ptycho-package: Bayesian Variable Selection with Hierarchical Priors
In ptycho: Bayesian Variable Selection with Hierarchical Priors

Description Details Author(s) References

Bayesian variable selection for linear regression models using hierarchical priors. There is a prior that combines information across responses and one that combines information across covariates, as well as a standard spike and slab prior for comparison. An MCMC samples from the marginal posterior distribution for the 0-1 variables indicating if each covariate belongs to the model for each response.

This package provides functions to carry out Bayesian model selection combining different layers of information: across multiple traits or across multiple variants in the same gene. The priors are described by Stell and Sabatti (2015). To sample the posterior distribution for specified genotype and phenotype matrices, use ptycho.

This package also provides functions to generate simulated data as in Stell and Sabatti (2015); see createData and web.stanford.edu/~lstell/ptycho/. Those datasets are not included in this package because they have images about 20 MB or larger. Instead small data objects are included for examples; see Data.

Functions for post-processing ptycho objects are described at checkConvergence and PosteriorStatistics.

Laurel Stell and Chiara Sabatti
Maintainer: Laurel Stell <lstell@stanford.edu>

Stell, L. and Sabatti, C. (2015) Genetic variant selection: learning across traits and sites, arXiv:1504.00946.

ptycho documentation built on May 2, 2019, 9:45 a.m.