Large-Scale Bayesian Variable Selection Using Variational Methods
Estimate credible interval.
Compute normalized probabilities.
Summarize variable selection results in a single plot.
Make predictions from a model fitted by varbvs.
Make predictions from a model fitted by varbvsmix.
Return matrices of pseudorandom values.
Select hyperparameter settings from varbvs analysis.
Summarize a fitted variable selection model.
Internal varbvs functions
Large-scale Bayesian variable selection using variational methods
Accessing Properties of Fitted varbvs Models
Fit variable selection model using variational approximation methods.
Compute numerical estimate of Bayes factor.
Compute posterior statistics, ignoring correlations.
Fit linear regression with mixture-of-normals priors using variational...
Compute Bayes factors measuring improvement-in-fit along 1 dimension.
Fast algorithms for fitting Bayesian variable selection models and computing Bayes factors, in which the outcome (or response variable) is modeled using a linear regression or a logistic regression. The algorithms are based on the variational approximations described in "Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies" (P. Carbonetto & M. Stephens, 2012, <DOI:10.1214/12-BA703>). This software has been applied to large data sets with over a million variables and thousands of samples.