Summarizing, printing, and plotting the contents of a
"blasso"-class object containing samples from
the posterior distribution of a Bayesian lasso model
Usage
## S3 method for class 'blasso'
print(x, ...)
## S3 method for class 'blasso'
summary(object, burnin = 0, ...)
## S3 method for class 'blasso'
plot(x, which=c("coef", "s2", "lambda2", "gamma",
"tau2i","omega2", "nu", "m", "pi"), subset = NULL, burnin = 0,
... )
## S3 method for class 'summary.blasso'
print(x, ...)
Arguments
object
a "blasso"-class object that must be named
object for the generic methods summary.blasso
x
a "blasso"-class object that must be named x
for the generic printing and plotting methods
print.summary.blasso and
plot.blasso
subset
a vector of indicies that can be used to specify
the a subset of the columns of tau2i or omega2 that
are plotted as boxplots in order to reduce clutter
burnin
number of burn-in rounds to discard before
reporting summaries and making plots. Must be non-negative
and less than x$T
which
indicates the parameter whose characteristics
should be plotted; does not apply to the summary
...
passed to print.blasso, or
plot.default
Details
print.blasso prints the call followed by a
brief summary of the MCMC run and a suggestion to try
the summary and plot commands.
plot.blasso uses an appropriate
plot command on the list entries of the
"blasso"-class object thus
visually summarizing the samples from the posterior distribution of
each parameter in the model depending on the which
argument supplied.
summary.blasso uses the summary command
on the list entries of the "blasso"-class object thus
summarizing the samples from the posterior distribution of each
parameter in the model.
print.summary.monomvn calls print.blasso
on the object and then prints the result of
summary.blasso
Value
summary.blasso returns a "summary.blasso"-class
object, which is a list containing (a subset of) the items below.
The other functions do not return values.
B
a copy of the input argument thin
T
total number of MCMC samples to be collected from x$T
thin
number of MCMC samples to skip before a sample is
collected (via thinning) from x$T
coef
a joint summary of x$mu and
the columns of x$beta, the regression coefficients
s2
a summary of x$s2, the variance parameter
lambda2
a summary of x$lambda2, the penalty
parameter, when lasso or ridge regression is active
lambda2
a summary of x$gamma,
when the NG extensions to the lasso are used
tau2i
a summary of the columns of the latent
x$tau2i parameters when lasso is active
omega2
a summary of the columns of the latent
x$omega2 parameters when Student-t errors are active
nu
a summary of x$nu, the degrees of freedom
parameter, when the Student-t model is active
bn0
the estimated posterior probability that the individual
components of the regression coefficients beta is
nonzero
m
a summary the model order x$m: the
number of non-zero regression coefficients beta
pi
the estimated Binomial proportion in the prior for
the model order when 2-vector input is provided for
mprior
providing 
.