980242
row13 -2.18061446  0.0000000
row14 -2.04464183  0.0000000
row15  0.32098210  0.2117828
row16  0.99944370 -0.4591713
row17 -2.32703319  0.2756565
row18  0.00000000 -0.4024568
row19  0.00000000  0.0000000
row20  0.00000000  0.1218164
attr(,"parameters")
attr(,"parameters")$means
    row1     row2     row3     row4     row5     row6     row7     row8 
2.947390 2.972905 3.100879 2.759876 3.169846 3.233912 3.072453 2.634659 
    row9    row10    row11    row12    row13    row14    row15    row16 
3.024032 2.994605 2.938349 3.012894 2.873762 2.737789 3.046314 2.761756 
   row17    row18    row19    row20 
3.020180 2.887363 2.937763 3.097059 

attr(,"parameters")$margin
[1] 1

attr(,"decostand")
[1] "rclr"
> vegan::decostand(testdata, method = "clr", pseudocount=1)
             col1       col2       col3       col4       col5      col6
row1  -1.39353914  1.3145111  1.9737567 -1.3935391 -1.3935391  2.518484
row2   2.13100640 -1.3953541 -1.3953541  2.2422320 -1.3953541 -1.395354
row3  -1.51326802 -1.5132680  1.6647858  1.4824643  2.3785523  2.200304
row4   2.26940793  2.3545657  0.7653305 -1.5372546  1.7208420  1.928481
row5  -1.68206970 -1.6820697  2.1680779 -1.6820697  2.1021199  1.090519
row6  -1.64014898 -1.6401490  2.0734231  2.2099986 -1.6401490 -1.640149
row7   1.89126334  2.4710818  1.6035813 -1.4409412 -1.4409412 -1.440941
row8  -1.26398142 -1.2639814  1.7805410  1.1339138  1.7805410  2.319538
row9   2.29945762 -1.4847320  1.6507622  0.7124926 -1.4847320 -1.484732
row10  2.44493286 -1.4670901 -1.4670901 -1.4670901 -1.4670901  2.294110
row11 -1.56183530 -1.5618353 -1.5618353  1.6570405 -1.5618353 -1.561835
row12 -0.98277920 -0.9827792 -0.9827792  2.3130577 -0.9827792  2.012953
row13 -1.24625547 -1.2462555  2.4426240  1.3928019 -1.2462555  2.467317
row14  1.72025314 -0.8446962 -1.5378434  0.4080667  0.2539161 -1.537843
row15 -1.62495846 -1.6249585 -1.6249585 -0.2386641 -1.6249585 -1.624958
row16 -1.77391098  1.1164608 -1.7739110 -1.7739110 -1.7739110  1.059302
row17 -1.60729438 -1.6072944  2.3245312  1.7249101  2.2213470  1.528200
row18 -1.35962740 -1.3596274 -1.3596274 -1.3596274  2.1667331 -1.359627
row19  1.55596354  0.2750297 -0.6412610  1.2305411 -1.3344082 -1.334408
row20  0.08881781 -1.7029417 -1.7029417 -1.7029417  1.5159342 -1.702942
            col7        col8         col9       col10      col11      col12
row1  -1.3935391  1.82533669 -0.700391959  2.04044807 -1.3935391  0.8036854
row2   1.8235217  0.68408742  1.823521704 -1.39535412  1.6003782  1.0895525
row3   0.7893171  2.04208004  2.150293626 -1.51326802 -1.5132680 -1.5132680
row4  -1.5372546  1.59823966  0.659970017 -1.53725456  0.9476521 -1.5372546
row5   2.1021199  1.81443786  2.079130413  2.10211993 -1.6820697  1.4089728
row6   1.5379049 -1.64014898 -1.640148979 -1.64014898 -1.6401490  2.0487305
row7   1.2671090 -1.44094117 -1.440941175 -1.44094117  1.1240082 -1.4409412
row8  -1.2639814 -1.26398142  1.731750850 -1.26398142  2.5861662 -1.2639814
row9  -1.4847320 -1.48473202  2.276468099  0.12470590 -1.4847320 -1.4847320
row10 -1.4670901  1.71096369  2.444932861  2.42473015 -1.4670901  1.0978592
row11 -1.5618353  0.22992417  1.734001567 -1.56183530 -1.5618353 -1.5618353
row12 -0.9827792 -0.98277920 -0.982779196  2.31305767 -0.9827792 -0.9827792
row13 -1.2462555 -1.24625547 -1.246255470 -1.24625547  2.1210404 -1.2462555
row14 -1.5378434  2.12571825  1.757993467  2.35397690  0.2539161 -1.5378434
row15 -1.6249585  1.46608399  1.208254882 -1.62495846  1.1476303 -1.6249585
row16 -0.1644731 -1.77391098  1.627286398 -1.77391098 -1.7739110  1.3171315
row17 -1.6072944  1.97622455  0.002143528 -1.60729438  1.7939030 -1.6072944
row18  0.4321321 -1.35962740  1.636104870  2.13688016  2.3780422  2.1368802
row19  0.9681769 -1.33440822 -1.334408221  2.27650969 -1.3344082  2.5157394
row20  1.2927906  0.08881781  1.069647060  0.08881781  1.0051085 -1.7029417
           col13      col14      col15      col16      col17       col18
row1  -1.3935391 -1.3935391  1.6975033 -1.3935391  0.3982203 -1.39353914
row2  -1.3953541 -1.3953541  1.9368504  1.9719417 -1.3953541 -1.39535412
row3   1.0516813 -1.5132680 -1.5132680  2.2479321  1.1947822  1.37710374
row4  -1.5372546 -1.5372546  1.1707956  0.9476521  1.9592530  1.95925300
row5  -0.2957753 -1.6820697 -1.6820697 -1.6820697  2.1680779  1.53680612
row6  -1.6401490  2.2310520 -1.6401490 -1.6401490  2.0975206  1.06790122
row7   2.4710818 -1.4409412  0.7562834 -1.4409412 -1.4409412  1.55479110
row8   1.6263903 -1.2639814 -1.2639814 -1.2639814 -1.2639814  2.20175448
row9  -1.4847320  2.4070883 -1.4847320 -1.4847320  1.8825638 -0.09843766
row10  0.6123514  1.1719672 -1.4670901  1.0178165  2.3615513  1.36612320
row11 -1.5618353  1.4826871  0.2299242  2.0490826 -1.5618353  0.74074979
row12 -0.9827792 -0.9827792  1.0966623 -0.9827792 -0.9827792 -0.98277920
row13  2.6038921  1.7494768  2.5149446 -1.2462555 -1.2462555  2.21948043
row14  1.6402104  2.0730745 -1.5378434  1.1702068  2.2233567 -1.53784340
row15  0.1668010 -1.6249585  1.8407774  1.2654133  1.4660840  1.95856048
row16 -0.1644731  0.7910384 -1.7739110 -1.7739110  1.6600762  0.30553056
row17  2.1539057 -1.6072944 -1.6072944  2.1062777 -1.6072944 -1.60729438
row18 -1.3596274 -1.3596274  0.9429577 -1.3596274  1.4735859 -1.35962740
row19 -1.3344082 -1.3344082 -1.3344082 -1.3344082  2.4722543 -1.33440822
row20 -1.7029417  2.1256997 -1.7029417  1.6292628 -1.7029417  1.69825572
            col19      col20      col21       col22     col23      col24
row1   2.10296842 -1.3935391 -1.3935391  1.17141022  2.189980 -1.3935391
row2   1.93685039 -1.3953541  2.3423155  2.24223204 -1.395354  1.1695952
row3  -1.51326802  2.3153734  2.2933945  0.78931707 -1.513268  2.0976499
row4  -1.53725456  0.9476521  2.2694079  2.29138684 -1.537255 -1.5372546
row5  -1.68206970  2.1465717  2.2097506 -1.68206970  1.873278  0.2638404
row6  -1.64014898  0.9989084 -1.6401490 -1.64014898 -1.640149 -1.6401490
row7  -0.05464681  2.3432485 -1.4409412  2.22262047 -1.440941 -1.4409412
row8   1.22092523  2.1372160 -1.2639814 -1.26398142  1.827061 -1.2639814
row9  -1.48473202 -1.4847320  0.3070275  1.91646536 -1.484732  0.9131633
row10 -1.46709014 -1.4670901 -1.4670901 -1.46709014 -1.467090  2.0294174
row11  1.43389697 -1.5618353  2.3501877  1.43389697  1.770369 -1.5618353
row12 -0.98277920 -0.9827792 -0.9827792  1.21444538  1.907593  0.9631310
row13  1.84478698 -1.2462555 -1.2462555  2.12104036  2.603892  2.1877317
row14 -1.53784340 -1.5378434 -1.5378434  2.37417961 -1.537843  0.2539161
row15 -1.62495846 -1.6249585  2.0386032  1.67087840 -1.624958 -1.6249585
row16  1.83700693 -1.7739110  0.1719992  0.01784849 -1.773911 -1.7739110
row17 -1.60729438  1.7600014  2.1768952  1.97622455  1.948054 -1.6072944
row18 -1.35962740 -1.3596274 -1.3596274  0.71981414  1.775867  0.7198141
row19 -1.33440822 -1.3344082 -1.3344082 -1.33440822 -1.334408 -1.3344082
row20 -1.70294166  1.3881008  1.9859378  2.14720594  1.515934 -1.7029417
           col25      col26      col27        col28      col29      col30
row1  -1.3935391 -1.3935391 -1.3935391 -1.393539139 -1.3935391 -1.3935391
row2  -1.3953541 -1.3953541 -1.3953541  2.070381782 -1.3953541 -1.3953541
row3  -1.5132680  1.5777744 -1.5132680 -1.513268020 -1.5132680 -1.5132680
row4  -1.5372546 -1.5372546 -1.5372546  0.072183352 -1.5372546 -1.5372546
row5   1.6137672 -1.6820697 -1.6820697 -1.682069703  0.7158256  1.9814919
row6   2.2099986  1.7938382  1.2502228 -1.640148979 -1.6401490  1.6556879
row7  -1.4409412  2.3432485 -1.4409412  2.471081831 -1.4409412  0.6385004
row8  -1.2639814 -1.2639814  2.2623791  2.031855443  0.5277780 -1.2639814
row9  -1.4847320  2.2764681  2.0416285 -1.484732017  2.4070883 -1.4847320
row10 -1.4670901 -1.4670901  0.1423478  1.171967186  0.9308051 -1.4670901
row11  1.9935128 -1.5618353 -1.5618353  0.836059973  1.4338970 -1.5618353
row12 -0.9827792 -0.9827792 -0.9827792 -0.982779196 -0.9827792 -0.9827792
row13 -1.2462555  1.9317984 -1.2462555 -1.246255470 -1.2462555  0.1400389
row14  1.5066790  1.4065956 -1.5378434  0.408066750  2.3123042  0.9470633
row15  1.6708784 -1.6249585 -1.6249585  1.840777441  1.2654133  2.1362417
row16  0.6239843 -1.7739110  1.3615832 -1.773910984  1.8096080  1.8096080
row17  1.8266928 -1.6072944 -1.6072944  0.002143528  2.2639066 -1.6072944
row18 -1.3596274  1.2794299 -1.3596274  1.898469135 -1.3596274  2.1667331
row19  2.3544712 -1.3344082  0.4573512  1.438180501  2.5574121  0.4573512
row20  2.2090813 -1.7029417  1.5159342  1.852406399 -1.7029417  1.9346445
           col31      col32      col33      col34      col35      col36
row1   1.8645574  0.6859024  1.9022977 -1.3935391  1.8645574  2.3906505
row2  -1.3953541  1.2437032  1.6003782 -1.3953541  0.2140838 -1.3953541
row3   2.0976499 -1.5132680  0.8846273 -1.5132680  0.9716386  0.8846273
row4  -0.1509602  1.7585823  1.8639428 -1.5372546  0.8606407 -1.5372546
row5  -1.6820697  1.6852261 -1.6820697  1.9555165 -1.6820697 -1.6820697
row6  -1.6401490  1.7271469  1.1324397  2.2718740  1.9974372 -1.6401490
row7  -1.4409412 -1.4409412  1.9930460  1.9263547  1.5547911 -1.4409412
row8  -1.2639814  1.2209252 -1.2639814 -1.2639814  1.0386037  2.2623791
row9   2.2764681  2.1788296 -1.4847320 -1.4847320  1.6063104 -1.4847320
row10 -1.4670901 -1.4670901  2.3830575  1.5774323 -1.4670901  2.0592704
row11  2.0757509 -1.5618353 -1.5618353  0.3840748 -1.5618353  1.6162185
row12  2.8238833 -0.9827792  2.7548904  2.5137284  1.6562781  2.7307929
row13 -1.2462555 -1.2462555 -1.2462555 -1.2462555 -1.2462555 -1.2462555
row14 -1.5378434 -1.5378434 -1.5378434  2.1757287 -1.5378434  2.0175047
row15  1.9303896 -1.6249585 -1.6249585 -1.6249585 -1.6249585  2.1817040
row16 -1.7739110 -1.7739110 -0.1644731  2.0102786 -1.0807638  2.0972900
row17  1.4372281 -1.6072944  1.2830774 -1.6072944 -1.6072944 -1.6072944
row18 -1.3596274  2.5321929  0.4321321 -1.3596274 -1.3596274 -1.3596274
row19 -1.3344082  2.0328876  2.2491107  2.2765097  2.5367928 -1.3344082
row20 -1.7029417 -1.7029417 -1.7029417 -1.7029417 -1.7029417 -1.7029417
           col37      col38      col39      col40      col41      col42
row1   1.3790496  2.1029684 -1.3935391  1.4968326 -1.3935391  1.9386654
row2   0.9072310 -1.3953541 -1.3953541 -1.3953541 -1.3953541 -1.3953541
row3   2.3987550 -1.5132680  1.9207192 -1.5132680 -1.5132680 -1.5132680
row4  -1.5372546 -1.5372546 -0.4386423 -1.5372546 -1.5372546  2.2004151
row5   1.6852261 -0.5834574  2.0791304 -1.6820697 -1.6820697 -1.6820697
row6  -1.6401490  1.5787268  0.3057612  0.3057612 -1.6401490  1.4953452
row7  -1.4409412  2.3432485  2.1966450 -1.4409412 -1.4409412 -1.4409412
row8   0.1223129 -1.2639814  0.9332432 -1.2639814  1.8270610 -0.1653691
row9   2.4272910  1.0802173 -1.4847320 -1.4847320  1.2878567  0.5947095
row10  1.0178165 -1.4670901 -1.4670901 -1.4670901 -1.4670901 -1.4670901
row11 -1.5618353  2.2668061  2.1270442  1.0772220 -1.5618353  1.6570405
row12 -0.9827792  2.3494253 -0.9827792  2.3845166 -0.9827792 -0.9827792
row13  1.9317984 -0.1476432  0.9509691  0.5455040 -1.2462555 -1.2462555
row14  2.3741796  1.8961438 -1.5378434  1.0271060 -1.5378434 -0.1515490
row15 -1.6249585 -1.6249585  2.2036829 -1.6249585  2.2870645  0.8599482
row16  1.9396611  2.1179093  0.7910384 -0.1644731  1.8096080  0.5286741
row17  1.1007558  1.6885425  1.1652943  0.3386158 -1.6072944  1.5281998
row18 -1.3596274  1.6361049 -1.3596274  1.3484228 -1.3596274  0.8375972
row19 -1.3344082 -1.3344082 -1.3344082 -1.3344082  2.4722543 -1.3344082
row20 -1.7029417  2.0347280  2.0812480  2.1682593 -1.7029417  1.3415808
           col43      col44      col45      col46      col47      col48
row1  -1.3935391 -1.3935391 -1.3935391 -1.3935391  2.3441305 -1.3935391
row2  -1.3953541 -1.3953541  2.0058433 -1.3953541  1.7401401  0.9072310
row3  -1.5132680  1.4824643 -1.5132680 -1.5132680 -1.5132680 -1.5132680
row4   0.4086556  0.9476521  1.2959588  1.7208420 -1.5372546 -1.5372546
row5   0.5151549 -1.6820697  2.1680779 -1.6820697 -1.6820697 -1.6820697
row6   1.4508935 -1.6401490  2.2516713  1.8255869  1.6920555 -1.6401490
row7  -1.4409412  1.5034978 -1.4409412  1.0439655  2.1966450 -1.4409412
row8  -0.5708342  2.2913666 -1.2639814 -1.2639814 -1.2639814 -1.2639814
row9   1.3484813 -1.4847320 -1.4847320  2.2994576  2.3864690 -1.4847320
row10  1.4232816 -1.4670901  0.9308051  1.0178165 -1.4670901 -1.4670901
row11  1.6162185  2.3299850 -1.5618353 -1.5618353 -1.5618353  0.5176062
row12 -0.9827792 -0.9827792 -0.9827792 -0.9827792 -0.9827792 -0.9827792
row13 -1.2462555  2.4173062 -1.2462555 -1.2462555 -1.2462555  2.2502521
row14 -1.5378434 -1.5378434 -1.5378434 -1.5378434  2.1257182 -1.5378434
row15  2.2870645 -1.6249585 -0.9318113  1.9585605 -1.6249585  1.8715491
row16 -1.7739110  1.9637586  1.9872891 -1.7739110 -1.7739110  2.0547304
row17 -1.6072944 -1.6072944 -1.6072944 -1.6072944 -1.6072944  1.0317629
row18 -1.3596274  2.0076684 -1.3596274 -1.3596274  2.4690140  2.3780422
row19  2.4942332 -1.3344082 -1.3344082  0.4573512  2.3291534  2.5974174
row20 -1.7029417  2.0347280  0.5996434  1.0696471  1.1302717 -1.7029417
           col49      col50
row1   2.3200329 -1.3935391
row2   1.6491683  2.3423155
row3   1.0516813 -1.5132680
row4   1.0276948 -1.5372546
row5   1.7836662 -1.6820697
row6  -1.6401490  1.7938382
row7  -1.4409412  1.0439655
row8  -1.2639814 -1.2639814
row9  -1.4847320 -1.4847320
row10  2.0882579  2.4247302
row11  0.6353893  1.8054605
row12  1.7252710  2.6548070
row13 -0.1476432 -1.2462555
row14 -0.4392311 -1.5378434
row15  1.7762389  1.6708784
row16  2.0102786  0.6239843
row17 -0.5086821  1.7249101
row18 -1.3596274  1.2053220
row19 -1.3344082 -1.3344082
row20 -1.7029417  1.5551549
attr(,"parameters")
attr(,"parameters")$means
     row1      row2      row3      row4      row5      row6      row7      row8 
1.3935391 1.3953541 1.5132680 1.5372546 1.6820697 1.6401490 1.4409412 1.2639814 
     row9     row10     row11     row12     row13     row14     row15     row16 
1.4847320 1.4670901 1.5618353 0.9827792 1.2462555 1.5378434 1.6249585 1.7739110 
    row17     row18     row19     row20 
1.6072944 1.3596274 1.3344082 1.7029417 

attr(,"parameters")$pseudocount
[1] 1

attr(,"parameters")$margin
[1] 1

attr(,"decostand")
[1] "clr"
> # Expect error
> class(try(vegan::decostand(testdata, method = "clr")))=="try-error"
Error : 'clr' cannot be used with non-positive data: use pseudocount > 0
[1] TRUE
> class(try(vegan::decostand(testdata, method = "clr", pseudocount=0)))=="try-error"
Error : 'clr' cannot be used with non-positive data: use pseudocount > 0
[1] TRUE
> 
> # Tests that clr robust gives values that are approximately same if only 
> # one value per sample are changed to zero
> # Adds pseudocount
> test <- testdata+1 
> test2 <- test; test2[,1] <- 0
> 
> # clr robust transformations
> test <- vegan::decostand(test, method = "rclr")
> test2 <- vegan::decostand(test2, method = "rclr")
> 
> # Removes first cols
> test <- test[, -1]
> test2 <- test2[, -1]
> 
> # Expect high correlation
> cor(unlist(test), unlist(test2)) > 0.99
       col2  col3  col4  col5  col6  col7  col8  col9 col10 col11 col12 col13
col2   TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col3  FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col4  FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col5  FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col6  FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col7  FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
col8  FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
col9  FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
col10 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
col11 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
col12 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
col13 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
col14 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col15 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col16 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col17 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col18 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col19 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col20 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col21 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col22 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col23 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col24 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col25 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col26 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col27 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col28 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col29 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col30 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col31 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col32 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col33 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col34 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col35 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col36 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col37 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col38 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col39 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col40 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col41 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col42 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col43 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col44 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col45 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col46 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col47 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col48 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col49 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col50 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
      col14 col15 col16 col17 col18 col19 col20 col21 col22 col23 col24 col25
col2  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col3  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col4  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col5  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col6  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col7  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col8  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col9  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col10 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col11 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col12 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col13 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col14  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col15 FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col16 FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col17 FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col18 FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col19 FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
col20 FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
col21 FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
col22 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
col23 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
col24 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
col25 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
col26 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col27 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col28 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col29 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col30 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col31 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col32 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col33 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col34 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col35 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col36 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col37 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col38 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col39 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col40 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col41 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col42 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col43 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col44 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col45 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col46 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col47 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col48 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col49 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col50 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
      col26 col27 col28 col29 col30 col31 col32 col33 col34 col35 col36 col37
col2  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col3  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col4  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col5  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col6  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col7  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col8  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col9  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col10 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col11 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col12 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col13 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col14 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col15 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col16 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col17 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col18 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col19 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col20 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col21 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col22 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col23 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col24 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col25 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col26  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col27 FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col28 FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col29 FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col30 FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col31 FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
col32 FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
col33 FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
col34 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
col35 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
col36 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
col37 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
col38 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col39 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col40 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col41 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col42 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col43 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col44 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col45 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col46 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col47 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col48 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col49 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col50 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
      col38 col39 col40 col41 col42 col43 col44 col45 col46 col47 col48 col49
col2  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col3  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col4  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col5  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col6  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col7  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col8  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col9  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col10 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col11 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col12 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col13 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col14 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col15 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col16 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col17 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col18 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col19 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col20 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col21 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col22 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col23 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col24 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col25 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col26 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col27 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col28 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col29 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col30 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col31 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col32 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col33 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col34 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col35 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col36 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col37 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col38  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col39 FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col40 FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col41 FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col42 FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
col43 FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
col44 FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
col45 FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
col46 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
col47 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
col48 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
col49 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
col50 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
      col50
col2  FALSE
col3  FALSE
col4  FALSE
col5  FALSE
col6  FALSE
col7  FALSE
col8  FALSE
col9  FALSE
col10 FALSE
col11 FALSE
col12 FALSE
col13 FALSE
col14 FALSE
col15 FALSE
col16 FALSE
col17 FALSE
col18 FALSE
col19 FALSE
col20 FALSE
col21 FALSE
col22 FALSE
col23 FALSE
col24 FALSE
col25 FALSE
col26 FALSE
col27 FALSE
col28 FALSE
col29 FALSE
col30 FALSE
col31 FALSE
col32 FALSE
col33 FALSE
col34 FALSE
col35 FALSE
col36 FALSE
col37 FALSE
col38 FALSE
col39 FALSE
col40 FALSE
col41 FALSE
col42 FALSE
col43 FALSE
col44 FALSE
col45 FALSE
col46 FALSE
col47 FALSE
col48 FALSE
col49 FALSE
col50  TRUE
> 
> ############################# NAMES ####################################
> 
> # Tests that dimensins have correct names
> all(rownames(vegan::decostand(testdata+1, method = "clr")) == rownames(testdata))
[1] TRUE
> all(colnames(vegan::decostand(testdata, method = "clr", pseudocount=1)) == colnames(testdata))
[1] TRUE
> all(rownames(vegan::decostand(testdata, method = "rclr")) == rownames(testdata))
[1] TRUE
> all(colnames(vegan::decostand(testdata, method = "rclr")) == colnames(testdata))
[1] TRUE
> 
> ########################################################################
> 
> # Count vs. Relative data
> 
> # CLR is identical with count and relative data
> a1 <- vegan::decostand(testdata.with.pseudo, method = "clr")
> a2 <- vegan::decostand(relative.with.pseudo, method = "clr")
> max(abs(a1-a2)) < 1e-6 # Tolerance
[1] TRUE
> 
> # rCLR is identical with count and relative data
> a1 <- vegan::decostand(testdata.with.pseudo, method = "rclr")
> a2 <- vegan::decostand(relative.with.pseudo, method = "rclr")
> max(abs(a1-a2)) < 1e-6 # Tolerance
[1] TRUE
> 
> # ALR is identical with count and relative data
> a1 <- vegan::decostand(testdata.with.pseudo, method = "alr")
> a2 <- vegan::decostand(relative.with.pseudo, method = "alr")
> max(abs(a1-a2)) < 1e-6 # Tolerance
[1] FALSE
> 
> ####### # ALR transformation drops one feature ################
> ncol(vegan::decostand(testdata.with.pseudo, "alr")) == ncol(testdata.with.pseudo)-1
[1] FALSE
> 
> 
> 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
   0.84    0.10    0.95