R Under development (unstable) (2026-03-02 r89513 ucrt) -- "Unsuffered Consequences" Copyright (C) 2026 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. > ###<--- BEGIN TESTS ---> > suppressPackageStartupMessages(require(vegan)) > > ############################### CLR #################################### > # Calculates clr-transformation. Should be equal. > > # Test data > data(varespec) > set.seed(42) > 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 <- decostand(testdata, "total") > relative.with.pseudo <- decostand(testdata + 1, "total") > > # CLR data > x.clr <- decostand(testdata + 1, method = "clr") > x.rclr <- decostand(testdata, method = "rclr") > x.clr.pseudo <- decostand(testdata, method = "clr", pseudocount = 1) > > max(abs(x.clr - x.clr.pseudo)) < 1e-6 [1] TRUE > max(abs(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 > > # Test that NAs are handled as expected in CLR > x <- testdata > set.seed(24) > x[sample(prod(dim(x)), 50)] <- NA # insert some NAs in the data > # NAs in the original data remain NAs in the returned data > all(is.na(decostand(x, "clr", na.rm = FALSE, pseudocount = 1)[is.na(x)])) == TRUE # NAs [1] TRUE > # For the other (non-NA) values, we get non-NA values back > any(is.na(decostand(x, "clr", na.rm = FALSE, pseudocount = 1)[!is.na(x)])) == FALSE [1] TRUE > any(is.na(decostand(x, "clr", MARGIN = 2, na.rm = FALSE, pseudocount = 1)[!is.na(x)])) == FALSE [1] TRUE > # Results match for the non-NA values always (with tolerance 1e-6) > inds <- !is.na(x) # Non-NA values > max(abs(decostand(x, "clr", na.rm = FALSE, pseudocount = 1)[inds] - + decostand(x, "clr", na.rm = TRUE, pseudocount = 1)[inds])) < 1e-6 [1] TRUE Warning message: In .calc_clr(x, na.rm = na.rm, ...) : replacing missing values with zero for `method = "clr"` - disable this with `na.rm = FALSE` > # For the other (non-NA) values, we get non-NA values back > any(is.na(decostand(x, "alr", na.rm = FALSE, pseudocount = 1)[!is.na(x[, -1] - x[, 1])])) == FALSE [1] TRUE > any(is.na(decostand(x, "alr", na.rm = FALSE, pseudocount = 1, reference=3)[!is.na(x[, -3] - x[, 3])])) == FALSE [1] TRUE > # Works correctly also with other MARGIN > inds <- !is.na(x) # Non-NA values > max(abs(decostand(x, "clr", MARGIN = 2, na.rm = FALSE, pseudocount = 1)[inds] - + decostand(x, "clr", MARGIN = 2, na.rm = TRUE, pseudocount = 1)[inds])) < 1e-6 [1] TRUE Warning message: In .calc_clr(t(x), na.rm = na.rm, ...) : replacing missing values with zero for `method = "clr"` - disable this with `na.rm = FALSE` > # Results match for the non-NA values always (with tolerance 1e-6) > inds <- !is.na(x) # Non-NA values > max(abs(na.omit(decostand(x, "alr", na.rm = FALSE, pseudocount = 1)[inds] - + decostand(x, "alr", na.rm = TRUE, pseudocount = 1)[inds]))) < 1e-6 [1] TRUE Warning message: In .calc_alr(x, na.rm = na.rm, ...) : replacing missing values with zero for `method = "alr"` - disable this with `na.rm = FALSE` > > # Test that NAs are handled as expected in ALR > set.seed(4354353) > x <- testdata > x[sample(prod(dim(x)), 50)] <- NA > x[4, c(2, 10)] <- NA # insert some NAs in the data > # NAs in the output share NAs with the reference vector > all(is.na(decostand(x, "alr", na.rm = FALSE, pseudocount = 1, reference = 4))[which(is.na(x[,4])),]) [1] TRUE > # Output vector has same NAs than the original vector and reference vector > all(is.na(decostand(x, "alr", na.rm = FALSE, pseudocount = 1, reference = 4)[,2]) == (is.na(x[,4]) | is.na(x[,2]))) [1] TRUE > # No NAs after removing them > !any(is.na(decostand(x, "alr", na.rm = TRUE, pseudocount = 1, reference = 4))) [1] TRUE Warning message: In .calc_alr(x, na.rm = na.rm, ...) : replacing missing values with zero for `method = "alr"` - disable this with `na.rm = FALSE` > # All NAs are replaced by zero > all((rowMeans(decostand(x, "alr", na.rm = TRUE, pseudocount = 1, reference = 4) == 0) == 1) == is.na(x[,4])) [1] TRUE Warning message: In .calc_alr(x, na.rm = na.rm, ...) : replacing missing values with zero for `method = "alr"` - disable this with `na.rm = FALSE` > > # Expect that error does not occur > decostand(testdata, method = "rclr") col1 col2 col3 col4 col5 col6 row1 0.1226672 0.083885024 0.041910621 0.05447274 -0.04533134 -0.18908731 row2 0.8149653 0.470058458 0.231710691 0.71124682 -0.49189567 -1.52781566 row3 0.1724082 -0.031650021 -0.065869203 1.13460213 0.10815032 -0.32149030 row4 -0.3834569 -0.211244585 -0.071207355 -0.70849645 -0.10970962 0.45166721 row5 -0.3323267 -0.136124506 -0.070393221 -0.45427055 0.38525044 0.84787503 row6 -0.6673927 -0.486535840 -0.182866040 -0.80583839 -0.50368486 0.37265881 row7 -0.6709047 -0.504589464 -0.226883982 -0.39074608 -0.15214518 0.64519808 row8 0.1730506 0.051337111 0.000253358 0.58139842 0.04619966 -0.26448486 row9 0.1190910 0.097046186 0.083635587 -0.36515926 -0.39605885 -0.45224452 row10 0.6633127 0.713521620 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-0.180851965 row19 5.393378e-01 -0.809036870 -0.18006391 -0.33572853 0.272338126 row20 7.001738e-02 -0.332387241 0.54352892 0.25288849 0.033518813 col50 row1 -0.01146742 row2 -0.29991806 row3 -0.71138561 row4 0.39338841 row5 0.22524476 row6 0.41291181 row7 0.13278653 row8 -0.34839714 row9 0.27027251 row10 0.40020057 row11 0.13373627 row12 0.72284725 row13 -0.18299523 row14 0.06810050 row15 -0.84680931 row16 -0.31383814 row17 -1.15104467 row18 -0.14885299 row19 0.91755915 row20 0.33766080 attr(,"scaled:center") [1] -0.0031079619 -0.0463480942 -0.0267762284 0.0089869339 0.0380073003 [6] -0.0216677154 -0.0095070243 -0.0129853877 -0.0151765084 0.0739366224 [11] 0.0813752690 0.0153170116 -0.0001358339 -0.0203385433 -0.0102092705 [16] -0.0133788389 0.0543704488 -0.0876478093 0.0272043261 -0.0319186959 attr(,"decostand") [1] "rclr" > decostand(testdata, method = "clr", pseudocount = 1) col1 col2 col3 col4 col5 col6 row1 2.1032316 2.0791341 -1.634438 1.3100010 0.56278658 1.0046193 row2 2.1761262 -1.6305363 -1.630536 2.2612840 -1.63053632 -1.6305363 row3 -1.1900323 2.7219907 -1.190032 2.1058045 -1.19003233 -1.1900323 row4 1.8206928 2.1229737 2.165533 0.3737738 1.06692099 -1.7056677 row5 1.0992724 -1.6087778 -1.608778 1.9747412 1.75851808 2.1978847 row6 -0.6739464 -1.0794115 2.077589 -1.7725587 0.17335146 2.0775889 row7 1.9153276 -1.3035482 2.385331 -1.3035482 -1.30354821 1.9153276 row8 -1.1135727 2.6240970 1.594478 2.4127879 -1.11357267 2.0644812 row9 1.5569880 -1.2762253 2.594976 1.7195069 -1.27622533 0.3332126 row10 1.9431078 2.4074134 1.417015 -1.1479346 -1.14793462 -1.1479346 row11 -1.2687951 1.9500807 -1.268795 -1.2687951 1.62157666 1.2161115 row12 1.6626874 1.9929291 -1.472807 -1.4728068 -1.47280678 2.0825413 row13 2.3237457 -1.4604440 -1.460444 -1.4604440 -1.46044396 1.7976526 row14 -1.3606232 1.6351090 2.006673 -1.3606232 2.42356641 -1.3606232 row15 -1.4083907 -1.4083907 -1.408391 -1.4083907 2.37579893 0.2010472 row16 2.2983853 2.0180833 1.749819 1.6272170 1.66977665 0.1011607 row17 2.6744936 -1.2173267 1.727112 -1.2173267 -1.21732666 -1.2173267 row18 -1.1331537 -1.1331537 -1.133154 -1.1331537 -0.03454143 -1.1331537 row19 -1.6565066 2.0811630 -1.656507 -0.9633594 1.56236920 0.8284000 row20 0.2342048 0.7732013 -1.018558 -1.7117053 0.85324403 1.8146552 col7 col8 col9 col10 col11 col12 row1 -1.6344380 -1.634438 2.1497516329 2.1267621 2.0544415 -1.6344380 row2 -1.6305363 -1.630536 0.1612231483 1.9803816 -0.5319240 -1.6305363 row3 0.8894092 -1.190032 1.2078629468 -1.1900323 2.3934866 0.4194056 row4 0.5969174 1.661628 -1.7056677321 -1.7056677 -1.7056677 2.1444799 row5 1.5267165 1.569276 0.0006601595 1.6100981 -1.6087778 -1.6087778 row6 -1.7725587 1.723949 -1.7725586865 1.4855379 -1.7725587 1.7239489 row7 2.4576519 -1.303548 -1.3035482139 2.4576519 2.5676528 -1.3035482 row8 2.7365749 2.693090 -1.1135726669 2.2876247 -1.1135727 -1.1135727 row9 -1.2762253 -1.276225 -1.2762253269 -1.2762253 -1.2762253 -1.2762253 row10 1.9875596 -1.147935 -1.1479346150 -1.1479346 -1.1479346 -1.1479346 row11 2.4447770 1.866699 1.9092587295 -1.2687951 0.3406428 -1.2687951 row12 0.9250885 -1.472807 -1.4728067753 1.8944891 1.2997819 -1.4728068 row13 1.1786134 1.906852 -1.4604439551 -1.4604440 -1.4604440 -1.4604440 row14 2.4460393 -1.360623 -1.3606232237 -1.3606232 -1.3606232 -1.3606232 row15 2.1751282 1.536048 -1.4083907017 -1.4083907 -1.4083907 2.5234349 row16 0.6889474 1.859019 -1.5082771842 -1.5082772 2.0470709 1.9257100 row17 2.2791809 -1.217327 2.2791808981 0.1689677 0.8621149 -1.2173267 row18 -1.1331537 -1.133154 1.1694313743 -1.1331537 -1.1331537 2.5044324 row19 1.6393302 -1.656507 1.7446907552 1.5623692 0.1352528 0.1352528 row20 0.8532440 1.232734 1.6204991818 1.4663485 1.9771741 -1.7117053 col13 col14 col15 col16 col17 col18 row1 -1.63443800 1.456604 -1.634438 -1.634438 -1.6344380 -1.6344380 row2 0.31537383 -1.630536 -1.630536 -1.630536 -1.6305363 -1.6305363 row3 -1.19003233 -1.190032 -1.190032 -1.190032 -1.1900323 2.2439549 row4 -1.70566773 2.122974 2.186153 -1.705668 -1.7056677 -1.7056677 row5 0.18298172 -1.608778 -1.608778 2.054784 0.3371324 -1.6087778 row6 -1.77255869 -1.772559 -1.772559 2.011631 2.0341038 -1.7725587 row7 -1.30354821 2.334038 -1.303548 -1.303548 -1.3035482 2.5250932 row8 -1.11357267 -1.113573 -1.113573 -1.113573 2.4417754 -1.1135727 row9 -1.27622533 2.507964 -1.276225 -1.276225 1.8592689 2.1895106 row10 2.65872787 2.540945 -1.147935 -1.147935 2.1101619 2.5897350 row11 -1.26879510 -1.268795 2.368791 -1.268795 2.4924050 -1.2687951 row12 -1.47280678 1.928391 1.705247 2.419014 -1.4728068 0.7244178 row13 -1.46044396 -1.460444 -1.460444 -1.460444 -1.4604440 -1.4604440 row14 0.02567114 -1.360623 -1.360623 -1.360623 -1.3606232 -1.3606232 row15 -1.40839070 2.280489 -1.408391 -1.408391 1.5360483 1.8104851 row16 1.92571002 -1.508277 -1.508277 2.275912 -1.5082772 -1.5082772 row17 -1.21732666 -1.217327 2.078510 -1.217327 0.1689677 1.7784056 row18 0.47628419 -1.133154 1.264742 1.639435 -1.1331537 2.6954877 row19 0.42293492 -1.656507 -1.656507 2.057065 1.8092293 -1.6565066 row20 0.85324403 -1.711705 -1.711705 2.200318 -1.7117053 -1.7117053 col19 col20 col21 col22 col23 col24 row1 0.3114721 0.5627866 -1.6344380 -1.6344380010 1.138151 -1.6344380 row2 1.1420524 -1.6305363 -0.9373891 -1.6305363210 2.130664 -1.6305363 row3 -1.1900323 -1.1900323 1.4490250 2.7017879721 -1.190032 2.3363282 row4 2.1861526 -1.7056677 -1.7056677 1.7600681707 1.905250 -1.7056677 row5 1.0992724 0.7891175 2.0547839 0.0006601595 -1.608778 -1.6087778 row6 0.4246659 1.7239489 -1.7725587 -1.7725586865 -1.772559 0.9354915 row7 1.2614011 -1.3035482 2.5882721 -1.3035482139 -1.303548 -1.3035482 row8 2.6706170 2.7365749 -1.1135727 -1.1135726669 2.352163 -1.1135727 row9 -1.2762253 -1.2762253 -1.2762253 1.7195069467 1.614146 -1.2762253 row10 -1.1479346 -1.1479346 -1.1479346 -0.4547874344 -1.147935 2.4629833 row11 -1.2687951 2.3421228 -1.2687951 -0.5756479203 -1.268795 -1.2687951 row12 -1.4728068 -0.3741945 0.7244178 0.3189526940 -1.472807 -1.4728068 row13 1.8353929 1.3727694 -1.4604440 -1.4604439551 1.584078 2.2772257 row14 -1.3606232 -1.3606232 1.7748710 -1.3606232237 1.472590 2.4680182 row15 1.6826518 1.7271035 -1.4083907 2.3757989322 -1.408391 -1.4083907 row16 2.3629238 -1.5082772 -1.5082772 -1.5082771842 -1.508277 -1.5082772 row17 -1.2173267 2.6538743 -1.2173267 -1.2173266634 -1.217327 1.1805686 row18 -1.1331537 2.6045159 2.2341421 -1.1331537187 1.757218 2.6045159 row19 -1.6565066 0.1352528 -1.6565066 -1.6565066265 2.032373 -1.6565066 row20 1.8146552 -1.7117053 -1.7117053 -1.7117053284 1.871814 1.6894921 col25 col26 col27 col28 col29 col30 row1 -1.6344380 -1.63443800 1.8312979 1.50105621 1.7995492 -1.6344380 row2 0.5666883 -1.63053632 2.1536533 2.21961128 -1.6305363 2.1306638 row3 2.3653157 -0.09142004 2.3363282 -0.09142004 -1.1900323 1.7003394 row4 -1.7056677 1.76006817 -1.7056677 -1.70566773 -1.7056677 1.0669210 row5 2.1047943 -1.60877775 1.1638110 0.33713240 -1.6087778 -1.6087778 row6 -1.7725587 1.91632077 1.2231736 1.52327818 -1.7725587 -1.7725587 row7 2.3853312 0.89367636 -1.3035482 1.58682354 1.9153276 2.6084748 row8 -1.1135727 -1.11357267 2.6476274 -1.11357267 -1.1135727 2.7984503 row9 -1.2762253 2.43734674 -1.2762253 -1.27622533 -0.1776130 -1.2762253 row10 -1.1479346 -1.14793462 -1.1479346 -1.14793462 2.1479023 1.9431078 row11 2.6230252 -1.26879510 2.3421228 2.44477697 1.4392551 -1.2687951 row12 -1.4728068 1.85939773 -1.4728068 -1.47280678 -1.4728068 2.0535537 row13 2.1230750 -1.46044396 1.9407534 1.48399502 2.4515791 -1.4604440 row14 -1.3606232 -1.36062322 1.4119655 -1.36062322 -1.3606232 2.5712024 row15 -1.4083907 -0.30977841 -1.4083907 -1.40839070 -1.4083907 2.0573452 row16 -1.5082772 -1.50827718 -1.5082772 -1.50827718 1.1307801 -1.5082772 row17 2.0785102 -1.21732666 -1.2173267 2.54387345 -1.2173267 0.3921112 row18 -1.1331537 -1.13315372 0.2531406 -1.13315372 -1.1331537 -1.1331537 row19 -1.6565066 -1.65650663 -0.5578943 2.03237283 0.8284000 -1.6565066 row20 -1.7117053 0.48551925 -1.7117053 1.62049918 0.3677362 -1.7117053 col31 col32 col33 col34 col35 col36 row1 -1.63443800 -1.63443800 -1.6344380 1.6977665 -1.6344380 1.6977665 row2 -1.63053632 0.67204877 -1.6305363 2.0830357 2.0330253 -0.9373891 row3 2.17726350 -1.19003233 1.0071923 -1.1900323 2.0680642 -1.1900323 row4 -1.70566773 1.47238610 1.8206928 1.7908398 2.1444799 1.4298265 row5 -0.51016546 -1.60877775 -0.5101655 -1.6087778 -1.6087778 1.7234268 row6 -1.77255869 1.27196375 1.2231736 1.5596458 1.7239489 1.3184838 row7 2.02865630 -1.30354821 -1.3035482 0.6423619 -1.3035482 -1.3035482 row8 -1.11357267 -1.11357267 -1.1135727 -1.1135727 -1.1135727 -1.1135727 row9 -1.27622533 1.85926889 2.5739223 2.2791227 1.9018285 -1.2762253 row10 -0.04932233 -1.14793462 2.6132655 -1.1479346 0.6438249 0.9315069 row11 -1.26879510 -1.26879510 -1.2687951 -1.2687951 1.2161115 2.3947665 row12 -0.37419449 -1.47280678 2.3558346 1.8944891 -1.4728068 2.1907549 row13 1.53528832 -1.46044396 2.1771422 -1.4604440 2.2031177 -1.4604440 row14 -1.36062322 -1.36062322 1.6838992 -1.3606232 2.1947248 1.8582526 row15 2.14695736 0.53751945 -0.7152435 -1.4083907 -1.4083907 2.0255965 row16 2.25292293 1.58276527 -1.5082772 -1.5082772 1.5827653 1.6272170 row17 -1.21732666 -1.21732666 -1.2173267 -1.2173267 0.1689677 -1.2173267 row18 -1.13315372 -0.03454143 -1.1331537 -1.1331537 2.6954877 -1.1331537 row19 -1.65650663 1.92701231 -1.6565066 -1.6565066 2.0811630 -1.6565066 row20 -1.71170533 1.37933712 -1.7117053 -1.7117053 -1.7117053 1.1786664 col37 col38 col39 col40 col41 col42 row1 -1.634438 2.1942034 2.2775850 1.0046193 2.1032316 -1.6344380 row2 1.008521 0.4489052 1.4605061 1.2026770 2.0830357 1.0775139 row3 -1.190032 0.4194056 -1.1900323 -1.1900323 -1.1900323 -1.1900323 row4 -1.705668 2.1009948 -1.7056677 -1.7056677 -0.3193734 -1.7056677 row5 -1.608778 2.0801017 1.3869545 1.3869545 -0.5101655 -1.6087778 row6 1.723949 -1.7725587 1.1718803 -1.7725587 -1.7725587 2.0775889 row7 -1.303548 -1.3035482 2.4576519 -1.3035482 -0.2049359 -1.3035482 row8 -1.113573 2.2537232 -1.1135727 1.5254847 -1.1135727 -1.1135727 row9 -1.276225 2.5079643 -1.2762253 -1.2762253 -1.2762253 -1.2762253 row10 1.249961 -1.1479346 0.4615033 -1.1479346 -1.1479346 -1.1479346 row11 -1.268795 -1.2687951 -1.2687951 2.3421228 -1.2687951 -1.2687951 row12 2.377341 -1.4728068 -1.4728068 -1.4728068 2.2883933 -1.4728068 row13 2.410757 -1.4604440 -1.4604440 1.4299278 -1.4604440 -1.4604440 row14 2.377046 -1.3606232 -1.3606232 -1.3606232 1.2043261 -1.3606232 row15 -1.408391 -1.4083907 1.7696631 0.9895046 1.8104851 0.7888339 row16 -1.508277 -1.5082772 -1.5082772 -1.5082772 1.0566722 0.9766295 row17 -1.217327 0.5744328 2.5438735 -1.2173267 2.5668630 -1.2173267 row18 1.757218 -1.1331537 2.5804183 -1.1331537 -1.1331537 1.8112853 row19 1.051544 2.1936410 1.6015899 1.8400009 -1.6565066 2.1501559 row20 1.899213 2.0949572 2.1169361 -1.7117053 -0.1022674 -1.7117053 col43 col44 col45 col46 col47 col48 row1 1.7667593807 2.2573823 2.103232 -1.6344380 1.7667594 -1.6344380 row2 1.8351995818 1.3139027 1.665301 -1.6305363 2.2612840 -0.9373891 row3 -1.1900323260 -1.1900323 1.449025 -1.1900323 2.7017880 -1.1900323 row4 0.3737738096 -1.7056677 1.695530 -1.7056677 -1.7056677 -1.7056677 row5 0.0006601595 -1.6087778 2.152422 0.4706638 0.9561716 1.6870591 row6 -1.7725586865 1.8109603 -1.772559 2.0341038 1.8383592 -1.7725587 row7 -1.3035482139 -1.3035482 -1.303548 -1.3035482 -1.3035482 -1.3035482 row8 -1.1135726669 -1.1135727 -1.113573 -1.1135727 -1.1135727 2.0219215 row9 2.1895105759 -1.2762253 2.250135 0.3332126 -1.2762253 0.6696848 row10 -1.1479346150 -1.1479346 2.317801 -1.1479346 -1.1479346 -1.1479346 row11 -0.1701828122 2.1969408 -1.268795 -1.2687951 -1.2687951 -1.2687951 row12 1.8230300907 2.1647794 -1.472807 -1.4728068 2.3558346 -1.4728068 row13 -1.4604439551 -1.4604440 1.178613 2.3681974 2.2531281 -1.4604440 row14 -1.3606232237 2.1358843 -1.360623 2.3770464 2.4460393 1.8582526 row15 1.4819810562 -1.4083907 -1.408391 1.8874462 1.9238138 -1.4083907 row16 -1.5082771842 1.3820946 2.229392 1.7875597 -1.5082772 1.0566722 row17 0.7285834857 -1.2173267 -1.217327 2.7144990 -1.2173267 1.3476227 row18 2.6510359152 -1.1331537 -1.133154 1.0640709 -1.1331537 2.3008335 row19 -1.6565066265 2.1276830 1.434536 -1.6565066 -1.6565066 1.1160821 row20 2.0949571614 0.3677362 1.284027 -1.7117053 1.6894921 1.2327337 col49 col50 row1 -1.6344380 -1.6344380 row2 1.2026770 -1.6305363 row3 -1.1900323 -1.1900323 row4 1.7908398 1.6955296 row5 -1.6087778 -1.6087778 row6 1.9410134 -1.7725587 row7 -1.3035482 2.3853312 row8 -1.1135727 -1.1135727 row9 2.0910705 -1.2762253 row10 -1.1479346 2.5156270 row11 -1.2687951 1.9893014 row12 -1.4728068 -1.4728068 row13 1.0244627 2.0360636 row14 0.8366014 2.2502947 row15 -1.4083907 -1.4083907 row16 -1.5082772 -1.5082772 row17 -1.2173267 0.5744328 row18 -1.1331537 -1.1331537 row19 1.4345358 -1.6565066 row20 -1.7117053 -1.7117053 attr(,"parameters") attr(,"parameters")$means row1 row2 row3 row4 row5 row6 row7 row8 1.634438 1.630536 1.190032 1.705668 1.608778 1.772559 1.303548 1.113573 row9 row10 row11 row12 row13 row14 row15 row16 1.276225 1.147935 1.268795 1.472807 1.460444 1.360623 1.408391 1.508277 row17 row18 row19 row20 1.217327 1.133154 1.656507 1.711705 attr(,"parameters")$pseudocount [1] 1 attr(,"parameters")$margin [1] 1 attr(,"decostand") [1] "clr" > # Expect error > #class(try(decostand(testdata, method = "clr"))) == "try-error" > #class(try(decostand(testdata, method = "clr", pseudocount = 0))) == "try-error" > > # 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 <- decostand(test, method = "rclr") > test2 <- 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 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 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 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 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 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 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 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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 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 FALSE > > # Compare rclr with imputation to without imputation + matrix completion > # rclr transformation with matrix completion for the 0/NA entries > x1 <- decostand(varespec, method = "rclr", impute = TRUE) > > # rclr transformation with no matrix completion for the 0/NA entries > x2 <- decostand(varespec, method = "rclr", impute = FALSE) > > # Matrix completion > x2c <- optspace(x2, ropt = 3, niter = 5, tol = 1e-5, verbose = FALSE)$M > all(as.matrix(x1) == as.matrix(x2c)) [1] TRUE > > x2c <- optspace(x2, niter = 5, tol = 1e-5, verbose = FALSE)$M > all(as.matrix(x1) == as.matrix(x2c)) [1] TRUE > > ############################# NAMES #################################### > > # Tests that dimensins have correct names > all(rownames(decostand(testdata + 1, method = "clr")) == rownames(testdata)) [1] TRUE > all(colnames(decostand(testdata, method = "clr", pseudocount = 1)) == colnames(testdata)) [1] TRUE > all(rownames(decostand(testdata, method = "rclr")) == rownames(testdata)) [1] TRUE > all(colnames(decostand(testdata, method = "rclr")) == colnames(testdata)) [1] TRUE > > ######################################################################## > > # Count vs. Relative data > > # CLR is identical with count and relative data > a1 <- decostand(testdata.with.pseudo, method = "clr") > a2 <- decostand(relative.with.pseudo, method = "clr") > max(abs(a1 - a2)) < 1e-6 # Tolerance [1] TRUE > > # rCLR is identical with count and relative data > a1 <- decostand(testdata.with.pseudo, method = "rclr") > a2 <- decostand(relative.with.pseudo, method = "rclr") > max(abs(a1 - a2)) < 1e-6 # Tolerance [1] TRUE > > # ALR is identical with count and relative data > a1 <- decostand(testdata.with.pseudo, method = "alr") > a2 <- decostand(relative.with.pseudo, method = "alr") > max(abs(a1 - a2)) < 1e-6 # Tolerance [1] TRUE > > ####### # ALR transformation drops one feature ################ > ncol(decostand(testdata.with.pseudo, "alr")) == ncol(testdata.with.pseudo) - 1 [1] TRUE > > proc.time() user system elapsed 1.48 0.28 1.70