subset.varbvs: Select hyperparameter settings from varbvs analysis.

In varbvs: Large-Scale Bayesian Variable Selection Using Variational Methods

Select hyperparameter settings from varbvs analysis.

Description

Select a subset of the candidate hyperparameter settings, and return a new varbvs analysis object with these hyperparameter settings only.

Usage

  ## S3 method for class 'varbvs'
subset(x, subset, ...)

Arguments

x	Output of function varbvs.
subset	Expression indicating hyperparameter settings to select. This expression should include one or more of logodds, sigma and sa.
...	Other arguments to generic subset function. These extra arguments are not used here.

Value

An object with S3 class c("varbvs","list").

Author(s)

Peter Carbonetto [e-mail]

References

P. Carbonetto and M. Stephens (2012). Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies. Bayesian Analysis 7, 73–108.

See Also

varbvs

Examples

  # First run one of the examples in help(varbvs), then try running 
  # this.
  #
  #   fit.new <- subset(fit,logodds < (-2))
  # 

varbvs documentation built on June 7, 2023, 5:43 p.m.