Generate a summary of the Bayesian variable selection model fitted using variational approximation methods.
# S3 method for varbvs
summary(object, cred.int = 0.95, nv, pip.cutoff, ...)
# S3 method for summary.varbvs
print(x, digits = 3, ...)
# S3 method for varbvs
print(x, digits = 3, ...)An object of class summary.varbvs, to be printed by
print.summary.varbvs.
Output of function varbvs.
Size of credible interval, a number between 0 and 1.
Show detailed statistics for top nv variables, ranked
according to their posterior inclusion probabilities. Only one of
nv and pip.cutoff may be specified. If neither are
specified, the default is nv = 5.
Show detailed statistics for all variables in which
the posterior inclusion probability (PIP) is at least
pip.cutoff. Only one of nv and pip.cutoff may
be specified.
Output of function summary.varbvs.
Number of digits shown when printing posterior probabilities of top nv variables.
Additional summary or print arguments.
Peter Carbonetto peter.carbonetto@gmail.com
The printed summary is divided into three parts. The first part
summarizes the data and optimization settings. It also reports the
hyperparameter setting that yields the largest marginal
likelihood---more precisely, the approximate marginal likelihood
computed using the variational method. For the linear regression only
(family = "gaussian") when no additional covariates (Z)
are included, it reports the estimated proportion of variance in the
outcome explained by the model (PVE), and the credible interval of the
PVE estimate brackets.
The second part summarizes the approximate posterior distribution of
the hyperparameters (sigma, sa, logodds). The "estimate" column is the
value averaged over hyperparameter settings, treating
objectlogw as (unnormalized) log-marginal probabilities. The
next column, labeled "Pr>x", where x = cred.int gives the
credible interval based on these weights (computed using function
cred).
The third part summarizes the variable selection results. This
includes the total number of variables included in the model at
different posterior probability thresholds, and a more detailed
summary of the variables included in the model with highest posterior
probability. For family = "gaussian", the "PVE" column gives
the estimated proportion of variance in the outcome explained by the
variable (conditioned on being included in the model).
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.
varbvs, varbvs.properties
# See help(varbvs) for examples.
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