Source
Pons J., Barraclough T. G., Gomez-Zurita J., Cardoso A., Duran D. P., Hazell S., Kamoun S., Sumlin W. D., Vogler A. P. 2006. Sequence-based species delimitation for the DNA taxonomy of undescribed insects. Systematic Biology. 55:595-609.
Monaghan M. T., Wild R., Elliot M., Fujisawa T., Balke M., Inward D. J. G., Lees D. C., Ranaivosolo R., Eggleton P., Barraclough T. G., Vogler A. P. 2009. Accelerated species inventory on Madagascar using coalescent-based models of species delineation. Systematic Biology. 58:298-311.
Fujisawa T., Barraclough T. G. 2013. Delimiting Species Using Single-Locus Data and the Generalized Mixed Yule Coalescent Approach: A Revised Method and Evaluation on Simulated Data Sets. Systematic Biology. 62(5):707–724.
Arguments
- gmyc_res
Output from gmyc.
- delimname
Character. String to rename the delimitation method in the table. Default to 'gmyc'.
Value
An object of class tbl_df.
Details
splits package uses gmyc to optimize
genetic clusters and spec.list to cluster samples into
species partitions. gmyc_tbl() turns these results into a tibble which matches
the output from bgmyc_tbl and locmin_tbl.
Examples
# \donttest{
# run GMYC
gmyc_res <- splits::gmyc(ape::as.phylo(geophagus_beast))
#> 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 05:36:21 2025
#> finish.
# create a tibble
gmyc_df <- gmyc_tbl(gmyc_res)
# check
gmyc_df
#> # A tibble: 137 × 2
#> labels gmyc
#> <chr> <int>
#> 1 GU701784.1 1
#> 2 GU701785.1 1
#> 3 MH780911.1 1
#> 4 OR732927.1 1
#> 5 JN988869.1 1
#> 6 OR732928.1 1
#> 7 MZ504387.1 2
#> 8 MZ504388.1 2
#> 9 MZ504369.1 2
#> 10 MZ504390.1 2
#> # ℹ 127 more rows
# }