Figure 1

Stratified accuracy analysis of Scoredist and ML. To illustrate how estimated distance depends on the model, the average deviation is plotted as a function of true distance for two evolutionary models, Dayhoff and Mueller-Vingron. For each evolutionary distance between 1 and 200 PAM, 10 alignments were generated. For each alignment, the deviation was calculated as the difference between the estimated distance and the true distance used for data generation by ROSE [16]. The average of the 10 deviations was plotted using a running average with a window of 10 residues. Note that positive and negative deviations at the same true distance can cancel each other out – the curve only shows the average deviation and not the variability. The values in Table 1 measure the accuracy more correctly by using RMSD of every datapoint. The testset data was created with the matrices given by Dayhoff (A) or Müller-Vingron (B). In both cases, the estimators using the same evolutionary model as the testset data perform well. However, when switching the model in the estimator, Scoredist diverges less than ML, indicating that Scoredist is more robust. The curves show that ML-MV is more different from ML-Dayhoff than Scoredist-MV is from Scoredist-Dayhoff, particularly for the MV dataset in (B). The less difference between estimates using different models, the more robust is the method.