Figure 4

RMSPE: ABC algorithm validation for estimator bias and precision. Histograms depicting the distribution of RMSPE (root mean square posterior error) for 100 estimates of parameter values given 100 PODS (pseudo observed data sets) simulated with known parameter values. Panel A corresponds to estimates of E(τ) and panel B corresponds to estimates of Ω. For all PODS, Ψ (number divergence times across five taxon-pairs) is drawn from its discrete uniform hyper-prior ranging between 1 (simultaneous divergence) and 5 (the number of taxon-pairs). PODS and corresponding priors were simulated given data from 1, 4, 8, 16, 32 and 64 loci each from 5 taxon-pairs. Each RMSPE is calculated from the true hyper-parameter value (E(τ) and Ω) and the corresponding 500 accepted points out of a total 1,500,000 draws from the hyper-prior using ABC with local linear regression and a summary statistic vector D _m that only included mean values of π _b across loci from every taxon-pair.