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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.
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BMC Ecology and Evolution

ISSN: 2730-7182

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