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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  0.0000000  0.0000000  0.00000000  0.68645464  0.00000000
row2  -1.0484992  0.0000000 -1.3361813 -0.03689827 -0.13220845  0.00000000
row3   0.0000000  0.4890304 -0.1040333  0.00000000  0.10954076 -0.20411680
row4   0.0000000  0.0000000  0.0000000  0.59519516  0.05619866 -1.88971149
row5   0.0000000  0.0000000  0.0000000 -2.89523658 -1.28579867  0.19580587
row6   0.0000000 -1.2698511  0.5684283  0.00000000  0.00000000 -0.73085464
row7   0.0000000  0.0000000  0.0000000  0.00000000 -0.22695995  0.00000000
row8   0.7703369  0.0000000  0.0000000  0.00000000  0.74734741  0.77033693
row9  -1.5049007  0.9800060  0.0000000  0.00000000  0.47610080 -0.05798168
row10  0.0000000  0.4096642  1.0433880  0.32962150 -0.28348298  0.00000000
row11  0.7008636  0.3971812  0.0000000  0.00000000  0.29539850 -0.14643425
row12 -0.4186410  0.0000000  0.5798878  0.74896415  0.00000000  0.37628887
row13  0.0000000  0.0000000  0.0000000  0.00000000 -1.03142624  0.00000000
row14  0.0000000  0.0000000  0.1776279  0.29898877  0.00000000  0.00000000
row15  0.0000000 -0.4111861 -0.5782401  0.00000000  0.63478250  0.00000000
row16  0.0000000  0.0000000  0.1489707  0.00000000  0.00000000 -0.12728263
row17  0.0000000  0.0000000  0.3344868  0.77385344 -0.14243729  0.26302782
row18  0.0000000  0.0000000  0.8918384  0.74665644  1.06511017  0.00000000
row19  0.0000000  0.0000000  0.8272681  0.22113225  1.06843011  0.00000000
row20  0.1688399  0.6177901  0.3229905  0.06347934  0.00000000  0.00000000
            col7       col8        col9       col10      col11       col12
row1   0.7716124  0.5933642  0.00000000  0.00000000  0.4633111  0.00000000
row2   0.7222069  0.7007007  0.58563134  0.00000000  0.0000000  0.00000000
row3   0.0000000  0.0000000 -0.31534244  0.00000000  0.1472811 -0.44050558
row4   0.0000000  0.1251915  1.20133096 -0.79109920  1.2883423  0.25035467
row5   0.0000000  0.0000000  0.00000000  0.24025764  0.0000000 -0.94932643
row6   0.0000000  0.0000000  0.00000000  0.65543972  0.0000000  0.00000000
row7   0.0000000  0.4322857 -0.04463839  0.00000000  0.0000000  0.00000000
row8   0.0000000  0.0000000  0.00000000  0.48265486  0.0000000 -0.44890335
row9   0.0000000 -1.0994356  0.00000000  0.00000000  0.0000000  0.05324395
row10  0.0000000  0.8404471  0.55276505  0.00000000 -1.2389944  0.00000000
row11  0.0000000  0.0000000  0.85501429  0.00000000  0.6267556  0.85501429
row12  0.0000000  0.0000000  0.79244926  0.00000000 -0.7551132  0.00000000
row13  0.0000000  0.0000000  0.48839951  0.80685324 -0.3382791  0.91448390
row14  0.0000000  0.2093766 -0.31192031  0.14483809  0.5048408  0.07584522
row15  0.0000000  0.6073835  0.00000000  0.76153421 -2.9761354  0.00000000
row16  0.0000000 -0.8204298  0.54287503 -0.73341843  0.6318225  0.00000000
row17  0.0000000  0.0000000 -1.64651469  0.01171339 -0.3937517  0.52253901
row18  0.8362686  0.0000000  0.45897437  0.53901707 -2.0259323  0.00000000
row19 -0.3666544 -1.9760923  0.88610855  0.00000000  0.1033492  0.00000000
row20 -1.0351329  0.0000000  0.00000000 -1.03513295  0.0000000  0.00000000
           col13      col14      col15      col16      col17      col18
row1   0.0000000  0.0000000 -0.7223126  0.4061527  0.7509932  0.1378887
row2   0.2732567  0.0000000  0.0000000  0.0000000  0.4829772 -0.2947274
row3   0.0000000  0.2854314  0.0000000 -1.7622614  0.3171801 -0.2041168
row4   0.0000000  0.8511285  0.0000000  0.0000000  0.0000000  0.0000000
row5  -1.1034771  0.8424330 -1.7966243  0.6012710  0.7683251  0.5704993
row6   0.0000000  0.6554397  0.0000000  0.0000000  0.6760590  0.6554397
row7  -1.3255722  0.0000000  0.0000000  0.0000000  0.3971944 -0.1017968
row8   0.0000000  0.0000000  0.7928098  0.0000000  0.0000000  0.7473474
row9   0.0000000  0.0000000  0.2442992  0.0000000 -0.4062884  0.0000000
row10  0.0000000  0.3704435 -0.2093750  0.6480752 -0.6512078  0.0000000
row11  0.0000000 -0.7342209  0.5741119  0.0000000 -0.3977487  0.6007801
row12  0.0000000  0.5258206 -0.4927490  0.7035018  0.3095975  0.0000000
row13  0.0000000  0.8293261 -0.4123870  0.7362357  0.0000000  0.6061825
row14  0.0000000  0.0000000  0.0000000  0.0000000  0.1776279  0.0000000
row15 -0.2035467  0.5203722  0.2819611  0.0000000  0.0000000  0.0000000
row16  0.5428750  0.0000000  0.0000000  0.6528759 -2.5251779  0.5883374
row17 -0.4678597 -1.0868989  0.8173386  0.0000000  0.0000000 -1.0868989
row18 -2.7190795  0.3254430  0.1712923  0.9444822 -0.4164944  0.0000000
row19  0.0000000 -2.6692395  0.0000000  0.8861086  0.0000000  0.0000000
row20  0.2016297 -0.7474509 -0.9297724  0.0000000  0.0000000  0.0000000
            col19      col20      col21      col22      col23      col24
row1   0.43514021  0.0000000 -0.0291654  0.6409923  0.0000000  0.0000000
row2   0.00000000  0.0000000 -0.4198905  0.5356209  0.3685668  0.7432603
row3   0.00000000 -0.5836064  0.0000000  0.0000000  0.1836487  0.6356339
row4  -2.58285867  0.2503547 -2.5828587 -1.8897115 -0.6369485  0.0000000
row5   0.00000000  0.0000000  0.0000000  0.7683251  0.8424330 -0.5926515
row6  -0.91317620  0.0000000  0.0000000  0.6554397  0.0000000 -0.2200290
row7   0.53072575 -0.2269599 -0.3700608  0.0000000  0.0000000  0.0000000
row8   0.00000000  0.0000000 -0.7112676  0.0000000  0.0000000  0.1216415
row9   0.00000000  0.5427922  0.0000000  0.7976844  0.2442992  0.0000000
row10  0.00000000  0.0000000 -0.3635257  0.7069157  0.0000000  0.0000000
row11  0.00000000  0.0000000  0.0000000  0.0000000  0.4895545  0.0000000
row12  0.20039820  0.0000000  0.0000000  0.0000000 -0.8604738  0.0000000
row13  0.80685324  0.8938646  0.0000000 -0.8978949  0.0000000  0.4566508
row14  0.00000000  0.0000000  0.0000000 -0.3659875 -0.2606270  0.0000000
row15 -0.03169643 -0.4111861  0.3197015  0.0000000  0.2019184  0.8080542
row16 -0.12728263  0.4452366 -0.2225928 -0.9157400  0.5883374  0.5658646
row17  0.00000000  0.0000000  0.4011782 -0.7302240  0.1026852  0.0000000
row18  0.00000000  0.0000000  0.0000000 -2.7190795  0.0000000 -0.2341728
row19  0.94167840  0.0000000 -1.9760923  0.5088143  1.0196399  1.0919606
row20  0.00000000  0.0000000  0.2641500  0.2333784  0.0000000  0.6594628
           col25      col26       col27       col28       col29       col30
row1  -1.0407663 -0.4121576 -0.12447558  0.00000000  0.00000000  0.21199666
row2   0.4555782 -1.3361813  0.00000000  0.00000000  0.00000000  0.00000000
row3   0.0000000  0.0000000 -0.20411680  0.00000000  0.00000000  0.00000000
row4   0.0000000  0.1897300  0.00000000  0.81833871  0.00000000  0.00000000
row5   0.0000000  0.8424330  0.10049569  0.00000000  0.00000000  0.97596443
row6   0.0000000  0.0000000  0.00000000  0.00000000  0.00000000  0.00000000
row7   0.3608267 -1.1432507  0.95681015  0.00000000  0.00000000  0.64850879
row8   0.0000000  0.7238169  0.42013450  0.00000000  0.00000000  0.54149536
row9   0.6351655 -0.9452849  0.10453725  0.69232391  0.00000000  0.10453725
row10  0.0000000  0.7350866  0.00000000  0.00000000 -1.23899442  0.00000000
row11  0.7918354  0.0000000 -0.09236703  0.00000000  0.00000000  0.70086361
row12 -1.6714040 -0.0131759  0.43880922  0.00000000 -1.95908605  0.00000000
row13  0.8068532  0.5191712  0.00000000 -0.14412305  0.00000000  0.01839588
row14  0.0000000  0.6556637  0.00000000  0.03947758  0.65566371  0.27000123
row15  0.0000000 -0.5782401  0.42506197  0.00000000 -0.14292207  0.87401219
row16  0.4452366  0.5193445  0.58833741  0.33702298  0.14897075  0.11387943
row17  0.0000000  0.0000000  0.40117816  0.00000000 -0.83558447 -0.26022033
row18  0.7149077 -0.9273200  0.41641475  0.05350926  0.05350926  0.00000000
row19  0.0000000  0.0000000 -1.05980160  0.00000000  0.00000000  0.00000000
row20  0.5053121  0.0000000 -0.18783509  0.00000000  0.00000000  0.00000000
           col31       col32       col33       col34      col35       col36
row1   0.0000000  0.00000000  0.31377935  0.00000000 -0.7223126  0.01528637
row2   0.0000000  0.67872177  0.50964544  0.00000000  0.0000000  0.09093510
row3   0.0000000  0.00000000  0.00000000  0.00000000  0.0000000  0.14728108
row4   0.0000000  0.00000000  1.26728893  0.00000000  0.0000000  0.63601715
row5   0.9965837 -0.81579504  0.60127098  0.00000000  0.0000000  0.14928586
row6  -0.8178660  0.52190832  0.15153454  0.00000000  0.0000000  0.33958677
row7   0.0000000  0.00000000  0.36082672  0.00000000  0.6203379  0.00000000
row8  -1.6275583  0.00000000  0.31835180 -1.91524042  0.5125078  0.83629490
row9   0.2442992  0.82237704  0.00000000  0.36690151  0.0000000  0.44100948
row10 -1.2389944  1.04338796  0.00000000  0.00000000  0.0000000  0.00000000
row11  0.0000000  0.39718119  0.00000000 -0.73422092  0.0000000  0.00000000
row12 -2.3645512  0.00000000  0.49764972  0.40803756  0.0000000  0.00000000
row13  0.0000000  0.00000000  0.28076014  0.35486812  0.6602498 -1.36789848
row14  0.0000000 -1.05913471 -0.85846402 -0.07830546  0.0000000  0.00000000
row15  0.0000000  0.00000000  0.00000000  0.39116042 -0.8966939  0.00000000
row16  0.0000000 -0.44573636  0.00000000 -1.27241493  0.0000000  0.33702298
row17  0.0000000  0.87921396  0.00000000  0.00000000 -0.5479024  0.00000000
row18  0.0000000  0.27665281  0.00000000  0.00000000  0.7149077  0.91850670
row19  1.0684301  1.15940189  0.03881069  0.00000000  0.0000000  0.00000000
row20  0.0000000 -0.05430369  0.00000000  0.40522864  0.0000000  0.00000000
           col37       col38      col39      col40       col41      col42
row1  -0.9229833  0.54335380 -0.2869945 -1.7339135 -0.12447558  0.7918152
row2   0.0000000  0.00000000  0.7638796  0.0000000  0.00000000  0.6097289
row3   0.0000000  0.00000000  0.0000000  0.2854314  0.00000000  0.0000000
row4  -0.7910992  0.00000000  0.4616638  0.7493458  0.00000000  0.0000000
row5   0.4006003  0.00000000  0.0000000 -0.1871864  0.00000000 -0.8157950
row6   0.5219083 -0.17123886  0.0000000  0.0000000  0.33958677  0.5219083
row7   0.0000000  0.00000000  0.5614974  0.0000000 -0.98910000  0.0000000
row8  -1.4044148  0.00000000  0.0000000  0.0000000  0.51250782 -1.9152404
row9  -0.2521377 -0.69397045  0.5427922  0.1045372  0.00000000  0.0000000
row10  0.0000000  0.00000000  0.0000000  0.0000000  0.00000000  0.0000000
row11  0.8343950 -0.32875581  0.0000000  0.0986882 -0.20359267  0.0000000
row12  0.6311811  0.03334412  0.0000000  0.0000000  0.49764972  0.6058633
row13 -1.8787241  0.45665081 -1.8787241  0.7362357  0.00000000  0.0000000
row14  0.1448381  0.00000000 -1.6469214  0.0000000  0.27000123 -0.4837706
row15  0.6874262 -0.08576365  0.0000000  0.0000000 -0.77891083  0.6347825
row16  0.1138794  0.00000000  0.0000000  0.3651939 -0.04027125  0.0000000
row17  0.7738534  0.49355148  0.2630278  0.7738534  0.75138059  0.0000000
row18  0.0000000 -0.32118419  0.0000000  0.0000000  0.41641475  0.0000000
row19  0.0000000 -1.28294515  0.8272681  0.0000000 -0.58979797  1.2019615
row20 -0.6674082  0.00000000  0.2940030  0.0000000  0.00000000  0.0000000
            col43       col44        col45      col46      col47       col48
row1  -0.02916540  0.00000000  0.000000000 -0.5552585 -0.4121576 -0.28699451
row2   0.00000000  0.00000000 -1.182030575  0.0000000  0.7007007 -2.43479354
row3   0.48903038 -0.01306157  0.000000000  0.0000000  0.2526416  0.48903038
row4   0.78443716  0.91364889  0.189730048  0.8183387  0.0000000  1.05472749
row5   0.66011148  1.01678643  0.818335488  0.0000000  0.0000000  0.00000000
row6   0.49781077 -0.08026708  0.000000000  0.0000000  0.0000000 -1.42400183
row7   0.00000000  0.00000000  0.009428829  0.0000000  0.0000000  0.00000000
row8   0.83629490  0.16420113 -1.222093236 -0.4489033  0.0000000  0.07718975
row9  -0.40628838 -1.79258274 -1.504900665  0.5745409  0.0000000  0.00000000
row10  0.00000000  0.00000000  0.000000000  0.0000000 -0.1403821  0.00000000
row11 -0.09236703 -0.32875581 -0.471856655 -2.3436588 -2.3436588  0.00000000
row12  0.00000000  0.00000000  0.605863308  0.5798878  0.0000000  0.00000000
row13  0.54902413  0.00000000 -1.878724105 -0.7801118 -1.1855769  0.00000000
row14  0.27000123  0.17762791  0.177627914 -0.2118369  0.0000000  0.55030320
row15  0.60738353 -0.57824014  0.000000000  0.0000000  0.4896005  0.00000000
row16  0.00000000  0.00000000  0.000000000  0.0000000  0.0000000  0.00000000
row17  0.26302782  0.49355148  0.000000000  0.1452448  0.5225390  0.00000000
row18  0.00000000  0.00000000 -1.332785102  0.0000000  0.6131250  0.00000000
row19 -1.28294515  1.18090809  0.000000000  0.4662547 -2.6692395  0.00000000
row20  0.00000000  0.13493831  0.000000000  0.1688399  0.3229905  0.00000000
           col49      col50
row1   0.0000000  0.6409923
row2   0.0000000  0.0000000
row3   0.0000000  0.0000000
row4  -2.5828587  1.2457827
row5   0.0000000 -0.9493264
row6  -1.6063234  0.4731182
row7  -0.4501035  0.0000000
row8   0.0000000  0.3183518
row9   0.6923239  0.0000000
row10 -0.5458472 -0.7689908
row11  0.0000000  0.0000000
row12  0.0000000  0.0000000
row13  0.8728112  0.0000000
row14  0.2989888  0.1776279
row15  0.4578518 -1.0302253
row16  0.0000000 -0.4457364
row17 -0.5479024 -1.2410496
row18  0.5390171  0.0000000
row19  0.0000000  0.3752829
row20  0.2940030  0.0000000
attr(,"parameters")
attr(,"parameters")$means
    row1     row2     row3     row4     row5     row6     row7     row8 
3.120208 3.127941 3.148556 2.582859 2.895237 3.215761 2.935010 3.013853 
    row9    row10    row11    row12    row13    row14    row15    row16 
2.891195 2.848432 3.036806 3.057698 2.977336 3.256359 2.976135 3.218325 
   row17    row18    row19    row20 
3.032809 2.719079 2.669240 3.232358 

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

attr(,"decostand")
[1] "rclr"
> vegan::decostand(testdata, method = "clr", pseudocount=1)
            col1       col2       col3       col4       col5       col6
row1  -1.8404004 -1.8404004 -1.8404004 -1.8404004  1.9882410 -1.8404004
row2   0.6645783 -1.5326463  0.4132638  1.6028479  1.5118761 -1.5326463
row3  -1.3430921  2.3204696  1.7479504 -1.3430921  1.9527448  1.6526402
row4  -1.5291142 -1.5291142 -1.5291142  1.6897616  1.1789360 -0.4305019
row5  -1.6717938 -1.6717938 -1.6717938 -0.9786466  0.1199657  1.4637004
row6  -1.5033505  0.5760910  2.3033120 -1.5033505 -1.5033505  1.0615989
row7  -1.1391814 -1.1391814 -1.1391814 -1.1391814  1.6334074 -1.1391814
row8   2.2014029 -1.6052596 -1.6052596 -1.6052596  2.1789300  2.2014029
row9  -0.1093110  2.1730714 -1.7187489 -1.7187489  1.6824485  1.1716229
row10 -1.1684568  2.1273800  2.7435662  2.0504190  1.4706005 -1.1684568
row11  2.1447564  1.8492922 -1.6164437 -1.6164437  1.7508521  1.3279953
row12  1.1433592 -1.5646910  2.0988707  2.2639504 -1.5646910  1.9010449
row13 -1.8320824 -1.8320824 -1.8320824 -1.8320824  0.2473592 -1.8320824
row14 -1.8481861 -1.8481861  1.6175498  1.7353329 -1.8481861 -1.8481861
row15 -1.7692929  0.8697644  0.7156137 -1.7692929  1.8682932 -1.7692929
row16 -1.8980901 -1.8980901  1.5031072 -1.8980901 -1.8980901  1.2374041
row17 -1.9171994 -1.9171994  1.4839979  1.9114420  1.0272395  1.4150051
row18 -1.5315316 -1.5315316  2.1060546  1.9649760  2.2751309 -1.5315316
row19 -1.5143633 -1.5143633  2.0119973  1.4300757  2.2468369 -1.5143633
row20  1.9921334  2.4293472  2.1416651  1.8903507 -1.4418538 -1.4418538
            col7       col8       col9      col10      col11      col12
row1   2.0716226  1.8972692 -1.8404004 -1.8404004  1.7705175 -1.8404004
row2   2.3385547  2.3175013  2.2050233 -1.5326463 -1.5326463 -1.5326463
row3  -1.3430921 -1.3430921  1.5472797 -1.3430921  1.9891124  1.4294966
row4  -1.5291142  1.2434745  2.2775483  0.4167960  2.3627061  1.3612576
row5  -1.6717938 -1.6717938 -1.6717938  1.5062600 -1.6717938  0.4076477
row6  -1.5033505 -1.5033505 -1.5033505  2.3884698 -1.5033505 -1.5033505
row7  -1.1391814  2.2620160  1.8052576 -1.1391814 -1.1391814 -1.1391814
row8  -1.6052596 -1.6052596 -1.6052596  1.9211009 -1.6052596  1.0337977
row9  -1.7187489  0.2271613 -1.7187489 -1.7187489 -1.7187489  1.2769834
row10 -1.1684568  2.5451152  2.2655304 -1.1684568  0.6233027 -1.1684568
row11 -1.6164437 -1.6164437  2.2955793 -1.6164437  2.0724357  2.2955793
row12 -1.5646910 -1.5646910  2.3065100 -1.5646910  0.8332043 -1.5646910
row13 -1.8320824 -1.8320824  1.6644252  1.9745801  0.8759678  2.0799406
row14 -1.8481861  1.6483215  1.1475462  1.5858012  1.9360036  1.5191098
row15 -1.7692929  1.8416250 -1.7692929  1.9919072 -1.0761457 -1.7692929
row16 -1.8980901  0.5868165  1.8860995  0.6668592  1.9731109 -1.8980901
row17 -1.9171994 -1.9171994 -0.3077615  1.1738430  0.7908508  1.6663195
row18  2.0519874 -1.5315316  1.6873443  1.7643053 -0.4329193 -1.5315316
row19  0.8835320 -0.4157510  2.0691557 -1.5143633  1.3188501 -1.5143633
row20  0.8607313 -1.4418538 -1.4418538  0.8607313 -1.4418538 -1.4418538
           col13      col14      col15      col16      col17      col18
row1  -1.8404004 -1.8404004  0.6445063  1.7149477  2.0514199  1.4554365
row2   1.9013409 -1.5326463 -1.5326463 -1.5326463  2.1049398  1.3577254
row3  -1.3430921  2.1226438 -1.3430921  0.2663458  2.1534155  1.6526402
row4  -1.5291142  1.9366217 -1.5291142 -1.5291142 -1.5291142 -1.5291142
row5   0.2741163  2.0894063 -0.2854994  1.8545667  2.0170857  1.8247138
row6  -1.5033505  2.3884698 -1.5033505 -1.5033505  2.4086725  2.3884698
row7   0.6525781 -1.1391814 -1.1391814 -1.1391814  2.2281145  1.7511904
row8  -1.6052596 -1.6052596  2.2233818 -1.6052596 -1.6052596  2.1789300
row9  -1.7187489 -1.7187489  1.4593049 -1.7187489  0.8462005 -1.7187489
row10 -1.1684568  2.0896397  1.5395934  2.3579037  1.1341283 -1.1684568
row11 -1.6164437  0.7814515  2.0211424 -1.6164437  1.0916065  2.0471179
row12 -1.5646910  2.0462269  1.0743663  2.2194986  1.8365064 -1.5646910
row13 -1.8320824  1.9965590  0.8069750  1.9055873 -1.8320824  1.7788355
row14 -1.8481861 -1.8481861 -1.8481861 -1.8481861  1.6175498 -1.8481861
row15  1.0639204  1.7570676  1.5265439 -1.7692929 -1.7692929 -1.7692929
row16  1.8860995 -1.8980901 -1.8980901  1.9937302 -0.7994779  1.9305513
row17  0.7218579  0.1622421  1.9540016 -1.9171994 -1.9171994  0.1622421
row18 -0.8383844  1.5595109  1.4129074  2.1573479  0.8663637 -1.5315316
row19 -1.5143633 -0.8212161 -1.5143633  2.0691557 -1.5143633 -1.5143633
row20  2.0238821  1.1230955  0.9560415 -1.4418538 -1.4418538 -1.4418538
           col19      col20      col21      col22      col23      col24
row1   1.7431186 -1.8404004  1.2950938  1.9437893 -1.8404004 -1.8404004
row2  -1.5326463 -1.5326463  1.2399424  2.1562331  1.9937142  2.3591740
row3  -1.3430921  1.2959652 -1.3430921 -1.3430921  2.0242037  2.4635704
row4  -0.8359670  1.3612576 -0.8359670 -0.4305019  0.5503274 -1.5291142
row5  -1.6717938 -1.6717938 -1.6717938  2.0170857  2.0894063  0.7261015
row6   0.8945448 -1.5033505 -1.5033505  2.3884698 -1.5033505  1.5411719
row7   2.3573262  1.6334074  1.4998760 -1.1391814 -1.1391814 -1.1391814
row8  -1.6052596 -1.6052596  0.7926357 -1.6052596 -1.6052596  1.5727942
row9  -1.7187489  1.7469870 -1.7187489  1.9948232  1.4593049 -1.7187489
row10 -1.1684568 -1.1684568  1.3964925  2.4150621 -1.1684568 -1.1684568
row11 -1.6164437 -1.6164437 -1.6164437 -1.6164437  1.9389043 -1.6164437
row12  1.7311459 -1.5646910 -1.5646910 -1.5646910  0.7378941 -1.5646910
row13  1.9745801  2.0597379 -1.8320824  0.3651422 -1.8320824  1.6336535
row14 -1.8481861 -1.8481861 -1.8481861  1.0962529  1.1963364 -1.8481861
row15  1.2264393  0.8697644  1.5629116 -1.7692929  1.4495829  2.0373696
row16  1.2374041  1.7907893  1.1464323  0.4998051  1.9305513  1.9085724
row17 -1.9171994 -1.9171994  1.5485365  0.4806958  1.2608544 -1.9171994
row18 -1.5315316 -1.5315316 -1.5315316 -0.8383844 -1.5315316  1.0334178
row19  2.1232229 -1.5143633 -0.4157510  1.7045126  2.1992088  2.2698264
row20 -1.4418538 -1.4418538  2.0845067  2.0546538 -1.4418538  2.4701692
            col25      col26      col27     col28      col29      col30
row1   0.35682421  0.9321884  1.2041221 -1.840400 -1.8404004  1.5268955
row2   2.07827159  0.4132638 -1.5326463 -1.532646 -1.5326463 -1.5326463
row3  -1.34309209 -1.3430921  1.6526402 -1.343092 -1.3430921 -1.3430921
row4  -1.52911418  1.3040992 -1.5291142  1.904873 -1.5291142 -1.5291142
row5  -1.67179380  2.0894063  1.3727286 -1.671794 -1.6717938  2.2200265
row6  -1.50335050 -1.5033505 -1.5033505 -1.503351 -1.5033505 -1.5033505
row7   2.19302314  0.8067288  2.7728416 -1.139181 -1.1391814  2.4717365
row8  -1.60525962  2.1559405  1.8604763 -1.605260 -1.6052596  1.9782593
row9   1.83659918  0.3606927  1.3257736  1.892169 -1.7187489  1.3257736
row10 -1.16845682  2.4424611 -1.1684568 -1.168457  0.6233027 -1.1684568
row11  2.23370388 -1.6164437  1.3792885 -1.616444 -1.6164437  2.1447564
row12  0.04474692  1.5263515  1.9616695 -1.564691 -0.1783966 -1.5646910
row13  1.97458012  1.6942782 -1.8320824  1.058289 -1.8320824  1.2124401
row14 -1.84818605  2.0836396 -1.8481861  1.484018  2.0836396  1.7071620
row15 -1.76929293  0.7156137  1.6646943 -1.769293  1.1210788  2.1019081
row16  1.79078931  1.8631100  1.9305513  1.685429  1.5031072  1.4692057
row17 -1.91719944 -1.9171994  1.5485365 -1.917199  0.3853856  0.9160139
row18  1.93420435  0.4143786  1.6465223  1.301682  1.3016818 -1.5315316
row19 -1.51436326 -1.5143633  0.2773962 -1.514363 -1.5143633 -1.5143633
row20  2.31934631 -1.4418538  1.6491886 -1.441854 -1.4418538 -1.4418538
             col31      col32      col33      col34      col35      col36
row1  -1.840400370 -1.8404004  1.6253355 -1.8404004  0.6445063  1.3376535
row2  -1.532646319  2.2959951  2.1309153 -1.5326463 -1.5326463  1.7254502
row3  -1.343092091 -1.3430921 -1.3430921 -1.3430921 -1.3430921  1.9891124
row4  -1.529114180 -1.5291142  2.3420868 -1.5291142 -1.5291142  1.7289824
row5   2.240229201  0.5254308  1.8545667 -1.6717938 -1.6717938  1.4192486
row6   0.981556147  2.2578496  1.8978469 -1.5033505 -1.5033505  2.0801684
row7  -1.139181370 -1.1391814  2.1930231 -1.1391814  2.4443376 -1.1391814
row8   0.004178293 -1.6052596  1.7620362 -0.2189653  1.9500884  2.2659414
row9   1.459304950  2.0189207 -1.7187489  1.5770880 -1.7187489  1.6485469
row10  0.623302652  2.7435662 -1.1684568 -1.1684568 -1.1684568 -1.1684568
row11 -1.616443726  1.8492922 -1.6164437  0.7814515 -1.6164437 -1.6164437
row12 -0.466078701 -1.5646910  2.0188279  1.9318166 -1.5646910 -1.5646910
row13 -1.832082365 -1.8320824  1.4637545  1.5352135  1.8314793 -0.0403229
row14 -1.848186054  0.4543990  0.6367206  1.3706898 -1.8481861 -1.8481861
row15 -1.769292927 -1.7692929 -1.7692929  1.6319045  0.4279317 -1.7692929
row16 -1.898090140  0.9351232 -1.8980901  0.1813514 -1.8980901  1.6854288
row17 -1.917199445  2.0146262 -1.9171994 -1.9171994  0.6477499 -1.9171994
row18 -1.531531553  1.5129909 -1.5315316 -1.5315316  1.9342043  2.1320301
row19  2.246836852  2.3357843  1.2582255 -1.5143633 -1.5143633 -1.5143633
row20 -1.441853810  1.7770220 -1.4418538  2.2217078 -1.4418538 -1.4418538
           col37       col38       col39      col40      col41      col42
row1   0.4621847  1.84847908  1.04997139 -0.2309625  1.2041221  2.0914253
row2  -1.5326463 -1.53264632  2.37937669 -1.5326463 -1.5326463  2.2285538
row3  -1.3430921 -1.34309209 -1.34309209  2.1226438 -1.3430921 -1.3430921
row4   0.4167960 -1.52911418  1.56192827  1.8381817 -1.5291142 -1.5291142
row5   1.6604107 -1.67179380 -1.67179380  1.1007949 -1.6717938  0.5254308
row6   2.2578496  1.58769195 -1.50335050 -1.5033505  2.0801684  2.2578496
row7  -1.1391814 -1.13918137  2.38717916 -1.1391814  0.9402602 -1.1391814
row8   0.1864998 -1.60525962 -1.60525962 -1.6052596  1.9500884 -0.2189653
row9   0.9893013  0.58383621  1.74698702  1.3257736 -1.7187489 -1.7187489
row10 -1.1684568 -1.16845682 -1.16845682 -1.1684568 -1.1684568 -1.1684568
row11  2.2753766  1.15614500 -1.61644373  1.5616101  1.2739280 -1.6164437
row12  2.1488811  1.57080323 -1.56469099 -1.5646910  2.0188279  2.1241885
row13 -0.4457880  1.63365354 -0.44578800  1.9055873 -1.8320824 -1.8320824
row14  1.5858012 -1.84818605 -0.05642658 -1.8481861  1.7071620  0.9850273
row15  1.9195865  1.17514605 -1.76929293 -1.7692929  0.5332922  1.8682932
row16  1.4692057 -1.89809014 -1.89809014  1.7128278  1.3207857 -1.8980901
row17  1.9114420  1.63814862  1.41500507  1.9114420  1.8894630 -1.9171994
row18 -1.5315316  0.95337510 -1.53153155 -1.5315316  1.6465223 -1.5315316
row19 -1.5143633  0.09507465  2.01199726 -1.5143633  0.6828613  2.3774570
row20  1.1972035 -1.44185381  2.11349425 -1.4418538 -1.4418538 -1.4418538
            col43      col44       col45      col46      col47      col48
row1   1.29509385 -1.8404004 -1.84040037  0.7986570  0.9321884  1.0499714
row2  -1.53264632 -1.5326463  0.54679522 -1.5326463  2.3175013 -0.4340340
row3   2.32046956  1.8349617 -1.34309209 -1.3430921  2.0908951  2.3204696
row4   1.87208320  1.9972463  1.30409916  1.9048730 -1.5291142  2.1344475
row5   1.91172513  2.2600318  2.06587581 -1.6717938 -1.6717938 -1.6717938
row6   2.23431912  1.6747033 -1.50335050 -1.5033505 -1.5033505  0.4425596
row7  -1.13918137 -1.1391814  1.85655090 -1.1391814 -1.1391814 -1.1391814
row8   2.26594139  1.6136162  0.34065053  1.0337977 -1.6052596  1.5302346
row9   0.84620048 -0.3324545 -0.10931097  1.7777587 -1.7187489 -1.7187489
row10 -1.16845682 -1.1684568 -1.16845682 -1.1684568  1.6041319 -1.1684568
row11  1.37928855  1.1561450  1.02261360 -0.5178314 -0.5178314 -1.6164437
row12 -1.56469099 -1.5646910  2.12418846  2.0988707 -1.5646910 -1.5646910
row13  1.72326570 -1.8320824 -0.44578800  0.4705027  0.1138278 -1.8320824
row14  1.70716201  1.6175498  1.61754985  1.2428564 -1.8481861  1.9804553
row15  1.84162499  0.7156137 -1.76929293 -1.7692929  1.7272146 -1.7692929
row16 -1.89809014 -1.8980901 -1.89809014 -1.8980901 -1.8980901 -1.8980901
row17  1.41500507  1.6381486 -1.91719944  1.3016764  1.6663195 -1.9171994
row18 -1.53153155 -1.5315316  0.07790636 -1.5315316  1.8357643 -1.5315316
row19  0.09507465  2.3568377 -1.51436326  1.6636906 -0.8212161 -1.5143633
row20 -1.44185381  1.9593436 -1.44185381  1.9921334  2.1416651 -1.4418538
           col49      col50
row1  -1.8404004  1.9437893
row2  -1.5326463 -1.5326463
row3  -1.3430921 -1.3430921
row4  -0.8359670  2.3210334
row5  -1.6717938  0.4076477
row6   0.2884090  2.2102216
row7   1.4257680 -1.1391814
row8  -1.6052596  1.7620362
row9   1.8921690 -1.7187489
row10  1.2294385  1.0287678
row11 -1.6164437 -1.6164437
row12 -1.5646910 -1.5646910
row13  2.0391186 -1.8320824
row14  1.7353329  1.6175498
row15  1.6964430  0.3101486
row16 -1.8980901  0.9351232
row17  0.6477499  0.0287107
row18  1.7643053 -1.5315316
row19 -1.5143633  1.5766792
row20  2.1134943 -1.4418538
attr(,"parameters")
attr(,"parameters")$means
    row1     row2     row3     row4     row5     row6     row7     row8 
1.840400 1.532646 1.343092 1.529114 1.671794 1.503351 1.139181 1.605260 
    row9    row10    row11    row12    row13    row14    row15    row16 
1.718749 1.168457 1.616444 1.564691 1.832082 1.848186 1.769293 1.898090 
   row17    row18    row19    row20 
1.917199 1.531532 1.514363 1.441854 

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 
   1.34    0.34    1.65