R Under development (unstable) (2025-06-04 r88278 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. > ###<--- 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") [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.1226672 0.083885024 0.041910621 0.05447274 -0.04533134 -0.18908731 [2,] 0.8149653 0.470058458 0.231710691 0.71124682 -0.49189567 -1.52781566 [3,] 0.1724082 -0.031650021 -0.065869203 1.13460213 0.10815032 -0.32149030 [4,] -0.3834569 -0.211244585 -0.071207355 -0.70849645 -0.10970962 0.45166721 [5,] -0.3323267 -0.136124506 -0.070393221 -0.45427055 0.38525044 0.84787503 [6,] -0.6673927 -0.486535840 -0.182866040 -0.80583839 -0.50368486 0.37265881 [7,] -0.6709047 -0.504589464 -0.226883982 -0.39074608 -0.15214518 0.64519808 [8,] 0.1730506 0.051337111 0.000253358 0.58139842 0.04619966 -0.26448486 [9,] 0.1190910 0.097046186 0.083635587 -0.36515926 -0.39605885 -0.45224452 [10,] 0.6633127 0.713521620 0.336569470 -0.44523499 0.64989868 0.05506890 [11,] 0.8000872 0.792864087 0.344564514 -0.00761715 0.91595579 0.08565031 [12,] -0.1798694 0.003479676 0.060982931 -1.14853716 -0.26767294 0.16929812 [13,] 0.1797414 0.107851412 0.035063958 0.32803205 0.10472611 -0.15629534 [14,] -0.3444308 -0.291768542 -0.121991147 -0.19307592 -0.28375752 0.11920058 [15,] 0.1526191 -0.031350577 -0.089973902 1.32487681 0.42205256 -0.02089938 [16,] 0.2057127 0.080116509 0.017885806 0.53903869 0.01447757 -0.32249231 [17,] 1.4429676 1.029915281 0.354660623 2.12775042 1.36374012 -0.60902694 [18,] -1.3538426 -1.224333827 -0.552222580 -0.12779398 -0.93626704 0.51363144 [19,] -0.1677221 0.066362031 0.102374070 -1.44400292 -0.24228797 0.24697846 [20,] -0.7466771 -0.578840033 -0.228204199 -0.71064524 -0.58164026 0.35660970 [,7] [,8] [,9] [,10] [,11] [1,] -0.10333753 -0.061834870 -0.012419388 0.04117590 -0.28080372 [2,] -0.89784013 -0.484099540 -0.179036025 0.36712737 -1.65200123 [3,] 0.00728522 -0.089018878 -0.074921712 -0.27311489 0.71680279 [4,] 0.07205560 0.146266729 0.016665885 0.17407605 0.21073783 [5,] 0.56590088 0.259725772 0.148156481 -0.27156541 0.63213431 [6,] -0.21703996 0.141627821 -0.113953063 0.48254975 0.57873862 [7,] 0.14473972 0.225449581 -0.047680646 0.11941434 1.19963319 [8,] -0.04323735 -0.079969095 -0.036089782 -0.12286064 0.15136965 [9,] -0.46101834 -0.142544547 -0.078394040 0.40366352 -0.88791467 [10,] 0.40793913 -0.035111289 0.291155487 -0.37806286 -1.66624913 [11,] 0.61706750 -0.029899200 0.336867458 -0.65938725 -1.42512023 [12,] -0.17228727 0.045706663 0.025940983 0.40553883 -0.81041005 [13,] 0.02269258 -0.052724636 0.009319004 -0.12455575 -0.06960365 [14,] -0.14333581 0.055046591 -0.089714574 0.22852207 0.49553636 [15,] 0.34262514 0.001004873 0.002213205 -0.56683512 1.14123008 [16,] -0.08996213 -0.099375213 -0.039571947 -0.08557087 0.01040923 [17,] 0.74909800 -0.244763765 0.291912531 -1.33776835 -0.71488346 [18,] -0.39146255 0.237571508 -0.362921281 0.63804357 2.65824999 [19,] -0.14263463 0.063281026 0.065728145 0.43918825 -1.10545605 [20,] -0.26724808 0.143660469 -0.153256723 0.52042149 0.81760012 [,12] [,13] [,14] [,15] [,16] [1,] -0.122964042 -0.007136787 -0.035329798 -0.036293705 -0.06642869 [2,] -0.733540240 -0.170630594 -0.384590264 -0.421075707 -0.67895229 [3,] 0.363381390 0.385791664 -0.306207478 -0.170724832 -0.70351516 [4,] 0.049564876 -0.347295915 0.197766838 0.081298833 0.49407193 [5,] 0.295864760 0.224976099 0.252299898 0.310211487 0.40245592 [6,] 0.158194441 -0.836690371 0.139981604 -0.123686179 0.55767827 [7,] 0.479797884 -0.390702004 0.114364062 -0.009455921 0.36059180 [8,] 0.092902386 0.207615303 -0.164760310 -0.092291798 -0.38055824 [9,] -0.438765872 -0.454855805 -0.007749531 -0.149166441 0.13506805 [10,] -0.655670797 0.726238661 0.255644265 0.463498826 0.21574722 [11,] -0.505134490 1.121299723 0.192109481 0.525299778 -0.03834541 [12,] -0.422042031 -0.554061046 0.268657326 0.083031048 0.69022193 [13,] -0.004874668 0.216634347 -0.077506359 -0.007028283 -0.22136049 [14,] 0.171850220 -0.410015073 0.002417544 -0.123090721 0.15055476 [15,] 0.587884633 0.742800478 -0.276563978 -0.031773452 -0.76752170 [16,] 0.027401760 0.173755687 -0.163121526 -0.101431447 -0.36697547 [17,] -0.066420935 2.170835810 -0.340266457 0.344168429 -1.39704267 [18,] 1.024310427 -1.296960126 -0.123369073 -0.511788933 0.23901335 [19,] -0.556893853 -0.586530082 0.358844331 0.158160156 0.86864891 [20,] 0.255154150 -0.915069970 0.097379425 -0.187861137 0.50664797 [,17] [,18] [,19] [,20] [,21] [1,] 0.067775402 -0.0727300354 -0.002342813 -0.13030273 -0.33220235 [2,] 0.433619583 -0.4300366165 0.068725070 -0.69521048 -2.50608287 [3,] -0.443486819 0.2247009327 0.252705503 0.89797532 0.19170754 [4,] 0.169024295 0.0247487910 -0.123056534 -0.26620031 0.36117527 [5,] -0.230878036 0.1713977899 -0.067561501 0.27492891 1.36077917 [6,] 0.325574375 0.0905017794 -0.111158678 -0.31468107 0.09703062 [7,] -0.072604473 0.2838758239 -0.010011213 0.32771074 0.95562532 [8,] -0.179667389 0.0595393417 0.118800681 0.35297781 -0.09573147 [9,] 0.463776775 -0.2626815501 -0.103693214 -0.76777890 -1.17576737 [10,] 0.081811039 -0.3971354254 -0.190697124 -0.69553738 -0.13810761 [11,] -0.200899269 -0.3052436973 -0.101877782 -0.26328416 0.25908609 [12,] 0.549370437 -0.2591011928 -0.255286052 -1.02188761 -0.51319911 [13,] -0.112311634 -0.0009122632 0.056748572 0.16128400 -0.05920870 [14,] 0.093665629 0.1022672877 -0.003515761 0.02004537 0.06353517 [15,] -0.734451700 0.3586538344 0.301509058 1.32906192 0.94159661 [16,] -0.125881258 0.0202978869 0.104408420 0.25233030 -0.24131969 [17,] -1.038378136 -0.0296182586 0.333799548 1.23406342 0.47284067 [18,] -0.003078877 0.6139392921 0.138957096 0.78137896 0.80241174 [19,] 0.657977094 -0.3418340495 -0.328237568 -1.28287521 -0.56903376 [20,] 0.299042961 0.1493703290 -0.078215709 -0.19399890 0.12486475 [,22] [,23] [,24] [,25] [,26] [,27] [1,] 0.01493330 0.09272288 -0.03331729 -0.12515308 0.2084107 0.1022861 [2,] 1.05681310 0.69685352 -0.18337484 -1.03784667 0.9189843 0.8812151 [3,] 1.40063639 -0.25217825 -0.01657225 0.04330262 -1.3004483 0.8060816 [4,] -0.41579725 0.04197484 0.10730421 0.09500940 0.1991080 -0.6679457 [5,] -1.07387437 -0.40167281 0.04618015 0.62723424 -0.1790447 -0.4476095 [6,] 0.60875018 0.23842584 0.24219229 -0.17654709 -0.2035482 -0.9213644 [7,] 0.57836781 -0.15134783 0.22310650 0.23162434 -0.9060153 -0.6440303 [8,] 0.62178785 -0.07519320 -0.03576216 -0.04026921 -0.4727124 0.4620809 [9,] 0.04584542 0.47896521 -0.01216727 -0.52816195 0.9013176 -0.1819387 [10,] -2.94350066 -0.08083124 -0.31611948 0.31344358 1.9930196 0.1598638 [11,] -2.95352383 -0.32743751 -0.37137170 0.53963163 1.5954333 0.5274092 [12,] -1.08966741 0.38475521 0.03645154 -0.21873247 1.3429961 -0.8300005 [13,] 0.08012005 -0.06336801 -0.05651639 0.01540257 -0.1003876 0.3142592 [14,] 0.72643187 0.09272167 0.13814855 -0.11253964 -0.4259220 -0.3401583 [15,] 1.10139929 -0.56863956 -0.03651956 0.41364267 -1.7008588 0.9330633 [16,] 0.57274547 -0.02122994 -0.04508165 -0.09710546 -0.3290401 0.4530034 [17,] -1.24389389 -0.83237616 -0.53660831 0.70048521 -0.1922073 2.2479616 [18,] 3.52297572 0.07220864 0.55240373 -0.22535207 -2.6433131 -0.9367051 [19,] -1.64025313 0.43679784 0.01564416 -0.20476390 1.7974781 -1.0027870 [20,] 1.02970409 0.23884887 0.28197978 -0.21330472 -0.5032498 -0.9146848 [,28] [,29] [,30] [,31] [,32] [,33] [1,] 0.19770943 -0.12303937 0.07846402 0.09345842 0.02381466 0.37893567 [2,] 1.14535525 -0.90579599 0.78358231 1.45675694 -0.29562538 2.51565864 [3,] -0.68077053 0.47316625 -0.02162212 1.55410267 -0.81749767 -0.85247814 [4,] -0.03207079 -0.14395848 0.02448791 -0.77330190 0.24388257 -0.17817513 [5,] -0.44494324 0.52671523 -0.56024441 -1.12752224 0.40027531 -1.19978495 [6,] -0.21385957 -0.45549809 0.46162855 -0.20605357 -0.18612247 -0.16615732 [7,] -0.76642840 0.14691905 0.05823185 -0.06644553 -0.32453251 -1.24246395 [8,] -0.19514177 0.16890057 0.01294821 0.76861061 -0.35251503 -0.17781282 [9,] 0.74106288 -0.72112433 0.47945435 -0.05108295 0.16086368 1.43807789 [10,] 1.13078778 0.12050916 -0.73811813 -1.99117723 1.43292752 1.14917765 [11,] 0.85665594 0.52479352 -1.01323211 -1.75796110 1.33475440 0.60832053 [12,] 0.77462118 -0.66789603 0.21659633 -1.33656615 0.71243935 1.13881571 [13,] -0.01363103 0.13077787 -0.07766026 0.27718246 -0.07335128 -0.01046656 [14,] -0.27627125 -0.17216918 0.28462053 0.28202816 -0.31716936 -0.26699026 [15,] -1.05744370 0.94890937 -0.39803622 1.40100730 -0.78986694 -1.67893218 [16,] -0.08473237 0.08871831 0.05380125 0.73243735 -0.30771438 0.01669270 [17,] -0.01401816 1.47878961 -1.30828126 0.65004724 0.24057118 -0.53571028 [18,] -1.70367143 -0.19799430 0.94543546 1.84395796 -1.70080286 -2.01656843 [19,] 1.01959017 -0.76780681 0.16455694 -1.84836202 1.01178109 1.42527361 [20,] -0.38280041 -0.45291635 0.55338681 0.09888358 -0.39611186 -0.34541240 [,34] [,35] [,36] [,37] [,38] [,39] [1,] 0.18630217 0.06808972 -0.28881453 -0.01405009 -0.08072494 -0.01944272 [2,] 1.29842362 0.71947256 -2.22183664 -0.05166909 -0.90339842 0.12888561 [3,] -0.27472247 -0.08005733 0.04709895 -0.12029912 -0.96415579 0.45265160 [4,] -0.15181802 0.09201133 0.37245212 0.14468721 0.62796548 -0.11739685 [5,] -0.64930656 -0.54377138 1.22223555 -0.02726618 0.57405623 -0.21210271 [6,] -0.11363333 0.59123404 0.13226339 0.32683841 0.61630029 0.14327224 [7,] -0.60184245 0.14883516 0.83572680 0.20215121 0.38203378 0.21914586 [8,] -0.02015018 -0.02479807 -0.13983986 -0.07630150 -0.50973622 0.19011645 [9,] 0.66597154 0.50886266 -0.97907682 0.11205968 0.15877470 -0.09418811 [10,] 0.40686667 -0.84270109 -0.02175942 -0.31282608 0.51191685 -0.84922874 [11,] 0.17801167 -1.16491415 0.28466590 -0.44157819 0.20784056 -0.80269623 [12,] 0.42308947 0.28862021 -0.32772696 0.17074076 0.92168121 -0.39168434 [13,] 0.02136264 -0.11334958 -0.07763145 -0.08376072 -0.27228515 0.03147012 [14,] -0.11396429 0.34695117 0.05391179 0.16565696 0.11802003 0.19915998 [15,] -0.66932007 -0.48443051 0.68052406 -0.21992938 -1.01565608 0.43573917 [16,] 0.06830364 0.01646157 -0.26013791 -0.07148722 -0.48913800 0.16333410 [17,] -0.15365551 -1.62101434 0.26702051 -0.80746535 -1.56853013 -0.22025940 [18,] -0.84161517 1.15830749 0.62218763 0.56799507 -0.01687293 1.03832833 [19,] 0.51843217 0.24188449 -0.34112807 0.17411692 1.18301640 -0.56177901 [20,] -0.17673553 0.69430604 0.13986496 0.36238668 0.51889213 0.26667464 [,40] [,41] [,42] [,43] [,44] [1,] 0.01512375 0.32695716 -0.002892249 0.13421290 0.07718009 [2,] -0.13615762 2.34342645 -0.181194784 1.49223230 0.32046087 [3,] -0.52521031 0.24154602 -0.647267291 0.57451380 -0.58460107 [4,] 0.19346280 -0.75002171 0.369625758 -0.30230464 0.13551075 [5,] 0.18834640 -1.12945359 0.130694295 -1.07247454 -0.05429767 [6,] 0.02924656 -1.01788093 0.461390203 0.36031101 0.01238754 [7,] -0.12221164 -1.39076305 0.150643316 -0.03181338 -0.30447428 [8,] -0.23285737 0.32958935 -0.316547521 0.31945087 -0.22579277 [9,] 0.16606791 0.68278684 0.309189980 0.52109723 0.39072742 [10,] 0.69442857 0.92277492 0.227365305 -1.48503657 0.75205459 [11,] 0.57463528 0.95871648 -0.056976772 -1.68620972 0.55352434 [12,] 0.49331397 -0.10146599 0.686594773 -0.27958285 0.61057089 [13,] -0.07632757 0.29937433 -0.181773495 0.03704768 -0.06882192 [14,] -0.11360981 -0.51224969 0.114888362 0.38193509 -0.13069679 [15,] -0.56817959 -0.12172580 -0.823661951 0.10110821 -0.76794384 [16,] -0.20182614 0.43859779 -0.280628108 0.35436136 -0.16645536 [17,] -0.21414272 1.91289545 -1.275085877 -1.04399593 -0.31059400 [18,] -0.75514058 -2.21876283 0.052323943 1.58503566 -0.93845329 [19,] 0.66808563 -0.08448455 0.853171564 -0.52657210 0.80366740 [20,] -0.07704752 -1.12985667 0.410140550 0.56668363 -0.10395289 [,45] [,46] [,47] [,48] [,49] [1,] 3.191799e-02 -0.029553866 0.08339981 -0.32409759 0.025292507 [2,] 9.906295e-02 -0.075837742 0.94201476 -2.47650776 0.125318763 [3,] -3.971138e-01 0.592059796 0.25563500 0.02326134 -0.193187693 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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 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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 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] 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 [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] 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 [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] 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 [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] 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 [,49] 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 1.37 0.29 1.65