Data for: Approaches for handling missing values and their impacts on biological inferences: a molecular rate case study

These data files are associated with the manuscript entitled: "Approaches for handling missing values and their impacts on biological inferences: a molecular rate case study" by Jacqueline A. May, Zeny Feng, and Sarah J. Adamowicz. This project entailed an evaluation of missing data handling approach on inferences using a molecular evolution case study. A target mixed-type dataset was first imputed using a real data-driven strategy for imputation method selection. Both trait-only (non-phylogenetic) and phylogenetic imputation methods were used to impute the dataset. Phylogenetic generalized least squares (PGLS) analyses were then applied to the complete-case and imputed datasets, specifying the traits as predictors and molecular evolutionary rates as the response variable. Those traits that associate significantly with molecular rates were identified and PGLS models compared to determine how the approach for handling missing data impacts biological inferences and conclusions.

The files stored here are the trees built for phylogenetic imputation (RAxML tree and ultrametric tree versions) and the corresponding GenBank accession numbers.