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Table 2 AICc and BIC scores of the best partitioning scheme found by different algorithms on each dataset

From: Selecting optimal partitioning schemes for phylogenomic datasets

  AICc BIC
Dataset Greedy Relaxed clustering Strict clustering Greedy Relaxed clustering Strict clustering
(AICc) (ΔAICc) (ΔAICc) (BIC) (ΔBIC) (ΔBIC)
Ward_2010 103258 -34 -61 104877 -294 -606
Wainwright_2012 473537 -7 -59 477322 -73 -663
Pyron_2011 154838 -42 -173 156039 -177 -383
Li_2008 252583 -6 -242 254327 -183 -769
Leavitt_2013 424129 -216 -757 426143 -837 -3176
Kaffenberger_2011 120020 -6 -75 121452 -62 -150
Irisarri_2012 214655 -41 -187 216209 -152 -1151
Hackett_2008 1830824 -356 -1442 1837230 -964 -6362
Fong_2012 276517 -254 -1508 278400 -900 -2129
Endicott_2008 66966 -90 -479 70139 -455 -752
  1. The greedy algorithm performed best in all cases, as expected, and the AICc/BIC score is shown for each run with that algorithm. The relaxed clustering algorithm typically performed almost as well as the greedy algorithm, and always performed better than the strict clustering algorithm. ΔAICc or ΔBIC scores are shown for the clustering algorithms, and represent the difference in AICc or BIC score from the greedy algorithm.