delim_join() returns a tbl_df of species delimitation
outputs whose partitions are consistent across different methods.
Arguments
- delim
A list or data.frame of multiple species delimitation methods outputs.
Value
an object of class tbl_df.
Details
delim_join() is a helper function to join multiple lists or columns of species
delimitation outputs into a single tbl_df while keeping consistent
identifications across multiple methods. Species delimitation outputs are in general a
list or data frame of sample labels and its species partitions (Species 1, Species 2, etc.). These
partition names may be or not the same across two or more methods. delim_join() standardizes
partition names across two or more species delimitation outputs while keeping its underlying structure intact.
Examples
# \donttest{
## run GMYC
gmyc_res <- splits::gmyc(ape::as.phylo(geophagus_beast), method = "single")
#> node T loglik
#> 2 -0.2503616 1000.199
#> 3 -0.1229369 999.5983
#> 4 -0.0789159 1000.413
#> 5 -0.07317712 999.2487
#> 6 -0.06528104 1000.194
#> 7 -0.05250486 1000.794
#> 8 -0.04465915 1000.808
#> 9 -0.04302288 1001.093
#> 10 -0.04030643 1001.338
#> 11 -0.03916037 1001.458
#> 12 -0.03904058 1002.02
#> 13 -0.03515353 1002.337
#> 14 -0.0346084 1002.964
#> 15 -0.03278097 1003.53
#> 16 -0.02541094 1004.957
#> 17 -0.02413816 1006.11
#> 18 -0.02142377 1006.353
#> 19 -0.02018164 1008.143
#> 20 -0.0167524 1010.027
#> 21 -0.0164289 1010.027
#> 22 -0.01521815 1009.605
#> 23 -0.01460825 1009.586
#> 24 -0.01435513 1009.153
#> 25 -0.01372697 1008.74
#> 26 -0.01335444 1007.496
#> 27 -0.01311278 1006.208
#> 28 -0.0131017 1006.185
#> 29 -0.01240316 1006.167
#> 30 -0.01219801 1004.897
#> 31 -0.01199715 1005.093
#> 32 -0.01174803 1006.109
#> 33 -0.01114228 1006.38
#> 34 -0.01111022 1004.802
#> 35 -0.01100491 1004.5
#> 36 -0.0107574 1004.747
#> 37 -0.01062164 1004.103
#> 38 -0.01060558 1003.999
#> 39 -0.01050048 1003.101
#> 40 -0.01049683 1002.807
#> 41 -0.01043136 1002.744
#> 42 -0.01003061 1003.447
#> 43 -0.01001803 1002.766
#> 44 -0.009975316 1002.918
#> 45 -0.009975263 1002.088
#> 46 -0.009938944 1001.959
#> 47 -0.009913875 1001.172
#> 48 -0.009825785 1001.681
#> 49 -0.009780681 1002.139
#> 50 -0.009371663 1001.726
#> 51 -0.009247986 1001.186
#> 52 -0.009007852 1001.444
#> 53 -0.008996279 1001.294
#> 54 -0.00888486 1000.469
#> 55 -0.008597392 1000.302
#> 56 -0.008565803 999.5867
#> 57 -0.008280494 999.1293
#> 58 -0.008164649 997.9924
#> 59 -0.008076452 996.8458
#> 60 -0.007999869 996.0699
#> 61 -0.00765457 995.2669
#> 62 -0.007646285 994.7324
#> 63 -0.007643749 993.9068
#> 64 -0.007627436 993.0406
#> 65 -0.007492421 993.934
#> 66 -0.007020123 993.1576
#> 67 -0.006912393 993.2364
#> 68 -0.006783196 992.5072
#> 69 -0.006747962 992.281
#> 70 -0.006264295 992.4479
#> 71 -0.00601552 992.8324
#> 72 -0.005795439 993.0989
#> 73 -0.00573242 993.1665
#> 74 -0.005509334 992.8808
#> 75 -0.005488503 992.3843
#> 76 -0.005425978 992.5391
#> 77 -0.005411267 992.0382
#> 78 -0.005306896 991.5123
#> 79 -0.005306051 990.9922
#> 80 -0.005164414 991.1308
#> 81 -0.004907784 991.3595
#> 82 -0.004797087 990.8825
#> 83 -0.004746877 990.4211
#> 84 -0.004662424 990.3632
#> 85 -0.004520232 991.0171
#> 86 -0.004264922 990.6133
#> 87 -0.004251489 990.7873
#> 88 -0.004157546 990.4221
#> 89 -0.004112639 990.721
#> 90 -0.004108289 991.061
#> 91 -0.003888943 990.6722
#> 92 -0.003801218 991.2324
#> 93 -0.003754736 990.9821
#> 94 -0.003731354 990.7235
#> 95 -0.003688184 990.4522
#> 96 -0.003591255 990.6112
#> 97 -0.003564653 990.68
#> 98 -0.003442048 990.46
#> 99 -0.00341143 990.2366
#> 100 -0.003393153 990.0051
#> 101 -0.003386863 989.8279
#> 102 -0.003361018 989.6713
#> 103 -0.003288484 989.7291
#> 104 -0.003241535 989.5243
#> 105 -0.003124931 989.9282
#> 106 -0.003105419 990.3339
#> 107 -0.003101937 990.2319
#> 108 -0.003075062 990.3473
#> 109 -0.00306445 990.4668
#> 110 -0.002980459 990.176
#> 111 -0.002962743 990.4197
#> 112 -0.002957225 990.2826
#> 113 -0.002838299 990.1388
#> 114 -0.002785399 990.0348
#> 115 -0.002736578 989.9954
#> 116 -0.002734335 990.0459
#> 117 -0.002686254 989.8565
#> 118 -0.002669519 990.2641
#> 119 -0.002637125 989.947
#> 120 -0.002617322 989.8852
#> 121 -0.002542929 989.8291
#> 122 -0.002478636 989.7958
#> 123 -0.002473902 989.7829
#> 124 -0.00206938 989.7749
#> 125 -0.002015661 989.8818
#> 126 -0.001766944 990.0066
#> 127 -0.001725274 990.2031
#> 128 -0.001652185 990.4162
#> 129 -0.001598489 990.6556
#> 130 -0.00149766 990.9172
#> 131 -0.001491313 991.216
#> 132 -0.001312548 991.5267
#> 133 -0.001197979 991.9014
#> 134 -0.001131569 992.3254
#> 135 -0.000997143 992.7881
#> 136 -0.0007525219 993.3164
#>
#> Sun Mar 30 05:36:07 2025
#> finish.
# create a tibble
gmyc_df <- gmyc_tbl(gmyc_res)
## run bGMYC
bgmyc_res <- bGMYC::bgmyc.singlephy(ape::as.phylo(geophagus_beast),
mcmc = 11000,
burnin = 1000,
thinning = 100,
t1 = 2,
t2 = ape::Ntip(ape::as.phylo(geophagus_beast)),
start = c(1, 0.5, 50)
)
#> You are running bGMYC on a single phylogenetic tree.
#> This tree contains 137 tips.
#> The Yule process rate change parameter has a uniform prior ranging from 0 to 2 .
#> The coalescent process rate change parameter has a uniform prior ranging from 0 to 2 .
#> The threshold parameter, which is equal to the number of species, has a uniform prior ranging from 2 to 137 . The upper bound of this prior should not be more than the number of tips in your trees.
#> The MCMC will start with the Yule parameter set to 1 .
#> The MCMC will start with the coalescent parameter set to 0.5 .
#> The MCMC will start with the threshold parameter set to 50 . If this number is greater than the number of tips in your tree, an error will result.
#> Given your settings for mcmc, burnin and thinning, your analysis will result in 100 samples being retained.
#> 10 %
#> 20 %
#> 30 %
#> 40 %
#> 50 %
#> 60 %
#> 80 %
#> 90 %
#> 100 %
#> acceptance rates
#> py pc th
#> 0.5508182 0.5491818 0.2359091
# create a tibble
bgmyc_df <- bgmyc_tbl(bgmyc_res, ppcutoff = 0.05)
## LocMin
# create a distance matrix
mat <- ape::dist.dna(geophagus, model = "raw", pairwise.deletion = TRUE)
# estimate local minima from `mat`
locmin_res <- spider::localMinima(mat)
#> [1] 0.006828358 0.018994392 0.040791871 0.055999413 0.067151612 0.082105695
# create a tibble
locmin_df <- locmin_tbl(mat,
threshold = locmin_res$localMinima[1],
haps = ape::as.phylo(geophagus_beast)$tip.label
)
# join delimitations
all_delims <- delim_join(list(gmyc_df, bgmyc_df, locmin_df))
#> Checking species delimitation tables...
#> Checking table 1 against table 2...
#> ✔ Labels are the same across tables but they likely are unordered.
#> Checking table 1 against table 2...
#> Checking table 1 against table 3...
#> Checking table 1 against table 3...
#> ✔ Labels are the same across tables but they likely are unordered.
#> Checking table 1 against table 3...
#> ✔ Checking complete!
#> Checking table 1 against table 3...
#> Checking table 1 against table 3...
# check
all_delims
#> # A tibble: 137 × 4
#> labels bgmyc gmyc locmin
#> <chr> <chr> <chr> <chr>
#> 1 GU701784.1 sp1 sp1 sp1
#> 2 GU701785.1 sp1 sp1 sp1
#> 3 MH780911.1 sp1 sp1 sp1
#> 4 OR732927.1 sp1 sp1 sp1
#> 5 JN988869.1 sp1 sp1 sp1
#> 6 OR732928.1 sp1 sp1 sp1
#> 7 MZ504448.1 sp2 sp2 sp2
#> 8 MZ504450.1 sp2 sp2 sp2
#> 9 MZ504454.1 sp2 sp2 sp2
#> 10 MZ504457.1 sp3 sp3 sp3
#> # ℹ 127 more rows
# }