Fig 5. Three dimensional representations of reconstructed wavelets from regression coefficients (βs) when differentiating among adaptive introgression, sweeps, and neutrality for summary statistics kurtosis of pairwise r² for SURFDAWave when γ = 1, when trained with statistics $\widehat{\pi }$, H₁, H₁₂, H₂/H₁, frequencies of first to fifth most common haplotypes, and mean, variance, skewness, and kurtosis of pairwise r².

SURFDAWave was trained on simulations of scenarios simulated under demographic specifications for European CEU demographic history. Note that the wavelet reconstructions for all summary statistics are plotted on the same scale, thereby making the distributions of some summaries difficult to decipher as their magnitudes are relatively small. SURFDAWave results shown are using Daubechies’ least-asymmetric wavelets to estimate spatial distributions of summary statistics. Level chosen through cross validation.