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Table 1 Summary of simulation results

From: Selecting informative subsets of sparse supermatrices increases the chance to find correct trees

Simulation Saturation tic taxa Genes d QS -value d QS f
       (min/max) ( correct )
Gaussian Set1        
Unreduced 0.29 0.15 50 50 0.003 (0.99/1.0) 0.01
mare with B 0.69 0.62 9 6 0.0 (0.73/1.0) 0.67
mare with B 0.74 0.74 7 9 0.0 (0.6/1.0) 0.47
Gaussian Set2        
Unreduced 0.29 0.1 50 50 0.003 (0.98/0.99) 0
mare with B 0.67 0.61 10 5 0 (0.6/1.0) 0.51
mare with B 0.73 0.73 7 9 0 (0.2/1) 0.42
Power-law non-random Set1        
Unreduced 0.13 0.06 50 50 0.17 (0.48/0.99) 0
mare with B 0.46 0.38 25 12 0.02 (0.81/1.0) 0.15
mare with B 0.51 0.51 15 24 0.02 (0.48/1.0) 0.16
Power-law non-random Set2        
Unreduced 0.13 0.05 50 50 0.15 (0.43/0.99) 0
mare with B 0.45 0.38 24.5 10 0.06 (0.64/1.0) 0.09
mare with B 0.53 0.53 23 16 0.01 (0.47/1.0) 0.12
Gene threshold Set1        
With B 0.72 0.50 34 2 0.05 (0.00/0.42) 0.06
Gene threshold Set2        
With B 0.64 0.28 44 3 0.03 (0.00/0.59) 0.03
Gene/taxa threshold Set1        
With B 0.59 0.37 21 4 0.05 (0.00/0.46) 0.12
Gene/taxa threshold Set2        
With B 0.66 0.30 21.5 4 0.01 (0.00/0.45) 0.25
  1. All values are medians of 100 simulations.
  2. total information content (tic) of un-weighted matrices is allways higher due to the fact that all genes are coded as present/absent (1/0).
  3. f(correct) refers to the frequency of correct trees per 100 simulations.