The function Bayes_HHT() takes posterior samples from a Bayesian variable selection model (B, effects in columns, samples in rows), a hierarchical clustering of the predictors (i.e., the columns of X in y=Xb+e), and a Bayesian FDR threshold (alpha) and returns credible sets, consisting of predictors that are jointly associated with the outcome. Further details about the methodology can be found in Samaddar, Maiti, and de los Campos, 2023. The following example demonstrates the use of the function.
The following examples (used in Samaddar, Maiti, and de los Campos, 2023) illustrate the application of Bayesian Hierarchical Hypothesis Testing.
- Simple demonstration. This example uses a publicly available mice data set and presents a simple example that illustrates the methodology.
- Simulation 1. This simulation uses simulated genotypes and the script is stand-alone (i.e., it does not require having data subject to access restriction).
- Simulation 2. This simulation uses real human genotypes from the UK-Biobank.