Data from: Recommendations for using msBayes to incorporate uncertainty in selecting an ABC model prior: a response to Oaks et al.

Prior specification is an essential component of parameter estimation and
model comparison in Approximate Bayesian computation (ABC). Oaks et al.
present a simulation-based power analysis of msBayes and conclude that
msBayes has low power to detect genuinely random divergence times across
taxa, and suggest the cause is Lindley's paradox. Although the
predictions are similar, we show that their findings are more
fundamentally explained by insufficient prior sampling that arises with
poorly chosen wide priors that critically undersample nonsimultaneous
divergence histories of high likelihood. In a reanalysis of their data on
Philippine Island vertebrates, we show how this problem can be
circumvented by expanding upon a previously developed procedure that
accommodates uncertainty in prior selection using Bayesian model
averaging. When these procedures are used, msBayes supports recent
divergences without support for synchronous divergence in the Oaks et al.
data and we further present a simulation analysis that demonstrates that
msBayes can have high power to detect asynchronous divergence under
narrower priors for divergence time. Our findings highlight the need for
exploration of plausible parameter space and prior sampling efficiency for
ABC samplers in high dimensions. We discus potential improvements to
msBayes and conclude that when used appropriately with model averaging,
msBayes remains an effective and powerful tool.