R Under development (unstable) (2024-05-20 r86569 ucrt) -- "Unsuffered Consequences"
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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   0.00000000 -0.55071474  0.00000000 -0.99254749  0.1775238  0.00000000
row2   0.37956166  0.14115063 -0.22657415  0.00000000  0.1788910  0.00000000
row3   0.00000000 -1.08167208  0.00000000 -1.59249770  0.0000000  0.01694021
row4  -0.06992714  0.00000000  0.00000000 -0.76307432  0.6456929  0.00000000
row5   0.64488768  0.83725957  0.73189905  0.00000000  0.8860497  0.58236732
row6  -2.59278195  0.00000000  0.00000000  0.93357857  0.8729540  0.00000000
row7   0.84767275 -0.04841528 -0.64625228  0.64473190  0.0000000  0.00000000
row8   0.75467743  0.00000000  0.00000000  0.00000000 -0.6316169  0.00000000
row9   0.00000000  0.00000000  0.00000000 -0.25182064  0.0000000 -0.46312974
row10 -0.60818844  0.08495875  0.00000000  0.05510578  0.1961844  0.47062123
row11  0.00000000  1.06159490  0.00000000  0.00000000  0.0000000  0.00000000
row12 -0.25218421  0.65068350  0.08428802  0.00000000  0.8899132  0.00000000
row13  0.39113705  0.22408296  0.00000000 -0.92104934  0.4652450  0.68838857
row14  0.00000000 -1.19915765  0.00000000 -0.68833202  0.0000000  0.81574537
row15  0.96728565  0.58591809  0.00000000  0.91727523  0.0000000  1.01491370
row16 -1.81036942  0.89768078 -1.52268735 -0.20093151  0.9196597 -0.82954017
row17  0.00000000  1.34019300  0.00000000  0.00000000  0.0000000 -0.60571715
row18  0.00000000  0.14293440  0.00000000  0.00000000  0.0000000  0.02515136
row19  0.00000000  0.29131519 -0.11414992  0.87446147  0.0000000  0.00000000
row20  0.00000000 -0.17042462  0.00000000  1.01915945 -0.8635718  0.00000000
            col7       col8       col9       col10       col11       col12
row1   0.0000000  0.1424324  0.2759638  0.21142531  0.06832447  0.00000000
row2   0.0000000  0.0000000  0.3795617  0.49397201  0.00000000  0.00000000
row3   0.3999325  0.0000000  0.7100874  1.18009102  0.00000000 -1.30481563
row4   0.0000000  0.0000000  0.5767000  0.00000000  0.00000000 -0.52191226
row5   0.1929026  0.0000000 -1.4653255 -0.07903116  0.75929803  0.00000000
row6   0.0000000  0.0000000  0.0000000  0.29758981  0.00000000  0.00000000
row7   0.0000000  0.0000000  0.0000000 -0.27155883  0.00000000 -1.65785319
row8   0.0000000  0.0000000  0.3238945  0.46699536  0.53153388  0.00000000
row9   0.0000000  0.1376441  0.1048543  0.03586143  0.00000000  0.03586143
row10  0.0000000  0.0000000  0.1695161  0.40874582  0.00000000 -0.73335158
row11  0.0000000  1.1692256  0.0000000  0.32192770  0.00000000  1.03860538
row12  0.0000000 -1.8616221  0.0000000  0.00000000 -0.18764569 -0.18764569
row13  0.0000000 -0.5643744  0.0000000  0.74395842  0.98318811  0.00000000
row14  0.0000000  0.0000000  0.0000000  0.00000000 -1.04500697  0.00000000
row15  0.0000000 -0.1813371  0.0000000  0.43176741  0.00000000  0.54955045
row16  0.0000000  0.0000000  0.0000000  0.00000000 -0.13639299  0.96221930
row17  0.0000000  0.0000000 -0.9421894  0.00000000 -0.24904221  0.00000000
row18 -0.2084635  0.0000000  0.7591205  0.48468369  0.00000000 -0.44485227
row19  0.0000000 -0.4018320  1.0050817  0.00000000  0.05770034 -2.88673864
row20  0.0000000  0.0000000  0.0000000  0.36857188  0.00000000  0.40939388
            col13      col14       col15       col16       col17       col18
row1  -1.39801260  0.0000000  0.63886933  0.00000000  0.00000000  0.00000000
row2  -0.02590345  0.0000000  0.21525861  0.00000000  0.00000000 -0.02590345
row3   0.00000000  0.0000000  0.91980792  0.00000000  1.11555250  1.04655963
row4   0.00000000 -1.3692101  0.00000000  0.00000000  0.66767181  0.68917801
row5   0.00000000  0.8372596  0.00000000  0.00000000 -0.65439531 -0.14356968
row6   0.96256611  0.0000000  0.00000000  0.17980677  0.00000000 -1.49416966
row7   0.00000000  0.0000000 -0.40509022 -0.09970857  0.00000000  0.00000000
row8   0.00000000 -0.5363067  0.72935963  0.00000000 -0.98829187  0.00000000
row9   0.55380452  0.0000000  0.23001744  0.00000000  0.00000000 -0.07746726
row10  0.00000000 -0.1833052  0.00000000 -0.95649513 -0.14556491  0.00000000
row11 -1.62398245 -2.7225947 -2.02944756  0.00000000  0.49628109  0.86092420
row12 -0.25218421  0.2178194  0.13080804  0.95178859  0.77743520  0.00000000
row13  0.31109434  0.0000000  0.87071013  0.00000000 -1.48066513 -1.07520002
row14  0.00000000 -0.4259678 -0.21832839  0.00000000  0.53544341  0.00000000
row15  0.00000000 -0.4437013  0.00000000 -2.05313924  0.00000000  0.00000000
row16  0.00000000  0.0000000 -1.81036942  0.00000000 -0.26992438  0.26907212
row17  0.00000000  0.0000000  1.05929061  1.11193434 -0.06672065 -1.45301501
row18  0.00000000  0.2143934  0.65376002  0.40244559  0.65376002  0.00000000
row19  0.00000000  0.0000000  0.05770034  0.00000000  0.00000000  0.48055719
row20 -0.24453259 -2.8094819  0.00000000  0.44861459  0.00000000  0.00000000
            col19      col20       col21       col22      col23      col24
row1   0.00000000  0.0000000  0.00000000  0.59441757  0.4211458  0.0000000
row2   0.77487439  0.4939720 -0.55199655  0.00000000 -1.0375044  0.0000000
row3   0.01694021  0.0000000 -0.89935052 -0.05205266  0.8053976  0.0000000
row4   0.52790986  0.0000000 -0.27059783  0.00000000 -0.2705978 -1.3692101
row5   0.00000000 -1.4653255  0.00000000  0.73189905  0.8372596  0.0000000
row6   0.00000000  0.0000000 -0.98334404  0.00000000  0.0000000 -1.4941697
row7   0.00000000 -0.2715588  0.66942452  0.00000000  0.0000000  0.3570498
row8   0.00000000  0.2438518  0.00000000  0.00000000  0.7793700  0.0000000
row9   0.39253637  0.4648570  0.57485793 -0.81143643  0.0000000  0.1376441
row10  0.00000000  0.0000000 -0.07410595 -0.73335158  0.3872396  0.0000000
row11  0.00000000  1.1692256 -0.64315320 -0.42000964  0.0000000  0.0000000
row12 -1.16847495  0.0000000  0.00000000  0.00000000  0.0000000 -0.5623391
row13 -1.76834720  0.8942406  0.00000000  0.00000000  0.0000000  0.7165594
row14  0.00000000  0.0000000  0.41028027 -0.42596776  0.0000000 -2.2977699
row15  0.02630230  0.0000000  0.00000000  1.06037607  0.0000000  0.0000000
row16  0.00000000  0.0000000  0.82868791  0.55675419  0.0000000  0.0000000
row17  0.00000000 -1.1653329  0.78057721  0.00000000  0.4441050  0.0000000
row18  0.69724513  0.0000000 -0.85031738  0.00000000  0.0000000  0.0000000
row19  0.00000000  0.0000000 -1.09497917  0.00000000  0.0000000  0.5789973
row20  0.00000000  0.0000000  0.00000000  0.02373140  0.0000000  0.0000000
           col25      col26       col27      col28      col29      col30
row1   0.3365885  0.2442151  0.42114585 -0.7918768  0.0000000  0.0000000
row2   0.4094146 -0.5519965  0.00000000  0.4094146  0.7748744  0.0000000
row3  -1.0816721  0.0000000 -0.05205266  0.0000000  0.0000000  0.0000000
row4   0.1348673  0.0000000  0.00000000  0.2730176  0.3653909  0.0000000
row5   0.0000000  0.0000000  0.00000000  0.0000000  0.0000000  0.0000000
row6   0.0000000  0.0000000  0.00000000  0.0000000  0.3516570  0.0000000
row7   0.4215884  0.5667704 -0.40509022  0.0000000  0.0000000 -0.6462523
row8   0.9369990  0.0000000  0.43309381  0.9778210  0.0000000  0.1103204
row9   0.0000000  0.0000000 -0.30061081  0.0000000  0.0000000  0.0000000
row10  0.3872396  0.0000000  0.00000000  0.0000000  0.0000000  0.0000000
row11  0.0000000  0.0000000  0.00000000  0.0000000  0.0000000  0.0000000
row12  0.0000000  0.5661261  0.00000000  0.0000000  0.0000000 -2.9602344
row13  0.0000000  0.5003363  0.00000000  0.0000000  0.0000000  0.0000000
row14  0.0000000  0.2279587  0.53544341  0.5644309  0.4102803  0.6979623
row15 -2.0531392 -0.5490618  0.00000000  0.0000000  0.9172752  0.0000000
row16  0.0000000  0.0000000 -0.13639299 -1.1172222  0.9828386  0.8752079
row17  1.0592906  0.0000000  0.00000000  0.0000000  1.0318916  0.0000000
row18  0.0000000  0.6312872  0.00000000 -2.0542902 -0.1571702 -0.3803137
row19  0.1089936  0.0000000  0.51445874  0.3713579  0.0000000  0.8021408
row20  1.0406657  0.4863549 -0.03689323 -0.1704246  0.7458661  0.0000000
            col31       col32      col33      col34       col35      col36
row1   0.27596384 -0.05427785  0.0000000  0.0000000  0.00000000  0.0000000
row2   0.00000000  0.64425421  0.0000000 -1.0375044  0.00000000  0.0000000
row3   1.13753140  0.00000000 -1.3048156  1.1375314  0.00000000  1.0930796
row4   0.05790623 -2.46782241  0.6232200  0.0000000  0.00000000  0.7308507
row5  -1.75300760  0.00000000  0.0000000  0.8119418  0.03875187  0.0000000
row6   0.00000000  0.96256611  0.0000000  0.0000000  0.00000000  0.0000000
row7   0.00000000  0.00000000  0.1339063  0.3570498  0.00000000  0.0000000
row8  -0.16161330  0.00000000  0.0000000  0.2438518 -0.36925266  0.0000000
row9   0.00000000  0.53229831  0.4172290 -1.3504329  0.00000000  0.3672186
row10 -0.22252595  0.00000000  0.0000000  0.0000000  0.00000000  0.0000000
row11  0.00000000 -2.72259474  0.0000000  1.1486063  0.00000000 -0.3246995
row12  0.37197009  0.00000000  0.0000000  0.5951136 -0.18764569  0.4737528
row13  0.00000000  0.00000000 -1.7683472  0.0000000  0.89424062  0.8707101
row14  0.00000000  0.41028027 -0.9114756  0.3763787  0.00000000  0.0000000
row15  0.00000000  0.00000000  0.8912997 -2.7462864  0.89129974  0.3892078
row16  0.00000000  0.00000000 -1.5226874  0.7286044  0.00000000  0.0000000
row17  0.00000000  0.01332206  1.2325623  0.9141086 -1.45301501 -2.5516273
row18 -0.06186002 -0.15717020  0.0000000  0.3128334  0.00000000 -0.1571702
row19  0.00000000  0.80214081  0.1089936  0.9844624  0.00000000  0.0000000
row20  0.00000000 -1.71086966  0.0000000  0.0000000  0.00000000  0.0000000
          col37       col38       col39       col40       col41       col42
row1  0.0000000  0.00000000  0.00000000 -0.01171824  0.68142894  0.21142531
row2  0.0000000  0.64425421  0.00000000  0.00000000  0.00000000 -3.11694590
row3  0.9198079  0.08147873  0.00000000  0.67618584 -2.69110999  0.00000000
row4  0.2402278  0.60023052  0.39437847  0.00000000 -1.36921012  0.33553797
row5  0.5156759  0.00000000 -1.05986042  0.00000000  0.00000000 -2.85161989
row6  1.1207901  1.16841816  0.00000000  0.00000000  0.00000000  0.70305491
row7  0.0000000 -0.84692297 -0.33609735  0.00000000 -0.09970857  0.09134667
row8  0.4997852  0.00000000 -2.24105484 -2.93420202  0.43309381  0.59215850
row9  0.0000000 -0.52375436  0.36721856  0.07095275 -1.68690517  0.46485703
row10 0.0000000  0.00000000  0.00000000  0.42979923  0.00000000  0.00000000
row11 0.0000000  0.00000000  0.00000000  0.00000000  1.12755286  0.04999398
row12 0.6232845  0.00000000  0.00000000  0.13080804  0.00000000  0.00000000
row13 0.0000000  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
row14 0.9211059 -0.68833202  0.00000000  0.00000000  0.00000000  0.00000000
row15 0.0000000 -0.66684488  0.00000000  0.47258941 -0.03823622  0.00000000
row16 0.0000000  0.00000000 -0.07576837  0.22651250  0.00000000  0.89768078
row17 0.0000000  0.00000000  0.00000000  0.00000000 -0.15373203 -2.55162730
row18 0.2810847  0.21439336  0.00000000  0.56066960  0.58476715  0.00000000
row19 0.0000000  0.85093098  0.00000000  0.00000000 -2.19359146  0.00000000
row20 0.0000000 -0.24453259  0.90409012  0.00000000  0.00000000  0.00000000
            col43      col44      col45      col46       col47       col48
row1   0.00000000  0.0000000  0.6814289  0.5478976 -0.88718697  0.00000000
row2  -0.71905063  0.0000000  0.0000000  0.0000000  0.00000000  0.62072371
row3   0.74287721  0.0000000  0.1421034  0.3046223  0.00000000 -1.99796281
row4   0.00000000  0.0000000  0.0000000  0.7102314  0.57670003  0.01708424
row5   0.00000000  0.7859663 -0.4537246  0.5495775  0.00000000 -0.36671324
row6   0.00000000 -0.9833440  0.0000000  0.5852719 -0.19488668  0.00000000
row7   0.48221298  0.0000000  0.7400421  0.8270535  0.00000000 -1.09823740
row8   0.77937005  0.7293596  0.1103204 -1.1424426  0.00000000 -1.32476411
row9  -0.58829288  0.3412431  0.5103194  0.0000000  0.00000000  0.00000000
row10  0.02433412  0.0000000  0.1139463  0.0000000  0.22215987  0.00000000
row11  0.00000000  1.1275529  0.0000000  0.9149914  0.00000000  0.00000000
row12  0.00000000  0.0000000  0.6506835  0.0000000  0.50550149  0.00000000
row13 -2.86695949  0.0000000  0.5342379  0.4288774  0.65940103  0.00000000
row14  0.00000000  0.0000000  0.6466690  0.7226550  0.30491975  0.05360532
row15  0.00000000 -0.2613798 -1.1368485  0.0000000  0.00000000  1.01491370
row16  0.00000000  0.0000000  0.2690721  0.0000000  0.03545727  0.98283859
row17  0.22096142  0.6264265 -1.8584801  1.0592906  0.28158604  1.05929061
row18  0.00000000  0.0000000  0.0000000  0.0000000  0.00000000  0.31283343
row19  0.66860942 -0.1141499  0.0000000  0.1089936  0.00000000  1.02528436
row20  0.00000000  0.0000000  0.0000000  0.0000000  0.97470769  0.00000000
              col49       col50
row1  -1.2438619180  0.00000000
row2   0.7332016978  0.00000000
row3   0.0000000000 -0.38852490
row4   0.0000000000  0.30476631
row5   0.5495774957  0.00000000
row6  -0.3955573749  0.00000000
row7   0.0003748881  0.69352207
row8   0.5921585040  0.06153025
row9   0.0000000000  0.31457483
row10  0.3197983363  0.38723962
row11  0.0000000000  0.00000000
row12  0.0000000000  0.00000000
row13  0.2685347250  0.00000000
row14  0.2671794224  0.00000000
row15  0.0000000000  0.00000000
row16  0.0000000000  0.00000000
row17  0.8156685287  0.00000000
row18 -2.4597552899  0.00000000
row19  0.0000000000 -2.88673864
row20  0.0000000000 -0.17042462
attr(,"parameters")
attr(,"parameters")$means
    row1     row2     row3     row4     row5     row6     row7     row8 
3.189772 3.116946 2.691110 3.160970 2.851620 2.592782 3.044148 2.934202 
    row9    row10    row11    row12    row13    row14    row15    row16 
3.296343 3.441402 2.722595 2.960234 2.866959 2.990917 2.746286 2.908982 
   row17    row18    row19    row20 
2.551627 3.152902 2.886739 2.809482 

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

attr(,"decostand")
[1] "rclr"
> vegan::decostand(testdata, method = "clr", pseudocount=1)
            col1       col2       col3       col4       col5       col6
row1  -1.5547789  1.1532713 -1.5547789  0.7478062  1.8464185 -1.5547789
row2   1.9958646  1.7653409  1.4139430 -1.5304960  1.8017085 -1.5304960
row3  -1.6256041  0.1661553 -1.6256041 -0.2393098 -1.6256041  1.1469846
row4   1.3313949 -1.8040993 -1.8040993  0.6808074  2.0245421 -1.8040993
row5   1.9941005  2.1813121  2.0786579 -1.5322600  2.2289401  1.9334759
row6  -0.2848921 -0.9780393 -0.9780393  2.5773088  2.5184683 -0.9780393
row7   2.2376087  1.3701081  0.8104923  2.0391577 -1.6744143 -1.6744143
row8   1.9610965 -1.7524755 -1.7524755 -1.7524755  0.6454197 -1.7524755
row9  -1.8704584 -1.8704584 -1.8704584  1.2205841 -1.8704584  1.0199134
row10  1.3609902  2.0259665 -1.5293815  1.9969790  2.1341801  2.4024441
row11 -1.0891436  2.7175189 -1.0891436 -1.0891436 -1.0891436 -1.0891436
row12  1.3114946  2.1764920  1.6299483 -1.4610941  2.4101069 -1.4610941
row13  1.8716225  1.7112799 -1.4242144  0.6552272  1.9430815  2.1593046
row14 -1.5295626  0.4163475 -1.5295626  0.8683326 -1.5295626  2.2990788
row15  2.3659947  1.9956210 -1.3716749  2.3172046 -1.3716749  2.4125148
row16 -0.1084017  2.3339453  0.1147419  1.2778927  2.3554515  0.7025285
row17 -1.4529151  2.4591079 -1.4529151 -1.4529151 -1.4529151  0.6265265
row18 -1.6715709  1.6606336 -1.6715709 -1.6715709 -1.6715709  1.5473049
row19 -1.4985070  1.7203688  1.3347063  2.2856826 -1.4985070 -1.4985070
row20 -1.1047789  1.6032713 -1.1047789  2.7453687  0.9746626 -1.1047789
            col7        col8        col9     col10      col11       col12
row1  -1.5547789  1.81251692  1.94172866  1.879208  1.7410580 -1.55477891
row2  -1.5304960 -1.53049596  1.99586456  2.107090 -1.5304960 -1.53049596
row3   1.5098901 -1.62560413  1.80838308  2.266216 -1.6256041 -0.01616622
row4  -1.8040993 -1.80409929  1.95710082 -1.804099 -1.8040993  0.90395091
row5   1.5587825 -1.53225999  0.07717792  1.300953  2.1053262 -1.53225999
row6  -0.9780393 -0.97803929 -0.97803929  1.966400 -0.9780393 -0.97803929
row7  -1.6744143 -1.67441433 -1.67441433  1.158799 -1.6744143 -0.06497642
row8  -1.7524755 -1.75247554  1.54336133  1.681512  1.7440320 -1.75247554
row9  -1.8704584  1.59527751  1.56352881  1.496837 -1.8704584  1.49683744
row10 -1.5293815 -1.52938151  2.10820465  2.341819 -1.5293815  1.24320721
row11 -1.0891436  2.82287941 -1.08914360  2.001899 -1.0891436  2.69504604
row12 -1.4610941 -0.07479976 -1.46109412 -1.461094  1.3721192  1.37211923
row13 -1.4242144  0.97368091 -1.42421436  2.213372  2.4469867 -1.42421436
row14 -1.5295626 -1.52956264 -1.52956264 -1.529563  0.5498789 -1.52956264
row15 -1.3716749  1.26738245 -1.37167488  1.847201 -1.3716749  1.96052963
row16 -1.4946961 -1.49469606 -1.49469606 -1.494696  1.3385173  2.39712424
row17 -1.4529151 -1.45291509  0.33884438 -1.452915  0.9449802 -1.45291509
row18  1.3241614 -1.67157092  2.26025471  1.991991 -1.6715709  1.10101780
row19 -1.4985070  1.06644233  2.41351598 -1.498507  1.4972252 -0.80535985
row20 -1.1047789 -1.10477894 -1.10477894  2.114097 -1.1047789  2.15331760
           col13      col14        col15      col16      col17       col18
row1   0.3911312 -1.5547789  2.295368697 -1.5547789 -1.5547789 -1.55477891
row2   1.6049983 -1.5304960  1.836799867 -1.5304960 -1.5304960  1.60499825
row3  -1.6256041 -1.6256041  2.011982031 -1.6256041  2.2030373  2.13559599
row4  -1.8040993  0.1418109 -1.804099291 -1.8040993  2.0460483  2.06710172
row5  -1.5322600  2.1813121 -1.532259995 -1.5322600  0.7703251  1.24032873
row6   2.6054797 -0.9780393 -0.978039285  1.8551741 -0.9780393  0.40825508
row7  -1.6744143 -1.6744143  1.033635870  1.3213179 -1.6744143 -1.67441433
row8  -1.7524755  0.7324311  1.936403918 -1.7524755  0.3269660 -1.75247554
row9   2.0007426 -1.8704584  1.684889667 -1.8704584 -1.8704584  1.38763814
row10 -1.5293815  1.7664554 -1.529381512  1.0355678  1.8028230 -1.52938151
row11  0.2971508 -0.3959964  0.009468691 -1.0891436  2.1689529  2.52177432
row12  1.3114946  1.7577817  1.674400100  2.4707315  2.3001060 -1.46109412
row13  1.7946615 -1.4242144  2.336985755 -1.4242144  0.1852236  0.52169579
row14 -1.5295626  1.1094947  1.303650706 -1.5295626  2.0257854 -1.52956264
row15 -1.3716749  1.0262204 -1.371674879 -0.2730626 -1.3716749 -1.37167488
row16 -1.4946961 -1.4946961 -0.108401697 -1.4946961  1.2133541  1.72417977
row17 -1.4529151 -1.4529151  2.184671069  2.2359644  1.1120343 -0.06662073
row18 -1.6715709  1.7296265  2.157070473  1.9119480  2.1570705 -1.67157092
row19 -1.4985070 -1.4985070  1.497225248 -1.4985070 -1.4985070  1.90269036
row20  1.5342784 -0.4116318 -1.104778937  2.1910579 -1.1047789 -1.10477894
           col19       col20      col21      col22      col23      col24
row1  -1.5547789 -1.55477891 -1.5547789  2.2518836  2.0828073 -1.5547789
row2   2.3815270  2.10709020  1.1085614 -1.5304960  0.6667286 -1.5304960
row3   1.1469846 -1.62560413  0.3203060  1.0824461  1.9007564 -1.6256041
row4   1.9094728 -1.80409929  1.1403397 -1.8040993  1.1403397  0.1418109
row5  -1.5322600  0.07717792 -1.5322600  2.0786579  2.1813121 -1.5322600
row6  -0.9780393 -0.97803929  0.8137202 -0.9780393 -0.9780393  0.4082551
row7  -1.6744143  1.15879901  2.0632553 -1.6744143 -1.6744143  1.7595729
row8  -1.7524755  1.46640029 -1.7524755 -1.7524755  1.9851941 -1.7524755
row9   1.8431137  1.91373124  2.0213619  0.6944910 -1.8704584  1.5952775
row10 -1.5293815 -1.52938151  1.8718159  1.2432072  2.3207661 -1.5293815
row11 -1.0891436  2.82287941  1.1080810  1.3087517 -1.0891436 -1.0891436
row12  0.4848160 -1.46109412 -1.4610941 -1.4610941 -1.4610941  1.0238125
row13 -0.0379200  2.35997527 -1.4242144 -1.4242144 -1.4242144  2.1867036
row14 -1.5295626 -1.52956264  1.9044246  1.1094947 -1.5295626 -0.4309503
row15  1.4615385 -1.37167488 -1.3716749  2.4569665 -1.3716749 -1.3716749
row16 -1.4946961 -1.49469606  2.2665041  2.0018115 -1.4946961 -1.4946961
row17 -1.4529151  0.15652282  1.9143807 -1.4529151  1.5916073 -1.4529151
row18  2.1996301 -1.67157092  0.7263243 -1.6715709 -1.6715709 -1.6715709
row19 -1.4985070 -1.49850703  0.4474031 -1.4985070 -1.4985070  1.9980005
row20 -1.1047789 -1.10477894 -1.1047789  1.7855928 -1.1047789 -1.1047789
           col25      col26      col27      col28     col29      col30
row1   2.0005692  1.9109570  2.0828073  0.9301277 -1.554779 -1.5547789
row2   2.0248521  1.1085614 -1.5304960  2.0248521  2.381527 -1.5304960
row3   0.1661553 -1.6256041  1.0824461 -1.6256041 -1.625604 -1.6256041
row4   1.5281052 -1.8040993 -1.8040993  1.6616366  1.751249 -1.8040993
row5  -1.5322600 -1.5322600 -1.5322600 -1.5322600 -1.532260 -1.5322600
row6  -0.9780393 -0.9780393 -0.9780393 -0.9780393  2.017693 -0.9780393
row7   1.8220932  1.9631718  1.0336359 -1.6744143 -1.674414  0.8104923
row8   2.1393448 -1.7524755  1.6487218  2.1793501 -1.752476  1.3385669
row9  -1.8704584 -1.8704584  1.1740640 -1.8704584 -1.870458 -1.8704584
row10  2.3207661 -1.5293815 -1.5293815 -1.5293815 -1.529382 -1.5293815
row11 -1.0891436 -1.0891436 -1.0891436 -1.0891436 -1.089144 -1.0891436
row12 -1.4610941  2.0942539 -1.4610941 -1.4610941 -1.461094 -0.7679469
row13 -1.4242144  1.9769830 -1.4242144 -1.4242144 -1.424214 -1.4242144
row14 -1.5295626  1.7285339  2.0257854  2.0539563  1.904425  2.1840094
row15 -0.2730626  0.9309102 -1.3716749 -1.3716749  2.317205 -1.3716749
row16 -1.4946961 -1.4946961  1.3385173  0.4512141  2.417327  2.3119664
row17  2.1846711 -1.4529151 -1.4529151 -1.4529151  2.158003 -1.4529151
row18 -1.6715709  2.1350916 -1.6715709 -0.2852766  1.372952  1.1616424
row19  1.5460154 -1.4985070  1.9354802  1.7973298 -1.498507  2.2150650
row20  2.7664221  2.2274256  1.7284344  1.6032713  2.478740 -1.1047789
           col31      col32       col33      col34       col35      col36
row1   1.9417287  1.6232749 -1.55477891 -1.5547789 -1.55477891 -1.5547789
row2  -1.5304960  2.2536937 -1.53049596  0.6667286 -1.53049596 -1.5304960
row3   2.2245435 -1.6256041 -0.01616622  2.2245435 -1.62560413  2.1810584
row4   1.4539972 -0.7054870  2.00256320 -1.8040993 -1.80409929  2.1079237
row5  -0.1459656 -1.5322600 -1.53225999  2.1566195  1.41217898 -1.5322600
row6  -0.9780393  2.6054797 -0.97803929 -0.9780393 -0.97803929 -0.9780393
row7  -1.6744143 -1.6744143  1.54446149  1.7595729 -1.67441433 -1.6744143
row8   1.0807378 -1.7524755 -1.75247554  1.4664003  0.88658179 -1.7524755
row9  -1.8704584  1.9796892  1.86721122  0.2089831 -1.87045839  1.8184211
row10  1.7287150 -1.5293815 -1.52938151 -1.5293815 -1.52938151 -1.5293815
row11 -1.0891436 -0.3959964 -1.08914360  2.8026767 -1.08914360  1.3957631
row12  1.9062017 -1.4610941 -1.46109412  2.1224248  1.37211923  2.0046418
row13 -1.4242144 -1.4242144 -0.03792000 -1.4242144  2.35997527  2.3369858
row14 -1.5295626  1.9044246  0.66766194  1.8716347 -1.52956264 -1.5295626
row15 -1.3716749 -1.3716749  2.29188677 -0.6785277  2.29188677  1.8063790
row16 -1.4946961 -1.4946961  0.11474185  2.1688656 -1.49469606 -1.4946961
row17 -1.4529151  1.1861422  2.35374740  2.0435925 -0.06662073 -0.7597679
row18  1.4639233  1.3729515 -1.67157092  1.8249366 -1.67157092  1.3729515
row19 -1.4985070  2.2150650  1.54601541  2.3933133 -1.49850703 -1.4985070
row20 -1.1047789  0.2815154 -1.10477894 -1.1047789 -1.10477894 -1.1047789
          col37      col38      col39      col40       col41      col42
row1  -1.554779 -1.5547789 -1.5547789  1.6640969  2.33704139  1.8792083
row2  -1.530496  2.2536937 -1.5304960 -1.5304960 -1.53049596 -0.8373488
row3   2.011982  1.2076092 -1.6256041  1.7755933 -0.93245695 -1.6256041
row4   1.629888  1.9800903  1.7794196 -1.8040993  0.14181086  1.7222612
row5   1.868937 -1.5322600  0.4136502 -1.5322600 -1.53225999 -0.8391128
row6   2.759630  2.8061503 -0.9780393 -0.9780393 -0.97803929  2.3541652
row7  -1.674414  0.6281708  1.0981744 -1.6744143  1.32131794  1.5036395
row8   1.713260 -1.7524755 -0.6538632 -1.0593284  1.64872185  1.8028725
row9  -1.870458  0.9627549  1.8184211  1.5307390 -0.07869892  1.9137312
row10 -1.529382 -1.5293815 -1.5293815  2.3624388 -1.52938151 -1.5293815
row11 -1.089144 -1.0891436 -1.0891436 -1.0891436  2.78205741  1.7440697
row12  2.149824 -1.4610941 -1.4610941  1.6744001 -1.46109412 -1.4610941
row13 -1.424214 -1.4242144 -1.4242144 -1.4242144 -1.42421436 -1.4242144
row14  2.402263  0.8683326 -1.5295626 -1.5295626 -1.52956264 -1.5295626
row15 -1.371675  0.8255497 -1.3716749  1.8864217  1.40091384 -1.3716749
row16 -1.494696 -1.4946961  1.3956757  1.6833578 -1.49469606  2.3339453
row17 -1.452915 -1.4529151 -1.4529151 -1.4529151  1.03199156 -0.7597679
row18  1.794165  1.7296265 -1.6715709  2.0660987  2.08962919 -1.6715709
row19 -1.498507  2.2626931 -1.4985070 -1.4985070 -0.39989474 -1.4985070
row20 -1.104779  1.5342784  2.6328907 -1.1047789 -1.10477894 -1.1047789
           col43      col44      col45      col46      col47       col48
row1  -1.5547789 -1.5547789  2.3370414  2.2064212  0.8431164 -1.55477891
row2   0.9544107 -1.5304960 -1.5304960 -1.5304960 -1.5304960  2.23070415
row3   1.8401318 -1.6256041  1.2647676  1.4189183 -1.6256041 -0.52699184
row4  -1.8040993 -1.8040993 -1.8040993  2.0877210  1.9571008  1.41477653
row5  -1.5322600  2.1313017  0.9526467  1.9017272 -1.5322600  1.03268936
row6  -0.9780393  0.8137202 -0.9780393  2.2408365  1.5068674 -0.97803929
row7   1.8809337 -1.6744143  2.1322482  2.2174060 -1.6744143  0.40502721
row8   1.9851941  1.9364039  1.3385669  0.1934346 -1.7524755  0.03928393
row9   0.9021303  1.7931033  1.9581830 -1.8704584 -1.8704584 -1.87045839
row10  1.9671260 -1.5293815  2.0541374 -1.5293815  2.1594979 -1.52938151
row11 -1.0891436  2.7820574 -1.0891436  2.5744180 -1.0891436 -1.08914360
row12 -1.4610941 -1.4610941  2.1764920 -1.4610941  2.0354134 -1.46109412
row13 -0.7310672 -1.4242144  2.0097728  1.9079901  2.1311337 -1.42421436
row14 -1.5295626 -1.5295626  2.1339990  2.2081070  1.8026419  1.56147982
row15 -1.3716749  1.1932745  0.4200846 -1.3716749 -1.3716749  2.41251476
row16 -1.4946961 -1.4946961  1.7241798 -1.4946961  1.5010362  2.41732695
row17  1.3802983  1.7659607 -0.3543028  2.1846711  1.4374567  2.18467107
row18 -1.6715709 -1.6715709 -1.6715709 -1.6715709 -1.6715709  1.82493664
row19  2.0850119  1.3347063 -1.4985070  1.5460154 -1.4985070  2.43331861
row20 -1.1047789 -1.1047789 -1.1047789 -1.1047789  2.7018836 -1.10477894
           col49      col50
row1   0.5246626 -1.5547789
row2   2.3407050 -1.5304960
row3  -1.6256041  0.7722911
row4  -1.8040993  1.6924083
row5   1.9017272 -1.5322600
row6   1.3245458 -0.9780393
row7   1.4166281  2.0867858
row8   1.8028725  1.2920469
row9  -1.8704584  1.7671278
row10  2.2548081  2.3207661
row11 -1.0891436 -1.0891436
row12 -1.4610941 -1.4610941
row13  1.7538395 -1.4242144
row14  1.7662742 -1.5295626
row15 -1.3716749 -1.3716749
row16 -1.4946961 -1.4946961
row17  1.9482823 -1.4529151
row18 -0.5729586 -1.6715709
row19 -1.4985070 -0.8053598
row20 -1.1047789  1.6032713
attr(,"parameters")
attr(,"parameters")$means
     row1      row2      row3      row4      row5      row6      row7      row8 
1.5547789 1.5304960 1.6256041 1.8040993 1.5322600 0.9780393 1.6744143 1.7524755 
     row9     row10     row11     row12     row13     row14     row15     row16 
1.8704584 1.5293815 1.0891436 1.4610941 1.4242144 1.5295626 1.3716749 1.4946961 
    row17     row18     row19     row20 
1.4529151 1.6715709 1.4985070 1.1047789 

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 
   0.87    0.14    1.00