Data from: Monte Carlo Strategies for selecting parameter values in simulation experiments

Simulation experiments are used widely throughout evolutionary biology and bioinformatics to compare models, promote methods, and test hypotheses. The biggest practical constraint on simulation experiments is the computational demand, particularly as the number of parameters increases. Given the extraordinary success of Monte Carlo methods for conducting inference in phylogenetics, and indeed throughout the sciences, we investigate ways in which Monte Carlo framework can be used to carry out simulation experiments more efficiently. The key idea is to sample parameter values for the experiments, rather than iterate through them exhaustively. Exhaustive analyses become completely infeasible when the number of parameters gets too large, whereas sampled approaches can fare better in higher dimensions. We illustrate the framework with applications to phylogenetics and genetic archaeology.Scripts and sources for Case study 2Our C++ implementation of the plant domestication model described in Allaby et al (2008) referenced in our manuscript, along with the Python scripts used to demonstrate our MCMC/importance sampling approach with this model.CaseStudyTwo.tar.bz2UPGMA/NJ comparison Python scriptsScripts used to perform MCMC/importance sampling to compare UPGMA and NJ tree inference methods. The scripts used to compare these methods using a grid search approach, and at random points sampled uniformly from the prior distribution are also included, as well as scripts to compare the grid search/prior sampling to our MCMC+IS method.CaseStudyOne.tar.bz2supplement_LeighBryant2015Updated supplementary information, including details of case study simulations