DOI: 10.5061/dryad.xksn02vpk
title: Data from: How to validate a Bayesian evolutionary model

description: Biology has become a highly mathematical discipline in which probabilistic
 models play a central role. As a result, research in the biological
 sciences is now dependent on computational tools capable of carrying out
 complex analyses. These tools must be validated before they can be used,
 but what is understood as validation varies widely among methodological
 contributions. This may be a consequence of the still embryonic stage of
 the literature on statistical software validation for computational
 biology. Our manuscript aims to advance this literature. Here, we
 describe, illustrate, and introduce new good practices for assessing the
 correctness of a model implementation, with an emphasis on Bayesian
 methods. We also introduce a suite of functionalities for automating
 validation protocols. It is our hope that the guidelines presented here
 help sharpen the focus of discussions on (as well as elevate) expected
 standards of statistical software for biology.
description: # How to validate a Bayesian evolutionary model The main supplementary
 file (text document) is `mendes_etal_validation_supp_2024.pdf`. You should
 find this file hosted on Zenodo and pointed to by Dryad. This Dryad
 repository also hosts key output files for reproducing the figures in the
 manuscript. Namely: 1. `covg_match_3_to_300_tips_yule_bm.RData`: R
 workspace containing table with simulated and inferred (HPD) values of
 parameters investigated in "Scenario 1". This data underlies
 coverage validation plots (Fig. 4 and 7) in the main manuscript; 2.
 `covg_mismatch_3_to_300_tips_yule_bm.RData`: R workspace containing table
 with simulated and inferred (HPD) values of parameters investigated in
 "Scenario 2". This data underlies coverage validation plots
 (Fig. 4 and 7) in the main manuscript; 3.
 `covg_match_100_to_200_tips_yule_bm.RData`: R workspace containing table
 with simulated and inferred (HPD) values of parameters investigated in
 "Scenario 3". This data underlies coverage validation plots
 (Fig. 4 and 7) in the main manuscript; 4. `coal_BHV_dist.csv`: Table
 containing simulated, inferred (HPD) values, and rank values of the BHV
 phylogenetic-space statistic, for the coalescent model mentioned in the
 main text (Fig. 8), and examined in more detail in the supplement
 (Supplementary Figs. 4 and 5); 5. `coal_KC_dist.csv`: Table containing
 simulated, inferred (HPD) values, and rank values of the KC
 phylogenetic-space statistic, for the coalescent model mentioned in the
 main text (Fig. 8), and examined in more detail in the supplement
 (Supplementary Figs. 4 and 5); 6. `coal_RF_dist.csv`: Table containing
 simulated, inferred (HPD) values, and rank values of the Robinson-Foulds
 phylogenetic-space statistic, for the coalescent model mentioned in the
 main text (Fig. 8), and examined in more detail in the supplement
 (Supplementary Figs. 4 and 5); 7. `coal_max_branch_dist.csv`: Table
 containing simulated, inferred (HPD) values, and rank values of the
 largest branch length (LB) phylogenetic-space statistic, for the
 coalescent model mentioned in the main text (Fig. 8), and examined in more
 detail in the supplement (Supplementary Figs. 4 and 5); 8.
 `coal_range_branch_dist.csv`: Table containing simulated, inferred (HPD)
 values, and rank values of the maximum branch distance (R)
 phylogenetic-space statistic, for the coalescent model mentioned in the
 main text (Fig. 8), and examined in more detail in the supplement
 (Supplementary Figs. 4 and 5); 9. `coal_tree_length_dist.csv`: Table
 containing simulated, inferred (HPD) values, and rank values of the tree
 length (LEN) phylogenetic-space statistic, for the coalescent model
 mentioned in the main text (Fig. 8), and examined in more detail in the
 supplement (Supplementary Figs. 4 and 5); 10. `RUV_topological.csv`: This
 table merges all "coal_*" tables listed above; 11.
 `RUV_scenario_1.csv`: Table containing simulated, inferred (HPD) values,
 and rank values of the parameters investigated in "Scenario 1".
 This data underlies RUV plots (Fig. 6) in the main manuscript; 12.
 `RUV_scenario_2.csv`: Table containing simulated, inferred (HPD) values,
 and rank values of the parameters investigated in "Scenario 2".
 This data underlies RUV plots (Fig. 6) in the main manuscript; 13.
 `RUV_scenario_3.csv`: Table containing simulated, inferred (HPD) values,
 and rank values of the parameters investigated in "Scenario 3".
 This data underlies RUV plots (Fig. 6) in the main manuscript; 14.
 `RUV_height_scenario_1.csv`: Table containing simulated, inferred (HPD)
 values, and rank values of the root age in "Scenario 1". This
 data underlies RUV plots (Fig. 7) in the main manuscript; 15.
 `RUV_height_scenario_2.csv`: Table containing simulated, inferred (HPD)
 values, and rank values of the root age in "Scenario 2". This
 data underlies RUV plots (Fig. 7) in the main manuscript; 16.
 `RUV_height_scenario_3.csv`: Table containing simulated, inferred (HPD)
 values, and rank values of the root age in "Scenario 3". This
 data underlies RUV plots (Fig. 7) in the main manuscript. Finally, the
 interested reader can also find the GitHub repository for this paper
 [here](https://github.com/rbouckaert/DeveloperManual/). In that repository
 you will find instructions and the necessary files to reproduce the entire
 pipeline of the (i)
 [coverage](https://github.com/rbouckaert/DeveloperManual/tree/master/r_scripts/coverage_validation), and (ii) [RUV](https://github.com/rbouckaert/DeveloperManual/tree/master/r_scripts/ruv) analyses, as we did.