Compare Diversity Measures
Usage
diversity.compare(
x,
group,
R = 1000,
base = exp(1),
na.omit = TRUE,
global.test = TRUE,
pairwise.test = TRUE,
bootstrap.ci = TRUE,
diversity.profile = TRUE,
p.adjust.method = c("bonferroni", "holm"),
ci.conf = 0.95,
ci.type = c("perc", "bca"),
q = seq(0, 3, 0.1),
parallel = c("no", "multicore", "snow"),
ncpus = 1L,
cl = NULL
)Arguments
- x
A factor vector of categories (e.g., species, traits). The frequency of each level is treated as the abundance of that category.
- group
A factor vector indicating the group of each observation. Must have the same length as
x.- R
Integer specifying the number of permutations. Default is 1000.
- base
The logarithm base to be used for computation of shannon family of diversity indices. Default is
exp(1).- na.omit
logical. If
TRUE, missing values (NA) are ignored and not included as a distinct factor level for computation. Default isTRUE.- global.test
logical. If
TRUEperforms the global permutation tests for the diversity measures. Default isTRUE.- pairwise.test
logical. If
TRUEperforms the pairwise permutation tests for the diversity measures. Default isTRUE.- bootstrap.ci
logical. If
TRUEcomputes the bootstrap confidence intervals for the diversity measures. Default isTRUE.- diversity.profile
logical. If
TRUEdiversity profiles. Default isTRUE.- p.adjust.method
(perm.test.pairwise only) Method for adjusting p-values for multiple comparisons. Options include
"bonferroni"and"holm". Default is"bonferroni".- ci.conf
Confidence level of the bootstrap interval. Default is 0.95.
- ci.type
A vector of character strings representing the type of intervals required. The options are
c("perc", "bca").- q
The order of the parametric index.
- parallel
The type of parallel operation to be used (if any). If missing, the default is taken from the option
"boot.parallel"(and if that is not set,"no").- ncpus
integer: number of processes to be used in parallel operation: typically one would chose this to the number of available CPUs.
- cl
An optional parallel or snow cluster for use if
parallel = "snow". If not supplied, a cluster on the local machine is created for the duration of thebootcall.
Value
A list with the following elements.
- Diversity Indices
A data frame of the different diversity indices computed for each group.
- Global Test
A data frame of results of global permutation test including the test statistic (weighted sum of squares between group summary indices) and the p value for the different diversity indices.
- Pairwise Test
A list of the following data frames.
- p-value
A data frame of p values for each between group comparison for different diversity measures.
- cld
A data frame of compact letter displays of significant differences among groups for different diversity measures.
- Bootstrap CIs
A data frame of lower and upper bootstrap confidence intervals computed for each group in different diversity measures.
- Diversity profiles
A list of data frames of Hill, Renyi and Tsallis diversity profiles computed for each group.
Examples
library(EvaluateCore)
pdata <- cassava_CC
qual <- c("CUAL", "LNGS", "PTLC", "DSTA", "LFRT", "LBTEF", "CBTR", "NMLB",
"ANGB", "CUAL9M", "LVC9M", "TNPR9M", "PL9M", "STRP", "STRC",
"PSTR")
# Convert qualitative data columns to factor
pdata[, qual] <- lapply(pdata[, qual], as.factor)
str(pdata)
#> 'data.frame': 168 obs. of 26 variables:
#> $ CUAL : Factor w/ 4 levels "Dark green","Green purple",..: 3 1 2 2 2 2 4 2 2 1 ...
#> $ LNGS : Factor w/ 3 levels "Long","Medium",..: 3 1 2 2 2 2 2 1 1 1 ...
#> $ PTLC : Factor w/ 5 levels "Dark green","Green purple",..: 3 4 4 4 4 5 4 2 2 5 ...
#> $ DSTA : Factor w/ 5 levels "Absent","Central part",..: 1 5 5 5 5 5 5 4 2 5 ...
#> $ LFRT : Factor w/ 4 levels "25-50% leaf retention",..: 1 1 1 1 3 2 2 2 2 2 ...
#> $ LBTEF : Factor w/ 6 levels "0","1","2","3",..: 3 1 2 1 4 5 4 4 3 2 ...
#> $ CBTR : Factor w/ 3 levels "Cream","White",..: 2 2 2 2 1 2 1 1 1 1 ...
#> $ NMLB : Factor w/ 9 levels "0","1","2","3",..: 3 1 2 1 4 4 4 3 3 4 ...
#> $ ANGB : Factor w/ 4 levels "150-300","450-600",..: 1 4 1 4 2 2 2 1 2 2 ...
#> $ CUAL9M: Factor w/ 5 levels "Dark green","Green",..: 1 1 3 5 3 3 5 5 5 4 ...
#> $ LVC9M : Factor w/ 5 levels "Dark green","Green",..: 4 3 3 3 3 1 3 1 4 3 ...
#> $ TNPR9M: Factor w/ 5 levels "1","2","3","4",..: 5 5 4 2 5 4 2 5 5 5 ...
#> $ PL9M : Factor w/ 2 levels "Long (25-30cm)",..: 2 2 1 1 1 1 1 1 2 2 ...
#> $ STRP : Factor w/ 4 levels "Absent","Intermediate",..: 2 3 1 1 1 1 4 1 1 4 ...
#> $ STRC : Factor w/ 2 levels "Absent","Present": 2 2 1 2 1 1 2 1 1 2 ...
#> $ PSTR : Factor w/ 2 levels "Irregular","Tending toward horizontal": 1 2 2 2 1 2 2 2 1 2 ...
#> $ NMSR : num 6 2 6 2 20 13 4 14 10 5 ...
#> $ TTRN : num 3 0.5 3 2 5 ...
#> $ TFWSR : num 1.4 2.6 1.2 1.6 5 7 4.2 2.8 2.8 4 ...
#> $ TTRW : num 0.7 0.65 0.6 1.6 1.25 ...
#> $ TFWSS : num 1 2.8 2.8 2.4 16 12 9 4.4 6.2 5 ...
#> $ TTSW : num 0.5 0.7 1.4 2.4 4 ...
#> $ TTPW : num 2.4 5.4 4 4 21 19 13.2 7.2 9 9 ...
#> $ AVPW : num 1.2 1.35 2 4 5.25 4.75 3.3 2.4 1.8 2.25 ...
#> $ ARSR : num 2 0 2 0 3 0 0 6 0 0 ...
#> $ SRDM : num 42 39.8 29.7 43 37.9 37 38.9 36.9 41 37.9 ...
diversity.compare(x = pdata$CUAL, group = pdata$LNGS, R = 100,
base = exp(1), na.omit = TRUE)
#> Computing diversity indices.
#> Performing global permutation tests.
#>
#> Performing pairwise permutation tests.
#> Computing bootstrap confidence intervals.
#> Generating diversity profiles.
#> $`Diversity Indices`
#> # A tibble: 4 × 30
#> group richness margalef_index menhinick_index berger_parker
#> <chr> <int> <dbl> <dbl> <dbl>
#> 1 Overall 4 0.585 0.309 0.530
#> 2 Long 3 0.468 0.354 0.514
#> 3 Medium 4 0.701 0.471 0.569
#> 4 Short 4 0.944 0.816 0.458
#> # ℹ 25 more variables: berger_parker_reciprocal <dbl>, simpson <dbl>,
#> # gini_simpson <dbl>, simpson_max <dbl>, simpson_reciprocal <dbl>,
#> # simpson_relative <dbl>, simpson_evenness <dbl>, shannon <dbl>,
#> # shannon_max <dbl>, shannon_relative <dbl>, shannon_ens <dbl>,
#> # heip_evenness <dbl>, mcintosh_diversity <dbl>, mcintosh_evenness <dbl>,
#> # smith_wilson <dbl>, brillouin_index <dbl>, renyi_entropy_0 <dbl>,
#> # renyi_entropy_1 <dbl>, renyi_entropy_2 <dbl>, tsallis_entropy_0 <dbl>, …
#>
#> $`Global Test`
#> Measure margalef_index menhinick_index berger_parker
#> 1 Test statistic 4.62557535 3.85787925 0.2539683
#> 2 p-value 0.08910891 0.06930693 0.6534653
#> berger_parker_reciprocal simpson gini_simpson simpson_max simpson_relative
#> 1 3.5485816 0.1459742 0.1459742 0.2857143 0.6397893
#> 2 0.5643564 0.5247525 0.4851485 0.0990099 0.2277228
#> shannon shannon_max shannon_relative shannon_ens heip_evenness
#> 1 2.1255498 7.0871092 1.15089028 55.1369748 10.2305896
#> 2 0.3069307 0.0990099 0.03960396 0.2673267 0.0990099
#> mcintosh_diversity mcintosh_evenness smith_wilson brillouin_index
#> 1 0.3514116 1.0809743 3.78072900 0.2103725
#> 2 0.2178218 0.1485149 0.00990099 0.8316832
#>
#> $`Pairwise Test`
#> $`Pairwise Test`$`p-value`
#> Comparison margalef_index menhinick_index berger_parker
#> 1 Long vs Medium 1.00000000 1.00000000 1
#> 2 Long vs Short 0.02970297 0.02970297 1
#> 3 Medium vs Short 1.00000000 1.00000000 1
#> berger_parker_reciprocal simpson gini_simpson simpson_max simpson_relative
#> 1 1 1.0000000 1.000000 1.0000000 1.0000000
#> 2 1 0.8613861 1.000000 0.8316832 1.0000000
#> 3 1 1.0000000 0.950495 1.0000000 0.8910891
#> shannon shannon_max shannon_relative shannon_ens heip_evenness
#> 1 1.0000000 1.0000000 0.9801980 1.0000000 1.0000000
#> 2 0.2970297 0.7128713 1.0000000 0.3267327 1.0000000
#> 3 0.6831683 1.0000000 0.4752475 0.5346535 0.5346535
#> mcintosh_diversity mcintosh_evenness smith_wilson brillouin_index
#> 1 1.0000000 0.7425743 1.0000000 1
#> 2 0.4455446 1.0000000 1.0000000 1
#> 3 0.3564356 0.6237624 0.1188119 1
#>
#> $`Pairwise Test`$cld
#> Group margalef_index menhinick_index berger_parker berger_parker_reciprocal
#> 1 Long a a a a
#> 2 Medium ab ab a a
#> 3 Short b b a a
#> simpson gini_simpson simpson_max simpson_relative shannon shannon_max
#> 1 a a a a a a
#> 2 a a a a a a
#> 3 a a a a a a
#> shannon_relative shannon_ens heip_evenness mcintosh_diversity
#> 1 a a a a
#> 2 a a a a
#> 3 a a a a
#> mcintosh_evenness smith_wilson brillouin_index
#> 1 a a a
#> 2 a a a
#> 3 a a a
#>
#>
#> $`Bootstrap CIs`
#> Group-CI margalef_index menhinick_index berger_parker
#> 1 Long: lower 0.4676540 0.3535534 0.4027778
#> 2 Long: upper 0.4676540 0.3535534 0.6388889
#> 3 Medium: lower 0.4676540 0.3535534 0.4451389
#> 4 Medium: upper 0.7014810 0.4714045 0.7010417
#> 5 Short: lower 0.6293160 0.6123724 0.3333333
#> 6 Short: upper 0.9439739 0.8164966 0.6666667
#> berger_parker_reciprocal simpson gini_simpson simpson_max simpson_relative
#> 1 1.619091 0.3573495 0.5207948 0.6666667 0.7727431
#> 2 2.400000 0.4783372 0.6525752 0.6666667 0.9800637
#> 3 1.471500 0.3587674 0.4611304 0.6666667 0.6932999
#> 4 2.284476 0.5274981 0.6519965 0.7500000 0.9377894
#> 5 1.500000 0.2673611 0.4514757 0.6666667 0.6968027
#> 6 3.000000 0.4874132 0.7395833 0.7500000 0.9861111
#> shannon shannon_max shannon_relative shannon_ens heip_evenness
#> 1 1.268182 1.584963 0.8026677 3.651240 1.2834996
#> 2 1.550426 1.584963 0.9822709 4.723640 1.8458488
#> 3 1.211598 1.584963 0.6511038 3.348900 0.9269083
#> 4 1.681684 2.000000 0.9404166 5.256166 1.7534524
#> 5 1.199287 1.584963 0.6862734 3.586009 0.9742734
#> 6 1.928964 2.000000 0.9862707 7.085143 2.0408130
#> mcintosh_diversity mcintosh_evenness smith_wilson brillouin_index
#> 1 0.3468894 0.7185593 0.5133413 0.8075800
#> 2 0.4663058 0.9768175 0.8094535 1.0112951
#> 3 0.3182851 0.6125709 0.3527128 0.7626342
#> 4 0.4622680 0.9136435 0.7572221 1.0709931
#> 5 0.3521915 0.6131844 0.4439441 0.7965172
#> 6 0.6067909 0.9754167 0.8313911 1.1432544
#>
#> $`Diversity profiles`
#> $`Diversity profiles`$hill
#> $`Diversity profiles`$hill$Long
#> q observed mean lower upper
#> 1 0.0 3.000000 3.000000 3.000000 3.000000
#> 2 0.1 2.970041 2.964507 2.913407 2.992622
#> 3 0.2 2.940750 2.930167 2.830548 2.985350
#> 4 0.3 2.912153 2.896998 2.751983 2.978185
#> 5 0.4 2.884272 2.865008 2.680495 2.971129
#> 6 0.5 2.857124 2.834195 2.614262 2.964182
#> 7 0.6 2.830721 2.804551 2.553018 2.957346
#> 8 0.7 2.805073 2.776061 2.496473 2.950622
#> 9 0.8 2.780186 2.748702 2.444321 2.944010
#> 10 0.9 2.756060 2.722450 2.396682 2.937511
#> 11 1.0 2.732695 2.697274 2.352870 2.931125
#> 12 1.1 2.710085 2.673144 2.312557 2.924853
#> 13 1.2 2.688224 2.650026 2.275449 2.918694
#> 14 1.3 2.667101 2.627884 2.241266 2.912648
#> 15 1.4 2.646704 2.606682 2.209749 2.906716
#> 16 1.5 2.627019 2.586383 2.180659 2.900895
#> 17 1.6 2.608031 2.566953 2.153775 2.895187
#> 18 1.7 2.589722 2.548353 2.128895 2.889590
#> 19 1.8 2.572075 2.530549 2.105837 2.884104
#> 20 1.9 2.555070 2.513506 2.079712 2.878727
#> 21 2.0 2.538688 2.497189 2.054650 2.873458
#> 22 2.1 2.522908 2.481565 2.031210 2.868297
#> 23 2.2 2.507711 2.466602 2.009283 2.863243
#> 24 2.3 2.493075 2.452270 1.988766 2.858293
#> 25 2.4 2.478981 2.438538 1.969563 2.853447
#> 26 2.5 2.465409 2.425378 1.951581 2.848703
#> 27 2.6 2.452337 2.412763 1.934733 2.844060
#> 28 2.7 2.439748 2.400666 1.918939 2.839516
#> 29 2.8 2.427621 2.389062 1.904123 2.835070
#> 30 2.9 2.415938 2.377927 1.890216 2.830720
#> 31 3.0 2.404680 2.367239 1.877151 2.826465
#>
#> $`Diversity profiles`$hill$Medium
#> q observed mean lower upper
#> 1 0.0 4.000000 3.720000 3.000000 4.000000
#> 2 0.1 3.775167 3.554848 2.917060 3.878659
#> 3 0.2 3.586424 3.411515 2.838319 3.765974
#> 4 0.3 3.427791 3.286906 2.763841 3.661485
#> 5 0.4 3.293906 3.178177 2.693637 3.564688
#> 6 0.5 3.180150 3.082815 2.627668 3.475057
#> 7 0.6 3.082659 2.998661 2.565856 3.392056
#> 8 0.7 2.998274 2.923904 2.508087 3.315162
#> 9 0.8 2.924454 2.857039 2.454214 3.248289
#> 10 0.9 2.859178 2.796834 2.400441 3.186299
#> 11 1.0 2.800853 2.742283 2.348804 3.126733
#> 12 1.1 2.748230 2.692567 2.300256 3.063674
#> 13 1.2 2.700330 2.647019 2.252135 3.014408
#> 14 1.3 2.656390 2.605094 2.200321 2.976618
#> 15 1.4 2.615811 2.566344 2.152981 2.943430
#> 16 1.5 2.578125 2.530400 2.109680 2.922474
#> 17 1.6 2.542961 2.496955 2.070033 2.903089
#> 18 1.7 2.510025 2.465752 2.033698 2.884995
#> 19 1.8 2.479080 2.436571 2.000366 2.867969
#> 20 1.9 2.449934 2.409227 1.969761 2.851665
#> 21 2.0 2.422430 2.383558 1.941635 2.833472
#> 22 2.1 2.396434 2.359423 1.915760 2.816125
#> 23 2.2 2.371835 2.336700 1.891934 2.799526
#> 24 2.3 2.348536 2.315280 1.869970 2.783596
#> 25 2.4 2.326451 2.295065 1.849702 2.768270
#> 26 2.5 2.305505 2.275967 1.830976 2.753492
#> 27 2.6 2.285629 2.257907 1.813656 2.739219
#> 28 2.7 2.266761 2.240814 1.797617 2.725413
#> 29 2.8 2.248843 2.224623 1.782746 2.712043
#> 30 2.9 2.231821 2.209274 1.768940 2.699083
#> 31 3.0 2.215647 2.194711 1.756108 2.686511
#>
#> $`Diversity profiles`$hill$Short
#> q observed mean lower upper
#> 1 0.0 4.000000 3.920000 3.000000 4.000000
#> 2 0.1 3.933810 3.834336 2.921506 3.988856
#> 3 0.2 3.870196 3.753673 2.846484 3.977595
#> 4 0.3 3.809136 3.677884 2.775035 3.966257
#> 5 0.4 3.750595 3.606797 2.707211 3.955115
#> 6 0.5 3.694521 3.540210 2.643022 3.944035
#> 7 0.6 3.640856 3.477895 2.582436 3.933020
#> 8 0.7 3.589527 3.419614 2.525386 3.922074
#> 9 0.8 3.540459 3.365120 2.471777 3.911201
#> 10 0.9 3.493567 3.314170 2.421490 3.900405
#> 11 1.0 3.448767 3.266524 2.351508 3.889690
#> 12 1.1 3.405970 3.221950 2.282823 3.879059
#> 13 1.2 3.365087 3.180229 2.220895 3.868516
#> 14 1.3 3.326031 3.141152 2.165081 3.858063
#> 15 1.4 3.288713 3.104524 2.114774 3.847703
#> 16 1.5 3.253050 3.070164 2.070403 3.837438
#> 17 1.6 3.218958 3.037901 2.030373 3.827273
#> 18 1.7 3.186359 3.007579 1.994125 3.817208
#> 19 1.8 3.155176 2.979054 1.961252 3.807245
#> 20 1.9 3.125338 2.952193 1.931386 3.797388
#> 21 2.0 3.096774 2.926872 1.904202 3.787636
#> 22 2.1 3.069420 2.902980 1.879410 3.777993
#> 23 2.2 3.043214 2.880412 1.856755 3.768459
#> 24 2.3 3.018098 2.859073 1.836008 3.759036
#> 25 2.4 2.994016 2.838875 1.816971 3.749724
#> 26 2.5 2.970917 2.819739 1.799467 3.740525
#> 27 2.6 2.948751 2.801590 1.783340 3.731439
#> 28 2.7 2.927474 2.784361 1.768452 3.722468
#> 29 2.8 2.907041 2.767989 1.754681 3.713611
#> 30 2.9 2.887412 2.752418 1.741920 3.704868
#> 31 3.0 2.868549 2.737593 1.730072 3.696241
#>
#> attr(,"R")
#> [1] 100
#> attr(,"conf")
#> [1] 0.95
#> attr(,"parameter")
#> [1] "hill"
#> attr(,"ci.type")
#> [1] "perc"
#>
#> $`Diversity profiles`$renyi
#> $`Diversity profiles`$renyi$Long
#> q observed mean lower upper
#> 1 0.0 1.0986123 1.0986123 1.0986123 1.098612
#> 2 0.1 1.0885756 1.0867065 1.0716769 1.096389
#> 3 0.2 1.0786646 1.0749957 1.0450036 1.094156
#> 4 0.3 1.0688928 1.0635027 1.0187053 1.091913
#> 5 0.4 1.0592726 1.0522473 0.9928892 1.089662
#> 6 0.5 1.0498155 1.0412470 0.9676551 1.087404
#> 7 0.6 1.0405315 1.0305164 0.9430928 1.085140
#> 8 0.7 1.0314297 1.0200672 0.9192806 1.082870
#> 9 0.8 1.0225177 1.0099086 0.8962842 1.080596
#> 10 0.9 1.0138021 1.0000474 0.8741559 1.078319
#> 11 1.0 1.0052882 0.9904876 0.8529346 1.076038
#> 12 1.1 0.9969801 0.9812315 0.8326461 1.073757
#> 13 1.2 0.9888807 0.9722792 0.8135935 1.071474
#> 14 1.3 0.9809920 0.9636289 0.7960083 1.069192
#> 15 1.4 0.9733151 0.9552777 0.7793783 1.066912
#> 16 1.5 0.9658499 0.9472210 0.7636711 1.064634
#> 17 1.6 0.9585956 0.9394532 0.7488499 1.062359
#> 18 1.7 0.9515507 0.9319679 0.7348751 1.060089
#> 19 1.8 0.9447130 0.9247578 0.7217054 1.057824
#> 20 1.9 0.9380796 0.9178153 0.7092988 1.055565
#> 21 2.0 0.9316472 0.9111322 0.6976128 1.053313
#> 22 2.1 0.9254122 0.9046999 0.6855324 1.051311
#> 23 2.2 0.9193702 0.8985098 0.6741376 1.049335
#> 24 2.3 0.9135169 0.8925532 0.6633944 1.047385
#> 25 2.4 0.9078477 0.8868214 0.6532689 1.045462
#> 26 2.5 0.9023576 0.8813057 0.6437270 1.043564
#> 27 2.6 0.8970416 0.8759974 0.6347355 1.041693
#> 28 2.7 0.8918947 0.8708883 0.6262620 1.039846
#> 29 2.8 0.8869118 0.8659701 0.6182754 1.038026
#> 30 2.9 0.8820876 0.8612349 0.6107455 1.036231
#> 31 3.0 0.8774170 0.8566748 0.6036439 1.034460
#>
#> $`Diversity profiles`$renyi$Medium
#> q observed mean lower upper
#> 1 0.0 1.3862944 1.2712215 1.0986123 1.386294
#> 2 0.1 1.3284446 1.2354654 1.0754906 1.356920
#> 3 0.2 1.2771555 1.2023144 1.0524286 1.328999
#> 4 0.3 1.2319160 1.1716614 1.0295155 1.302535
#> 5 0.4 1.1920742 1.1433397 1.0068396 1.277477
#> 6 0.5 1.1569284 1.1171510 0.9844864 1.253727
#> 7 0.6 1.1257925 1.0928886 0.9625369 1.231286
#> 8 0.7 1.0980367 1.0703514 0.9410657 1.210094
#> 9 0.8 1.0731077 1.0493541 0.9201397 1.190085
#> 10 0.9 1.0505342 1.0297308 0.8998173 1.171190
#> 11 1.0 1.0299241 1.0113363 0.8801472 1.153339
#> 12 1.1 1.0109570 0.9940456 0.8611687 1.136461
#> 13 1.2 0.9933740 0.9777519 0.8398910 1.121734
#> 14 1.3 0.9769680 0.9623642 0.8183980 1.109621
#> 15 1.4 0.9615743 0.9478051 0.7982844 1.098325
#> 16 1.5 0.9470625 0.9340086 0.7796244 1.090874
#> 17 1.6 0.9333293 0.9209177 0.7621082 1.084958
#> 18 1.7 0.9202928 0.9084831 0.7456683 1.077946
#> 19 1.8 0.9078876 0.8966617 0.7302405 1.067209
#> 20 1.9 0.8960613 0.8854153 0.7157642 1.057666
#> 21 2.0 0.8847711 0.8747096 0.7021816 1.051146
#> 22 2.1 0.8739819 0.8645137 0.6894378 1.044707
#> 23 2.2 0.8636641 0.8547994 0.6774802 1.038548
#> 24 2.3 0.8537922 0.8455406 0.6662590 1.032869
#> 25 2.4 0.8443440 0.8367132 0.6557266 1.028852
#> 26 2.5 0.8352998 0.8282945 0.6458380 1.024978
#> 27 2.6 0.8266413 0.8202633 0.6365508 1.021234
#> 28 2.7 0.8183519 0.8125996 0.6278246 1.017608
#> 29 2.8 0.8104158 0.8052847 0.6196218 1.014093
#> 30 2.9 0.8028180 0.7983005 0.6119070 1.010680
#> 31 3.0 0.7955444 0.7916303 0.6046468 1.007362
#>
#> $`Diversity profiles`$renyi$Short
#> q observed mean lower upper
#> 1 0.0 1.386294 1.3517725 1.0986123 1.386294
#> 2 0.1 1.369609 1.3302319 1.0745579 1.380347
#> 3 0.2 1.353305 1.3093484 1.0503188 1.374583
#> 4 0.3 1.337402 1.2891692 1.0260126 1.369001
#> 5 0.4 1.321914 1.2697285 1.0017614 1.363601
#> 6 0.5 1.306851 1.2510482 0.9776878 1.358383
#> 7 0.6 1.292219 1.2331396 0.9539118 1.353343
#> 8 0.7 1.278021 1.2160045 0.9305471 1.348480
#> 9 0.8 1.264256 1.1996371 0.9076982 1.343791
#> 10 0.9 1.250923 1.1840250 0.8854578 1.339272
#> 11 1.0 1.238017 1.1691507 0.8639052 1.334920
#> 12 1.1 1.225530 1.1549928 0.8431051 1.330730
#> 13 1.2 1.213454 1.1415269 0.8165183 1.326652
#> 14 1.3 1.201780 1.1287264 0.7909496 1.322228
#> 15 1.4 1.190496 1.1165637 0.7671094 1.317886
#> 16 1.5 1.179593 1.1050098 0.7449247 1.313625
#> 17 1.6 1.169058 1.0940359 0.7243118 1.309446
#> 18 1.7 1.158879 1.0836130 0.7051809 1.305345
#> 19 1.8 1.149044 1.0737128 0.6874391 1.301324
#> 20 1.9 1.139542 1.0643073 0.6709929 1.297380
#> 21 2.0 1.130361 1.0553696 0.6557503 1.293513
#> 22 2.1 1.121489 1.0468738 0.6416223 1.290362
#> 23 2.2 1.112914 1.0387949 0.6285237 1.287322
#> 24 2.3 1.104627 1.0311092 0.6163742 1.284387
#> 25 2.4 1.096616 1.0237942 0.6050982 1.281554
#> 26 2.5 1.088871 1.0168283 0.5946253 1.278818
#> 27 2.6 1.081382 1.0101915 0.5848902 1.276176
#> 28 2.7 1.074140 1.0038644 0.5758327 1.273622
#> 29 2.8 1.067136 0.9978293 0.5673973 1.271154
#> 30 2.9 1.060361 0.9920691 0.5595332 1.268767
#> 31 3.0 1.053806 0.9865680 0.5521937 1.266458
#>
#> attr(,"R")
#> [1] 100
#> attr(,"conf")
#> [1] 0.95
#> attr(,"parameter")
#> [1] "renyi"
#> attr(,"ci.type")
#> [1] "perc"
#>
#> $`Diversity profiles`$tsallis
#> $`Diversity profiles`$tsallis$Long
#> q observed mean lower upper
#> 1 0.0 2.0000000 2.0000000 2.0000000 2.0000000
#> 2 0.1 1.8485612 1.8430441 1.7910575 1.8700431
#> 3 0.2 1.7126236 1.7031448 1.6149316 1.7506878
#> 4 0.3 1.5903508 1.5781030 1.4655903 1.6409685
#> 5 0.4 1.4801432 1.4660366 1.3351151 1.5400141
#> 6 0.5 1.3806058 1.3653308 1.2219832 1.4470380
#> 7 0.6 1.2905205 1.2745974 1.1235738 1.3613299
#> 8 0.7 1.2088223 1.1926393 1.0375969 1.2822476
#> 9 0.8 1.1345790 1.1184225 0.9621552 1.2092105
#> 10 0.9 1.0669734 1.0510515 0.8958623 1.1416928
#> 11 1.0 1.0052882 0.9897487 0.8372554 1.0792184
#> 12 1.1 0.9488928 0.9338379 0.7851750 1.0213560
#> 13 1.2 0.8972324 0.8827291 0.7386987 0.9677144
#> 14 1.3 0.8498176 0.8359070 0.6970527 0.9179385
#> 15 1.4 0.8062167 0.7929199 0.6595853 0.8717060
#> 16 1.5 0.7660477 0.7533715 0.6257467 0.8287239
#> 17 1.6 0.7289728 0.7169132 0.5950708 0.7887259
#> 18 1.7 0.6946920 0.6832376 0.5671616 0.7514697
#> 19 1.8 0.6629392 0.6520730 0.5416815 0.7167346
#> 20 1.9 0.6334774 0.6231791 0.5183417 0.6843196
#> 21 2.0 0.6060957 0.5963426 0.4968943 0.6540413
#> 22 2.1 0.5806057 0.5713739 0.4771259 0.6257325
#> 23 2.2 0.5568392 0.5481041 0.4588522 0.5992404
#> 24 2.3 0.5346455 0.5263826 0.4419134 0.5744256
#> 25 2.4 0.5138895 0.5060746 0.4262648 0.5511604
#> 26 2.5 0.4944499 0.4870595 0.4120504 0.5293282
#> 27 2.6 0.4762176 0.4692288 0.3987271 0.5088220
#> 28 2.7 0.4590941 0.4524852 0.3862123 0.4895440
#> 29 2.8 0.4429909 0.4367409 0.3744335 0.4714044
#> 30 2.9 0.4278278 0.4219168 0.3633266 0.4543207
#> 31 3.0 0.4135320 0.4079413 0.3528350 0.4382174
#>
#> $`Diversity profiles`$tsallis$Medium
#> q observed mean lower upper
#> 1 0.0 3.0000000 2.6200000 2.0000000 3.0000000
#> 2 0.1 2.5617123 2.3001691 1.8070878 2.6641696
#> 3 0.2 2.2224763 2.0400464 1.6383924 2.3820368
#> 4 0.3 1.9552854 1.8257399 1.4905924 2.1432938
#> 5 0.4 1.7411443 1.6470172 1.3608427 1.9398361
#> 6 0.5 1.5665950 1.4962700 1.2467044 1.7652446
#> 7 0.6 1.4220235 1.3677850 1.1460846 1.6144161
#> 8 0.7 1.3004971 1.2572269 1.0571861 1.4810648
#> 9 0.8 1.1969637 1.1612703 0.9784642 1.3638935
#> 10 0.9 1.1076994 1.0773365 0.9085899 1.2608095
#> 11 1.0 1.0299241 1.0034035 0.8464190 1.1697039
#> 12 1.1 0.9615347 0.9378685 0.7883323 1.0888291
#> 13 1.2 0.9009177 0.8794473 0.7342359 1.0167313
#> 14 1.3 0.8468176 0.8271002 0.6866586 0.9527941
#> 15 1.4 0.7982434 0.7799771 0.6446108 0.8954410
#> 16 1.5 0.7544018 0.7373768 0.6072742 0.8436423
#> 17 1.6 0.7146493 0.6987154 0.5739693 0.7966894
#> 18 1.7 0.6784574 0.6635031 0.5441299 0.7539833
#> 19 1.8 0.6453869 0.6313260 0.5172818 0.7151080
#> 20 1.9 0.6150691 0.6018316 0.4930261 0.6798456
#> 21 2.0 0.5871914 0.5747184 0.4710262 0.6474248
#> 22 2.1 0.5614865 0.5497265 0.4509970 0.6175388
#> 23 2.2 0.5377246 0.5266314 0.4326958 0.5904820
#> 24 2.3 0.5157061 0.5052374 0.4159152 0.5653574
#> 25 2.4 0.4952573 0.4853739 0.4004778 0.5419253
#> 26 2.5 0.4762260 0.4668914 0.3862306 0.5200429
#> 27 2.6 0.4584783 0.4496582 0.3730419 0.4996052
#> 28 2.7 0.4418959 0.4335582 0.3607975 0.4804871
#> 29 2.8 0.4263738 0.4184885 0.3493982 0.4625766
#> 30 2.9 0.4118190 0.4043577 0.3387576 0.4457736
#> 31 3.0 0.3981481 0.3910846 0.3288002 0.4299877
#>
#> $`Diversity profiles`$tsallis$Short
#> q observed mean lower upper
#> 1 0.0 3.0000000 2.8800000 2.0000000 3.0000000
#> 2 0.1 2.7003331 2.5734826 1.8147007 2.7421172
#> 3 0.2 2.4405950 2.3124385 1.6541673 2.5116032
#> 4 0.3 2.2146076 2.0888371 1.5145195 2.3052466
#> 5 0.4 2.0172417 1.8962276 1.3925377 2.1202369
#> 6 0.5 1.8442276 1.7294050 1.2855441 1.9541124
#> 7 0.6 1.6920012 1.5841507 1.1913073 1.8047151
#> 8 0.7 1.5575795 1.4570295 1.1081597 1.6701513
#> 9 0.8 1.4384586 1.3452315 1.0343970 1.5487575
#> 10 0.9 1.3325309 1.2464472 0.9698694 1.4390715
#> 11 1.0 1.2380168 1.1587697 0.9129924 1.3398061
#> 12 1.1 1.1534096 1.0806170 0.8619297 1.2498275
#> 13 1.2 1.0774297 1.0106708 0.8158643 1.1681359
#> 14 1.3 1.0089869 0.9478270 0.7741143 1.0938485
#> 15 1.4 0.9471496 0.8911572 0.7352384 1.0261854
#> 16 1.5 0.8911198 0.8398767 0.6960151 0.9644565
#> 17 1.6 0.8402110 0.7933200 0.6608967 0.9080507
#> 18 1.7 0.7938317 0.7509197 0.6290807 0.8564263
#> 19 1.8 0.7514702 0.7121901 0.5992307 0.8091025
#> 20 1.9 0.7126828 0.6767142 0.5701165 0.7656519
#> 21 2.0 0.6770833 0.6441319 0.5436632 0.7256944
#> 22 2.1 0.6443352 0.6141317 0.5195342 0.6888917
#> 23 2.2 0.6141439 0.5864426 0.4974444 0.6549420
#> 24 2.3 0.5862509 0.5608282 0.4771513 0.6235757
#> 25 2.4 0.5604291 0.5370818 0.4584475 0.5945523
#> 26 2.5 0.5364780 0.5150217 0.4411551 0.5676562
#> 27 2.6 0.5142206 0.4944880 0.4251208 0.5426946
#> 28 2.7 0.4934997 0.4753392 0.4102120 0.5194943
#> 29 2.8 0.4741760 0.4574500 0.3963135 0.4979000
#> 30 2.9 0.4561251 0.4407092 0.3833251 0.4777719
#> 31 3.0 0.4392361 0.4250174 0.3711589 0.4589844
#>
#> attr(,"R")
#> [1] 100
#> attr(,"conf")
#> [1] 0.95
#> attr(,"parameter")
#> [1] "tsallis"
#> attr(,"ci.type")
#> [1] "perc"
#>
#>
diversity.compare(x = pdata$ANGB, group = pdata$LNGS, R = 100,
base = exp(1), na.omit = TRUE)
#> Computing diversity indices.
#> Performing global permutation tests.
#>
#> Performing pairwise permutation tests.
#> Computing bootstrap confidence intervals.
#> Generating diversity profiles.
#> $`Diversity Indices`
#> # A tibble: 4 × 30
#> group richness margalef_index menhinick_index berger_parker
#> <chr> <int> <dbl> <dbl> <dbl>
#> 1 Overall 4 0.585 0.309 0.452
#> 2 Long 4 0.701 0.471 0.514
#> 3 Medium 4 0.701 0.471 0.444
#> 4 Short 3 0.629 0.612 0.458
#> # ℹ 25 more variables: berger_parker_reciprocal <dbl>, simpson <dbl>,
#> # gini_simpson <dbl>, simpson_max <dbl>, simpson_reciprocal <dbl>,
#> # simpson_relative <dbl>, simpson_evenness <dbl>, shannon <dbl>,
#> # shannon_max <dbl>, shannon_relative <dbl>, shannon_ens <dbl>,
#> # heip_evenness <dbl>, mcintosh_diversity <dbl>, mcintosh_evenness <dbl>,
#> # smith_wilson <dbl>, brillouin_index <dbl>, renyi_entropy_0 <dbl>,
#> # renyi_entropy_1 <dbl>, renyi_entropy_2 <dbl>, tsallis_entropy_0 <dbl>, …
#>
#> $`Global Test`
#> Measure margalef_index menhinick_index berger_parker
#> 1 Test statistic 0.1071317 0.4087945 0.1825397
#> 2 p-value 1.0000000 1.0000000 0.5643564
#> berger_parker_reciprocal simpson gini_simpson simpson_max simpson_relative
#> 1 3.4727768 0.03859249 0.03859249 0.1428571 0.2681430
#> 2 0.6633663 0.76237624 0.65346535 0.2574257 0.2574257
#> shannon shannon_max shannon_relative shannon_ens heip_evenness
#> 1 0.6662977 3.5435546 0.3615327 17.6011822 3.0824569
#> 2 0.7029703 0.3168317 0.3069307 0.6534653 0.3663366
#> mcintosh_diversity mcintosh_evenness smith_wilson brillouin_index
#> 1 0.07321862 0.4906392 1.6496292 0.6374954
#> 2 0.63366337 0.2277228 0.1386139 0.4851485
#>
#> $`Pairwise Test`
#> $`Pairwise Test`$`p-value`
#> Comparison margalef_index menhinick_index berger_parker
#> 1 Long vs Medium 1 1 1
#> 2 Long vs Short 1 1 1
#> 3 Medium vs Short 1 1 1
#> berger_parker_reciprocal simpson gini_simpson simpson_max simpson_relative
#> 1 1 1 1 1.0000000 1.0000000
#> 2 1 1 1 1.0000000 0.5346535
#> 3 1 1 1 0.8613861 0.5940594
#> shannon shannon_max shannon_relative shannon_ens heip_evenness
#> 1 1 1.0000000 1.0000000 1 1.0000000
#> 2 1 0.9207921 0.2079208 1 0.5643564
#> 3 1 0.9801980 0.6534653 1 0.8019802
#> mcintosh_diversity mcintosh_evenness smith_wilson brillouin_index
#> 1 1 1.0000000 1.0000000 1.0000000
#> 2 1 0.4158416 0.1485149 1.0000000
#> 3 1 0.5643564 0.3861386 0.9207921
#>
#> $`Pairwise Test`$cld
#> Group margalef_index menhinick_index berger_parker berger_parker_reciprocal
#> 1 Long a a a a
#> 2 Medium a a a a
#> 3 Short a a a a
#> simpson gini_simpson simpson_max simpson_relative shannon shannon_max
#> 1 a a a a a a
#> 2 a a a a a a
#> 3 a a a a a a
#> shannon_relative shannon_ens heip_evenness mcintosh_diversity
#> 1 a a a a
#> 2 a a a a
#> 3 a a a a
#> mcintosh_evenness smith_wilson brillouin_index
#> 1 a a a
#> 2 a a a
#> 3 a a a
#>
#>
#> $`Bootstrap CIs`
#> Group-CI margalef_index menhinick_index berger_parker
#> 1 Long: lower 0.467654 0.4714045 0.4034722
#> 2 Long: upper 0.701481 0.4714045 0.6170139
#> 3 Medium: lower 0.467654 0.4154252 0.3611111
#> 4 Medium: upper 0.701481 0.4714045 0.5687500
#> 5 Short: lower 0.629316 0.6123724 0.3750000
#> 6 Short: upper 0.629316 0.6123724 0.6447917
#> berger_parker_reciprocal simpson gini_simpson simpson_max simpson_relative
#> 1 1.516755 0.3092882 0.5582658 0.7500000 0.7493699
#> 2 2.482759 0.4516590 0.6944252 0.7500000 0.9397377
#> 3 1.783516 0.2961323 0.5848476 0.7500000 0.7832176
#> 4 2.821846 0.4153935 0.7056327 0.7500000 0.9402392
#> 5 1.411765 0.3368056 0.4548611 0.6666667 0.7069010
#> 6 2.666667 0.4618056 0.6648437 0.6666667 0.9972656
#> shannon shannon_max shannon_relative shannon_ens heip_evenness
#> 1 1.398932 2.000000 0.6846781 3.849376 0.9445944
#> 2 1.829966 2.000000 0.9186726 6.089423 1.6918448
#> 3 1.463025 2.000000 0.7325934 4.475436 1.1927354
#> 4 1.870477 2.000000 0.9164158 6.326070 1.8351325
#> 5 1.207261 1.584963 0.7816209 3.320279 1.1066169
#> 6 1.584963 1.584963 0.9975045 4.879108 1.9212443
#> mcintosh_diversity mcintosh_evenness smith_wilson brillouin_index
#> 1 0.3676601 0.6350560 0.3494951 0.8977944
#> 2 0.5069574 0.8860126 0.6109161 1.1644251
#> 3 0.4156116 0.7131378 0.3557487 0.9646353
#> 4 0.5215998 0.9381250 0.6856812 1.1923234
#> 5 0.3477589 0.6548518 0.4945224 0.6049201
#> 6 0.5290714 0.9929037 0.9203697 0.9522141
#>
#> $`Diversity profiles`
#> $`Diversity profiles`$hill
#> $`Diversity profiles`$hill$Long
#> q observed mean lower upper
#> 1 0.0 4.000000 3.970000 3.000000 4.000000
#> 2 0.1 3.886394 3.844652 2.957442 3.937434
#> 3 0.2 3.779642 3.729528 2.915691 3.876881
#> 4 0.3 3.679590 3.623901 2.874819 3.818355
#> 5 0.4 3.586026 3.527025 2.834893 3.761858
#> 6 0.5 3.498684 3.438169 2.795973 3.708884
#> 7 0.6 3.417265 3.356631 2.758112 3.660657
#> 8 0.7 3.341444 3.281752 2.721355 3.615361
#> 9 0.8 3.270881 3.212920 2.685739 3.572695
#> 10 0.9 3.205233 3.149575 2.651292 3.532420
#> 11 1.0 3.144158 3.091204 2.618037 3.491691
#> 12 1.1 3.087324 3.037343 2.585987 3.451114
#> 13 1.2 3.034412 2.987573 2.535192 3.412174
#> 14 1.3 2.985118 2.941514 2.487798 3.374805
#> 15 1.4 2.939157 2.898824 2.444774 3.338766
#> 16 1.5 2.896264 2.859196 2.396583 3.307408
#> 17 1.6 2.856191 2.822354 2.349466 3.281465
#> 18 1.7 2.818711 2.788050 2.309700 3.256929
#> 19 1.8 2.783616 2.756059 2.269781 3.233741
#> 20 1.9 2.750715 2.726183 2.230360 3.211791
#> 21 2.0 2.719832 2.698238 2.196904 3.190979
#> 22 2.1 2.690809 2.672064 2.166077 3.171217
#> 23 2.2 2.663500 2.647513 2.137630 3.152423
#> 24 2.3 2.637774 2.624452 2.111339 3.134524
#> 25 2.4 2.613511 2.602763 2.087007 3.117454
#> 26 2.5 2.590601 2.582338 2.064453 3.101154
#> 27 2.6 2.568944 2.563078 2.043518 3.085569
#> 28 2.7 2.548450 2.544894 2.024058 3.075441
#> 29 2.8 2.529037 2.527708 2.005945 3.066514
#> 30 2.9 2.510629 2.511444 1.989061 3.058039
#> 31 3.0 2.493156 2.496038 1.973303 3.049975
#>
#> $`Diversity profiles`$hill$Medium
#> q observed mean lower upper
#> 1 0.0 4.000000 3.990000 4.000000 4.000000
#> 2 0.1 3.902761 3.881627 3.770824 3.961054
#> 3 0.2 3.812649 3.782888 3.577933 3.922901
#> 4 0.3 3.729325 3.692978 3.415458 3.885509
#> 5 0.4 3.652407 3.611096 3.278109 3.848908
#> 6 0.5 3.581486 3.536467 3.161307 3.813117
#> 7 0.6 3.516139 3.468366 3.061207 3.778147
#> 8 0.7 3.455945 3.406127 2.979496 3.744009
#> 9 0.8 3.400487 3.349142 2.927379 3.710709
#> 10 0.9 3.349367 3.296865 2.879631 3.678250
#> 11 1.0 3.302206 3.248808 2.835741 3.646633
#> 12 1.1 3.258648 3.204534 2.796370 3.616290
#> 13 1.2 3.218365 3.163658 2.749966 3.591454
#> 14 1.3 3.181053 3.125836 2.696542 3.567923
#> 15 1.4 3.146434 3.090765 2.645859 3.545617
#> 16 1.5 3.114256 3.058176 2.595767 3.524463
#> 17 1.6 3.084291 3.027829 2.549447 3.504388
#> 18 1.7 3.056333 2.999515 2.506574 3.485325
#> 19 1.8 3.030197 2.973046 2.466851 3.466950
#> 20 1.9 3.005715 2.948254 2.430006 3.446189
#> 21 2.0 2.982739 2.924992 2.395793 3.424042
#> 22 2.1 2.961134 2.903126 2.363989 3.402281
#> 23 2.2 2.940781 2.882540 2.334389 3.381138
#> 24 2.3 2.921573 2.863128 2.306810 3.360600
#> 25 2.4 2.903412 2.844794 2.281142 3.340654
#> 26 2.5 2.886214 2.827453 2.257187 3.321290
#> 27 2.6 2.869900 2.811029 2.234779 3.303679
#> 28 2.7 2.854401 2.795453 2.213795 3.288901
#> 29 2.8 2.839655 2.780662 2.194123 3.274655
#> 30 2.9 2.825604 2.766599 2.175661 3.260914
#> 31 3.0 2.812200 2.753214 2.158318 3.247653
#>
#> $`Diversity profiles`$hill$Short
#> q observed mean lower upper
#> 1 0.0 3.000000 3.000000 3.000000 3.000000
#> 2 0.1 2.989707 2.979641 2.941363 2.998426
#> 3 0.2 2.979386 2.959673 2.884567 2.996855
#> 4 0.3 2.969042 2.940115 2.829455 2.995285
#> 5 0.4 2.958682 2.920985 2.775177 2.993718
#> 6 0.5 2.948312 2.902295 2.722420 2.992154
#> 7 0.6 2.937939 2.884057 2.671301 2.990593
#> 8 0.7 2.927570 2.866281 2.621919 2.989034
#> 9 0.8 2.917212 2.848971 2.574355 2.987478
#> 10 0.9 2.906871 2.832133 2.528669 2.985925
#> 11 1.0 2.896553 2.815769 2.484900 2.984375
#> 12 1.1 2.886267 2.799877 2.443071 2.982828
#> 13 1.2 2.876017 2.784456 2.403187 2.981285
#> 14 1.3 2.865812 2.769503 2.365235 2.979745
#> 15 1.4 2.855656 2.755013 2.329338 2.978209
#> 16 1.5 2.845557 2.740978 2.299143 2.976676
#> 17 1.6 2.835521 2.727392 2.270752 2.975148
#> 18 1.7 2.825553 2.714246 2.244062 2.973623
#> 19 1.8 2.815659 2.701531 2.218971 2.972102
#> 20 1.9 2.805845 2.689236 2.192123 2.970585
#> 21 2.0 2.796117 2.677351 2.165414 2.969072
#> 22 2.1 2.786478 2.665865 2.140197 2.967564
#> 23 2.2 2.776935 2.654766 2.116408 2.966060
#> 24 2.3 2.767491 2.644043 2.093980 2.964560
#> 25 2.4 2.758150 2.633684 2.072846 2.963065
#> 26 2.5 2.748918 2.623677 2.052936 2.961575
#> 27 2.6 2.739797 2.614010 2.034185 2.960089
#> 28 2.7 2.730792 2.604671 2.016525 2.958608
#> 29 2.8 2.721904 2.595650 1.999892 2.957132
#> 30 2.9 2.713137 2.586933 1.984225 2.955662
#> 31 3.0 2.704494 2.578511 1.969464 2.954196
#>
#> attr(,"R")
#> [1] 100
#> attr(,"conf")
#> [1] 0.95
#> attr(,"parameter")
#> [1] "hill"
#> attr(,"ci.type")
#> [1] "perc"
#>
#> $`Diversity profiles`$renyi
#> $`Diversity profiles`$renyi$Long
#> q observed mean lower upper
#> 1 0.0 1.3862944 1.3805407 1.2496454 1.386294
#> 2 0.1 1.3574818 1.3467986 1.2037093 1.371691
#> 3 0.2 1.3296293 1.3146439 1.1613260 1.357354
#> 4 0.3 1.3028014 1.2841134 1.1225260 1.343300
#> 5 0.4 1.2770445 1.2552064 1.0872061 1.329545
#> 6 0.5 1.2523869 1.2278942 1.0551672 1.316100
#> 7 0.6 1.2288406 1.2021287 1.0261530 1.302977
#> 8 0.7 1.2064030 1.1778494 0.9998812 1.290182
#> 9 0.8 1.1850594 1.1549878 0.9760672 1.277720
#> 10 0.9 1.1647848 1.1334715 0.9545664 1.265595
#> 11 1.0 1.1455462 1.1132264 0.9403713 1.253808
#> 12 1.1 1.1273048 1.0941786 0.9226953 1.242359
#> 13 1.2 1.1100177 1.0762557 0.9051133 1.231246
#> 14 1.3 1.0936393 1.0593875 0.8886494 1.220465
#> 15 1.4 1.0781229 1.0435068 0.8732091 1.210012
#> 16 1.5 1.0634215 1.0285493 0.8538808 1.199883
#> 17 1.6 1.0494889 1.0144543 0.8333266 1.190070
#> 18 1.7 1.0362798 1.0011645 0.8148084 1.180567
#> 19 1.8 1.0237510 0.9886261 0.7984615 1.171367
#> 20 1.9 1.0118608 0.9767886 0.7832548 1.162462
#> 21 2.0 1.0005702 0.9656050 0.7690984 1.153844
#> 22 2.1 0.9898419 0.9550314 0.7559092 1.145505
#> 23 2.2 0.9796412 0.9450269 0.7437026 1.137437
#> 24 2.3 0.9699355 0.9355536 0.7324660 1.129631
#> 25 2.4 0.9606945 0.9265763 0.7219631 1.122079
#> 26 2.5 0.9518897 0.9180622 0.7121349 1.114772
#> 27 2.6 0.9434949 0.9099810 0.7029275 1.107077
#> 28 2.7 0.9354855 0.9023045 0.6942916 1.099299
#> 29 2.8 0.9278387 0.8950069 0.6861824 1.091765
#> 30 2.9 0.9205332 0.8880638 0.6785589 1.084469
#> 31 3.0 0.9135494 0.8814529 0.6713337 1.078713
#>
#> $`Diversity profiles`$renyi$Medium
#> q observed mean lower upper
#> 1 0.0 1.386294 1.386294 1.3862944 1.386294
#> 2 0.1 1.361684 1.359842 1.3299986 1.375825
#> 3 0.2 1.338324 1.334944 1.2807176 1.365653
#> 4 0.3 1.316227 1.311581 1.2379155 1.355784
#> 5 0.4 1.295386 1.289705 1.2009025 1.346223
#> 6 0.5 1.275778 1.269248 1.1689327 1.336972
#> 7 0.6 1.257364 1.250129 1.1412747 1.328029
#> 8 0.7 1.240096 1.232265 1.1172546 1.319393
#> 9 0.8 1.223919 1.215567 1.0910379 1.311061
#> 10 0.9 1.208771 1.199951 1.0662838 1.303028
#> 11 1.0 1.194591 1.185335 1.0436300 1.295288
#> 12 1.1 1.181312 1.171640 1.0228489 1.287833
#> 13 1.2 1.168873 1.158795 1.0035584 1.280657
#> 14 1.3 1.157212 1.146732 0.9888917 1.273751
#> 15 1.4 1.146270 1.135389 0.9760726 1.267107
#> 16 1.5 1.135990 1.124710 0.9642301 1.260714
#> 17 1.6 1.126322 1.114641 0.9532340 1.254565
#> 18 1.7 1.117216 1.105137 0.9429775 1.248649
#> 19 1.8 1.108628 1.096153 0.9324885 1.242920
#> 20 1.9 1.100516 1.087649 0.9212869 1.237397
#> 21 2.0 1.092842 1.079591 0.9107415 1.232078
#> 22 2.1 1.085572 1.071946 0.9007980 1.226955
#> 23 2.2 1.078675 1.064683 0.8914080 1.222018
#> 24 2.3 1.072122 1.057777 0.8825283 1.217260
#> 25 2.4 1.065887 1.051201 0.8741204 1.212672
#> 26 2.5 1.059945 1.044934 0.8663092 1.208248
#> 27 2.6 1.054277 1.038955 0.8589023 1.204347
#> 28 2.7 1.048862 1.033245 0.8518644 1.201467
#> 29 2.8 1.043682 1.027787 0.8451700 1.198726
#> 30 2.9 1.038722 1.022565 0.8387959 1.196113
#> 31 3.0 1.033967 1.017565 0.8327210 1.193618
#>
#> $`Diversity profiles`$renyi$Short
#> q observed mean lower upper
#> 1 0.0 1.0986123 1.0986123 1.0986123 1.098612
#> 2 0.1 1.0951755 1.0910644 1.0710416 1.098337
#> 3 0.2 1.0917172 1.0835984 1.0437308 1.098062
#> 4 0.3 1.0882393 1.0762277 1.0167984 1.097787
#> 5 0.4 1.0847438 1.0689647 0.9903557 1.097512
#> 6 0.5 1.0812328 1.0618204 0.9645046 1.097237
#> 7 0.6 1.0777084 1.0548045 0.9393357 1.096963
#> 8 0.7 1.0741728 1.0479256 0.9149275 1.096690
#> 9 0.8 1.0706283 1.0411906 0.8913451 1.096416
#> 10 0.9 1.0670771 1.0346054 0.8686402 1.096143
#> 11 1.0 1.0635215 1.0281744 0.8468513 1.095871
#> 12 1.1 1.0599638 1.0219012 0.8260045 1.095599
#> 13 1.2 1.0564064 1.0157878 0.8061137 1.095327
#> 14 1.3 1.0528516 1.0098357 0.7871823 1.095056
#> 15 1.4 1.0493016 1.0040451 0.7692042 1.094785
#> 16 1.5 1.0457588 0.9984157 0.7521650 1.094515
#> 17 1.6 1.0422255 0.9929463 0.7360433 1.094245
#> 18 1.7 1.0387040 0.9876354 0.7208122 1.093976
#> 19 1.8 1.0351963 0.9824806 0.7064402 1.093707
#> 20 1.9 1.0317048 0.9774794 0.6928927 1.093439
#> 21 2.0 1.0282315 0.9726288 0.6801327 1.093172
#> 22 2.1 1.0247784 0.9679256 0.6681218 1.092905
#> 23 2.2 1.0213476 0.9633662 0.6568209 1.092639
#> 24 2.3 1.0179410 0.9589470 0.6461911 1.092373
#> 25 2.4 1.0145603 0.9546643 0.6361937 1.092109
#> 26 2.5 1.0112074 0.9505141 0.6267911 1.091844
#> 27 2.6 1.0078840 0.9464927 0.6179467 1.091581
#> 28 2.7 1.0045915 0.9425961 0.6096255 1.091318
#> 29 2.8 1.0013316 0.9388203 0.6017940 1.091056
#> 30 2.9 0.9981055 0.9351616 0.5944204 1.090795
#> 31 3.0 0.9949147 0.9316160 0.5874745 1.090535
#>
#> attr(,"R")
#> [1] 100
#> attr(,"conf")
#> [1] 0.95
#> attr(,"parameter")
#> [1] "renyi"
#> attr(,"ci.type")
#> [1] "perc"
#>
#> $`Diversity profiles`$tsallis
#> $`Diversity profiles`$tsallis$Long
#> q observed mean lower upper
#> 1 0.0 3.0000000 2.9900000 3.0000000 3.0000000
#> 2 0.1 2.6589612 2.6378910 2.4996223 2.7161679
#> 3 0.2 2.3713503 2.3447960 2.1181515 2.4676103
#> 4 0.3 2.1274272 2.0988213 1.8235216 2.2492716
#> 5 0.4 1.9193870 1.8907700 1.5928772 2.0568872
#> 6 0.5 1.7409539 1.7134770 1.4098232 1.8868521
#> 7 0.6 1.5870644 1.5613196 1.2625179 1.7361131
#> 8 0.7 1.4536179 1.4298533 1.1567171 1.6020787
#> 9 0.8 1.3372809 1.3155399 1.0650894 1.4825443
#> 10 0.9 1.2353333 1.2155430 0.9801273 1.3756306
#> 11 1.0 1.1455462 1.1275734 0.9078287 1.2797376
#> 12 1.1 1.0660859 1.0497713 0.8455749 1.1934717
#> 13 1.2 0.9954373 0.9806158 0.7914366 1.1156705
#> 14 1.3 0.9323437 0.9188559 0.7416317 1.0453117
#> 15 1.4 0.8757575 0.8634558 0.6948633 0.9815190
#> 16 1.5 0.8248023 0.8135534 0.6534720 0.9235343
#> 17 1.6 0.7787414 0.7684270 0.6166461 0.8707008
#> 18 1.7 0.7369530 0.7274689 0.5837189 0.8224474
#> 19 1.8 0.6989100 0.6901654 0.5541388 0.7782770
#> 20 1.9 0.6641637 0.6560795 0.5274471 0.7377551
#> 21 2.0 0.6323302 0.6248380 0.5032600 0.7005015
#> 22 2.1 0.6030805 0.5961205 0.4812547 0.6661827
#> 23 2.2 0.5761307 0.5696510 0.4611581 0.6345670
#> 24 2.3 0.5512352 0.5451907 0.4427386 0.6054311
#> 25 2.4 0.5281808 0.5225320 0.4257981 0.5784096
#> 26 2.5 0.5067815 0.5014938 0.4101673 0.5533067
#> 27 2.6 0.4868749 0.4819176 0.3957002 0.5299479
#> 28 2.7 0.4683181 0.4636643 0.3822711 0.5081781
#> 29 2.8 0.4509853 0.4466111 0.3697706 0.4878587
#> 30 2.9 0.4347655 0.4306495 0.3581038 0.4688654
#> 31 3.0 0.4195602 0.4156832 0.3471879 0.4510869
#>
#> $`Diversity profiles`$tsallis$Medium
#> q observed mean lower upper
#> 1 0.0 3.0000000 3.0000000 3.0000000 3.0000000
#> 2 0.1 2.6732473 2.6523415 2.5743673 2.7140085
#> 3 0.2 2.3966283 2.3636721 2.2412466 2.4639346
#> 4 0.3 2.1610045 2.1217618 1.9751598 2.2445591
#> 5 0.4 1.9590700 1.9172331 1.7635515 2.0518492
#> 6 0.5 1.7849628 1.7428433 1.5832919 1.8820998
#> 7 0.6 1.6339616 1.5929579 1.4319269 1.7318509
#> 8 0.7 1.5022489 1.4631610 1.3068920 1.5984064
#> 9 0.8 1.3867251 1.3499657 1.1989887 1.4794915
#> 10 0.9 1.2848625 1.2505983 1.1022878 1.3731815
#> 11 1.0 1.1945906 1.1628361 1.0177378 1.2778429
#> 12 1.1 1.1142058 1.0848843 0.9445421 1.1920851
#> 13 1.2 1.0422993 1.0152838 0.8805382 1.1147205
#> 14 1.3 0.9777011 0.9528392 0.8225672 1.0447279
#> 15 1.4 0.9194342 0.8965647 0.7698358 0.9812134
#> 16 1.5 0.8666793 0.8456416 0.7229659 0.9237489
#> 17 1.6 0.8187454 0.7993857 0.6811250 0.8713677
#> 18 1.7 0.7750476 0.7572214 0.6436161 0.8234740
#> 19 1.8 0.7350876 0.7186616 0.6098537 0.7795816
#> 20 1.9 0.6984396 0.6832915 0.5793439 0.7392671
#> 21 2.0 0.6647377 0.6507562 0.5516686 0.7021605
#> 22 2.1 0.6336659 0.6207502 0.5264728 0.6679337
#> 23 2.2 0.6049505 0.5930093 0.5034538 0.6363063
#> 24 2.3 0.5783532 0.5673040 0.4823529 0.6070271
#> 25 2.4 0.5536656 0.5434338 0.4629479 0.5798739
#> 26 2.5 0.5307051 0.5212231 0.4450475 0.5546499
#> 27 2.6 0.5093107 0.5005173 0.4284866 0.5311799
#> 28 2.7 0.4893400 0.4811798 0.4131220 0.5093080
#> 29 2.8 0.4706669 0.4630896 0.3988293 0.4888948
#> 30 2.9 0.4531792 0.4461389 0.3854999 0.4698155
#> 31 3.0 0.4367766 0.4302317 0.3730390 0.4519579
#>
#> $`Diversity profiles`$tsallis$Short
#> q observed mean lower upper
#> 1 0.0 2.0000000 2.0000000 2.0000000 2.0000000
#> 2 0.1 1.8661938 1.8539385 1.7787768 1.8740070
#> 3 0.2 1.7437218 1.7223719 1.5950475 1.7577557
#> 4 0.3 1.6315129 1.6035690 1.4413061 1.6504216
#> 5 0.4 1.5286044 1.4960382 1.3116710 1.5512539
#> 6 0.5 1.4341299 1.3984902 1.2015178 1.4595689
#> 7 0.6 1.3473096 1.3098066 1.1071974 1.3747430
#> 8 0.7 1.2674411 1.2290140 1.0258192 1.2962082
#> 9 0.8 1.1938915 1.1552627 0.9550842 1.2234462
#> 10 0.9 1.1260900 1.0878088 0.8931564 1.1559841
#> 11 1.0 1.0635215 1.0259993 0.8368996 1.0933903
#> 12 1.1 1.0057210 0.9692595 0.7867645 1.0352708
#> 13 1.2 0.9522684 0.9170824 0.7419856 0.9812656
#> 14 1.3 0.9027839 0.8690195 0.7017921 0.9310457
#> 15 1.4 0.8569240 0.8246732 0.6655454 0.8843104
#> 16 1.5 0.8143777 0.7836906 0.6327139 0.8407845
#> 17 1.6 0.7748634 0.7457574 0.6057917 0.8002164
#> 18 1.7 0.7381257 0.7105933 0.5810880 0.7623757
#> 19 1.8 0.7039330 0.6779480 0.5582623 0.7270513
#> 20 1.9 0.6720751 0.6475973 0.5370839 0.6940500
#> 21 2.0 0.6423611 0.6193403 0.5173611 0.6631944
#> 22 2.1 0.6146175 0.5929964 0.4980090 0.6343222
#> 23 2.2 0.5886863 0.5684033 0.4799675 0.6072842
#> 24 2.3 0.5644238 0.5454146 0.4631037 0.5819435
#> 25 2.4 0.5416992 0.5238983 0.4473029 0.5581744
#> 26 2.5 0.5203930 0.5037350 0.4324653 0.5358616
#> 27 2.6 0.5003964 0.4848167 0.4179304 0.5148987
#> 28 2.7 0.4816099 0.4670456 0.4040632 0.4951883
#> 29 2.8 0.4639427 0.4503326 0.3910486 0.4766405
#> 30 2.9 0.4473116 0.4345971 0.3788115 0.4591725
#> 31 3.0 0.4316406 0.4197656 0.3672852 0.4427083
#>
#> attr(,"R")
#> [1] 100
#> attr(,"conf")
#> [1] 0.95
#> attr(,"parameter")
#> [1] "tsallis"
#> attr(,"ci.type")
#> [1] "perc"
#>
#>