R Under development (unstable) (2025-01-27 r87654 ucrt) -- "Unsuffered Consequences" Copyright (C) 2025 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > > ############################### 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 0.00000000 0.000000000 -0.62010649 0.000000000 0.0000000 row2 0.00000000 -1.06085903 0.191903934 0.00000000 0.260896806 0.0000000 row3 0.64055213 0.00000000 0.000000000 0.00000000 -1.033424305 0.6088034 row4 0.00000000 0.00000000 0.000000000 0.11964594 0.000000000 0.0000000 row5 -1.37732385 -0.97185875 0.008970507 0.00000000 0.000000000 0.0000000 row6 -0.03695747 0.00000000 0.546188820 0.00000000 -0.324639538 -0.2192790 row7 -0.40924061 0.00000000 0.000000000 0.25215787 0.000000000 0.0000000 row8 0.00000000 -1.12550507 0.000000000 0.00000000 0.000000000 0.6990442 row9 0.00000000 -0.04290202 1.031612721 0.00000000 0.000000000 0.0000000 row10 0.43772064 0.00000000 0.708010967 0.00000000 0.536160710 0.9205724 row11 0.14509371 0.00000000 0.816261987 -0.21781178 -1.604106142 0.0000000 row12 0.00000000 0.63548598 0.566493109 0.19611932 0.086920029 0.5423956 row13 0.00000000 0.00000000 0.000000000 0.00000000 0.525775738 0.7553502 row14 0.00000000 0.61370374 0.998115441 0.03833960 0.092406818 0.0000000 row15 0.91686722 -0.96586403 0.000000000 -0.27271685 0.825895440 0.5539617 row16 0.62668966 0.00000000 0.453417937 0.00000000 0.000000000 -0.1617677 row17 0.00000000 -0.34513202 0.976623824 0.00000000 -1.443744304 1.0411623 row18 0.00000000 0.00000000 0.000000000 0.00000000 -1.959721381 0.0000000 row19 0.00000000 0.00000000 0.000000000 -0.06133775 -1.735314183 0.4241701 row20 0.00000000 0.00000000 0.136239486 0.00000000 -0.003522457 0.0000000 col7 col8 col9 col10 col11 col12 row1 0.000000000 0.2553622 0.0000000 0.0000000 0.0000000 0.4560329 row2 0.000000000 -0.7424053 0.0000000 0.6883408 0.5232611 0.6208995 row3 0.000000000 -0.1861264 0.0000000 0.2193387 0.6713238 0.9590059 row4 0.000000000 0.4724673 0.0000000 0.0000000 -0.7405553 0.4168975 row5 0.000000000 0.0000000 -0.1245609 0.0000000 0.9499539 0.0000000 row6 0.591651194 0.0000000 0.0000000 0.0000000 -0.2192790 -0.9124262 row7 0.481732311 0.0000000 0.0000000 0.4557568 0.4557568 0.0000000 row8 -1.348648625 0.8204051 0.4005512 0.0000000 1.1568773 0.0000000 row9 0.928958567 0.9289586 0.0000000 0.0000000 0.0000000 0.8444012 row10 0.920572409 0.0000000 0.0000000 0.0000000 -1.1378157 0.1614673 row11 0.000000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 row12 0.006877321 -0.6862699 0.4923851 0.0000000 0.0000000 0.4397414 row13 0.000000000 0.4929859 0.8294582 -2.2150643 0.4929859 0.3876254 row14 0.785553999 0.0000000 -1.2425942 -0.2870828 0.4801723 0.0000000 row15 -2.218626998 0.0000000 0.0000000 0.0000000 0.0000000 0.3840627 row16 0.671141421 0.6926476 0.0000000 0.0000000 -0.1129775 0.7137010 row17 -0.750597124 0.0000000 0.0000000 0.0000000 0.1144003 0.0000000 row18 1.175772835 -0.8611091 0.2375032 0.0000000 -2.6528686 -1.5542563 row19 0.000000000 0.0000000 0.0000000 0.0000000 -0.6367019 0.9727360 row20 -0.360197400 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-1.49202991 1.5990125 -1.4920299 -1.4920299 0.1174080 row14 2.0529535 0.02480525 -1.5846327 1.9118749 -1.5846327 1.6342432 row15 2.4038864 2.24261821 -1.4462612 -0.3476490 -1.4462612 2.2426182 row16 1.5976863 -1.92867422 1.5370617 -1.9286742 1.8325259 1.3294223 row17 -1.4542300 -1.45423001 0.8483551 2.2593421 -1.4542300 1.6812642 row18 -1.3311642 0.61474594 -1.3311642 1.9646727 2.1951963 2.4065054 row19 -1.2213155 -1.22131547 0.8581261 0.7245947 2.3896024 -1.2213155 row20 0.7930754 2.04583837 1.9717304 -1.2863661 -1.2863661 -1.2863661 col25 col26 col27 col28 col29 col30 row1 -1.6882453 -1.6882453 -1.6882453 -1.6882453 -1.6882453 -1.6882453 row2 1.2939184 1.7708425 2.0411328 0.8884533 -1.5964534 2.2747476 row3 2.1515000 1.5324608 0.9728450 -1.5120616 -1.5120616 1.8201429 row4 -1.2745478 1.8164947 2.6172725 -1.2745478 2.5321147 2.2518128 row5 -0.9102627 -0.9102627 -0.9102627 -0.9102627 -0.9102627 -0.9102627 row6 2.3276287 2.2644498 -1.5641916 0.9207150 -1.5641916 -1.5641916 row7 -1.4848987 -1.4848987 -1.4848987 1.1541586 -1.4848987 2.3437427 row8 -1.2446470 -1.2446470 -1.2446470 0.3647909 -1.2446470 -1.2446470 row9 1.9320538 -1.4001507 -0.7070036 2.2634109 -1.4001507 2.3840389 row10 -1.8003672 -1.8003672 0.3968574 0.7645822 0.9722216 1.4954697 row11 -1.6522701 -1.6522701 -1.6522701 2.0112916 1.7150258 2.2395502 row12 -1.5497862 2.3214148 -1.5497862 -1.5497862 -1.5497862 -1.5497862 row13 -1.4920299 2.1455563 -1.4920299 -1.4920299 -1.4920299 -1.4920299 row14 1.9707154 -1.5846327 -1.5846327 -1.5846327 -1.5846327 1.5508615 row15 1.3263275 2.4249398 1.1186881 -1.4462612 1.3263275 -1.4462612 row16 1.1623682 1.8555154 -1.9286742 -1.9286742 -1.9286742 -1.9286742 row17 -1.4542300 -1.4542300 0.9436653 -1.4542300 -1.4542300 2.1833561 row18 -1.3311642 0.7482773 2.0700332 -1.3311642 -1.3311642 -1.3311642 row19 -1.2213155 -1.2213155 -1.2213155 -1.2213155 -1.2213155 -1.2213155 row20 2.5422753 1.8491281 2.2971528 2.3512200 -1.2863661 -1.2863661 col31 col32 col33 col34 col35 col36 row1 1.4027971 2.161902 1.4898085 2.0959443 -1.6882453 2.20357495 row2 0.4829882 -1.596453 -1.5964534 -1.5964534 -1.5964534 1.53904084 row3 -1.5120616 1.195989 -1.5120616 2.3380860 1.6659922 1.05288771 row4 1.7211845 1.860946 -1.2745478 -1.2745478 -1.2745478 -1.27454776 row5 2.0341763 2.225232 -0.9102627 1.7287947 -0.9102627 -0.91026268 row6 -1.5641916 -1.564192 -1.5641916 -1.5641916 -1.5641916 1.48033083 row7 -1.4848987 -1.484899 1.0800506 2.0414618 -1.4848987 2.27630139 row8 -1.2446470 -1.244647 1.8908472 -1.2446470 2.4689250 -1.24464703 row9 2.1262098 -1.400151 -1.4001507 0.7970738 -1.4001507 -0.01385639 row10 1.8631945 -1.800367 -1.8003672 2.0282742 1.1953651 -0.70175486 row11 1.2381017 1.292169 1.1203187 1.2381017 -1.6522701 -1.65227007 row12 1.5857080 2.033733 1.2834271 -1.5497862 2.3003614 -0.16349183 row13 0.9058654 -1.492030 -1.4920299 -1.4920299 -1.4920299 -1.49202991 row14 1.9118749 -1.584633 -1.5846327 -1.5846327 -1.5846327 1.67346387 row15 -1.4462612 -1.446261 -1.4462612 1.1927961 -1.4462612 2.46576176 row16 0.7103831 1.567833 1.0157648 -1.9286742 1.4035303 1.36716265 row17 2.2834396 2.329960 -1.4542300 2.4577930 -1.4542300 -1.45423001 row18 -1.3311642 -1.331164 -1.3311642 -1.3311642 0.6147459 -1.33116421 row19 1.0812696 -1.221315 2.4922566 -1.2213155 2.3622035 -1.22131547 row20 -1.2863661 -1.286366 -1.2863661 1.8491281 1.1115291 -1.28636614 col37 col38 col39 col40 col41 col42 row1 -1.6882453 -1.6882453 1.7129520 -0.5896331 1.30748693 -1.6882453 row2 -1.5964534 -1.5964534 0.4829882 1.3992789 -1.59645338 -1.5964534 row3 -1.5120616 -0.1257673 1.9536743 1.7068142 2.37975865 0.4338485 row4 -1.2745478 -1.2745478 2.3630384 -1.2745478 1.81649470 -1.2745478 row5 -0.9102627 -0.9102627 1.7287947 -0.9102627 1.79778752 -0.9102627 row6 1.6546842 -1.5641916 1.2690217 2.2199980 1.38024738 1.0748657 row7 -1.4848987 2.1526874 -1.4848987 -1.4848987 -1.48489873 0.8176864 row8 -1.2446470 -1.2446470 2.0875575 1.2402596 1.89084718 -1.2446470 row9 -1.4001507 -0.3015385 -1.4001507 -1.4001507 -0.01385639 1.8187251 row10 1.6653688 -1.8003672 1.6008302 -1.8003672 1.56692868 -1.8003672 row11 2.1319196 -1.6522701 -1.6522701 1.7489273 0.74562521 -1.6522701 row12 -1.5497862 -1.5497862 -1.5497862 1.7824183 1.85141119 1.3405856 row13 -1.4920299 2.0914890 1.1470274 1.7268459 1.94195729 1.8038070 row14 -1.5846327 -1.5846327 0.2071268 2.0789290 -1.58463267 -1.5846327 row15 -1.4462612 -1.4462612 -1.4462612 -1.4462612 -1.44626124 2.4249398 row16 -1.9286742 1.0670581 -1.9286742 1.6266738 0.71038311 -1.9286742 row17 1.6368124 1.5415023 -1.4542300 -1.4542300 -1.45423001 1.1848273 row18 1.7598782 2.1345717 2.5189834 2.5400368 0.97142088 -1.3311642 row19 0.9759091 1.5512733 -1.2213155 -1.2213155 -1.22131547 2.4922566 row20 2.6256569 1.7581563 -1.2863661 -1.2863661 0.09992823 1.4862226 col43 col44 col45 col46 col47 col48 row1 1.5698512 1.9226726 -1.6882453 -1.6882453 1.44724887 1.8952736 row2 2.2953669 1.3992789 -1.5964534 -1.5964534 -1.59645338 1.9588947 row3 -1.5120616 -1.5120616 0.2796978 1.5324608 0.79052345 -1.5120616 row4 1.3645096 1.2904016 -1.2745478 -1.2745478 2.25181277 -1.2745478 row5 -0.9102627 2.9609383 -0.9102627 -0.9102627 -0.91026268 -0.9102627 row6 -1.5641916 -1.5641916 1.8697956 -1.5641916 1.69390493 2.0993700 row7 0.9129965 -1.4848987 -1.4848987 -1.4848987 1.94908848 -1.4848987 row8 1.3203023 -1.2446470 -0.1460347 2.6673760 0.54711244 -1.2446470 row9 -1.4001507 2.2374354 -1.4001507 -1.4001507 0.90243435 1.5442882 row10 -1.8003672 -1.8003672 1.0328462 -1.8003672 1.33512706 -1.8003672 row11 -1.6522701 2.0366094 -1.6522701 2.2597529 -1.65227007 0.5449545 row12 -1.5497862 -1.5497862 -1.5497862 2.1390933 -1.54978620 -1.5497862 row13 -1.4920299 -1.4920299 1.5524925 -1.4920299 -1.49202991 2.0044777 row14 0.2071268 2.2865683 2.1995570 1.3057391 -1.58463267 1.1879561 row15 -0.7531141 1.8859433 -1.4462612 1.4981777 -1.44626124 -1.4462612 row16 1.2068200 -1.9286742 1.5976863 -1.9286742 2.00315142 -0.1369147 row17 2.3299596 0.1552079 -1.4542300 1.5902924 -1.45423001 -1.4542300 row18 -1.3311642 2.1653434 -1.3311642 -1.3311642 0.05513015 1.3768860 row19 -1.2213155 -1.2213155 1.9141787 2.3340326 -1.22131547 1.9567384 row20 -1.2863661 0.6595440 -1.2863661 2.4978235 -1.28636614 -1.2863661 col49 col50 row1 -1.6882453 1.2561936 row2 -1.5964534 2.2102091 row3 -1.5120616 1.8201429 row4 -1.2745478 -1.2745478 row5 3.0215630 -0.9102627 row6 -1.5641916 -1.5641916 row7 -1.4848987 0.9129965 row8 -1.2446470 1.9334068 row9 2.3375189 1.0847559 row10 0.9076830 0.3968574 row11 2.1763713 2.1543924 row12 1.7824183 -1.5497862 row13 1.4524091 1.9737060 row14 -0.4860204 -1.5846327 row15 -1.4462612 -1.4462612 row16 -0.3192363 1.8999672 row17 0.3375295 -1.4542300 row18 -1.3311642 1.9646727 row19 1.8697270 1.4867347 row20 -1.2863661 1.9717304 attr(,"parameters") attr(,"parameters")$means row1 row2 row3 row4 row5 row6 row7 row8 1.6882453 1.5964534 1.5120616 1.2745478 0.9102627 1.5641916 1.4848987 1.2446470 row9 row10 row11 row12 row13 row14 row15 row16 1.4001507 1.8003672 1.6522701 1.5497862 1.4920299 1.5846327 1.4462612 1.9286742 row17 row18 row19 row20 1.4542300 1.3311642 1.2213155 1.2863661 attr(,"parameters")$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 0.79 0.17 0.96