\HeaderA{blasso.s3}{Summarizing Bayesian Lasso Output}{blasso.s3}
\aliasA{plot.blasso}{blasso.s3}{plot.blasso}
\aliasA{print.blasso}{blasso.s3}{print.blasso}
\aliasA{print.summary.blasso}{blasso.s3}{print.summary.blasso}
\aliasA{summary.blasso}{blasso.s3}{summary.blasso}
\keyword{methods}{blasso.s3}
\keyword{hplot}{blasso.s3}
\begin{Description}\relax
Summarizing, printing, and plotting the contents of a
\code{"blasso"}-class object containing samples from
the posterior distribution of a Bayesian lasso model
\end{Description}
\begin{Usage}
\begin{verbatim}
## 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, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] a \code{"blasso"}-class object that must be named
\code{object} for the generic methods \code{\LinkA{summary.blasso}{summary.blasso}} 
\item[\code{x}] a \code{"blasso"}-class object that must be named \code{x}
for the generic printing and plotting methods 
\code{\LinkA{print.summary.blasso}{print.summary.blasso}} and
\code{\LinkA{plot.blasso}{plot.blasso}} 
\item[\code{subset}] a vector of indicies that can be used to specify
the a subset of the columns of \code{tau2i} or \code{omega2} that
are plotted as boxplots in order to reduce clutter 
\item[\code{burnin}] number of burn-in rounds to discard before
reporting summaries and making plots.  Must be non-negative
and less than \code{x\$T}
\item[\code{which}] indicates the parameter whose characteristics
should be plotted; does not apply to the \code{\LinkA{summary}{summary}} 
\item[\code{...}] passed to \code{\LinkA{print.blasso}{print.blasso}}, or
\code{\LinkA{plot.default}{plot.default}}
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
\code{\LinkA{print.blasso}{print.blasso}} prints the \code{call} followed by a
brief summary of the MCMC run and a suggestion to try
the summary and plot commands.  

\code{\LinkA{plot.blasso}{plot.blasso}} uses an appropriate
\code{\LinkA{plot}{plot}} command on the \code{list} entries of the
\code{"blasso"}-class object thus
visually summarizing the samples from the posterior distribution of
each parameter in the model depending on the \code{which}
argument supplied.

\code{\LinkA{summary.blasso}{summary.blasso}} uses the \code{\LinkA{summary}{summary}} command
on the list entries of the \code{"blasso"}-class object thus
summarizing the samples from the posterior distribution of each
parameter in the model.

\code{\LinkA{print.summary.monomvn}{print.summary.monomvn}} calls \code{\LinkA{print.blasso}{print.blasso}}
on the \code{object} and then prints the result of
\code{\LinkA{summary.blasso}{summary.blasso}}
\end{Details}
\begin{Value}
\code{\LinkA{summary.blasso}{summary.blasso}} returns a \code{"summary.blasso"}-class
object, which is a \code{list} containing (a subset of) the items below.
The other functions do not return values.

\begin{ldescription}
\item[\code{B}] a copy of the input argument \code{thin} 
\item[\code{T}] total number of MCMC samples to be collected from \code{x\$T}

\item[\code{thin}] number of MCMC samples to skip before a sample is
collected (via thinning) from \code{x\$T} 
\item[\code{coef}] a joint \code{summary} of \code{x\$mu} and
the columns of \code{x\$beta}, the regression coefficients 
\item[\code{s2}] a \code{summary} of \code{x\$s2}, the variance parameter 
\item[\code{lambda2}] a \code{summary} of \code{x\$lambda2}, the penalty
parameter, when lasso or ridge regression is active 
\item[\code{lambda2}] a \code{summary} of \code{x\$gamma},
when the NG extensions to the lasso are used 
\item[\code{tau2i}] a \code{summary} of the columns of the latent
\code{x\$tau2i} parameters when lasso is active 
\item[\code{omega2}] a \code{summary} of the columns of the latent
\code{x\$omega2} parameters when Student-t errors are active 
\item[\code{nu}] a \code{summary} of \code{x\$nu}, the degrees of freedom
parameter, when the Student-t model is active 
\item[\code{bn0}] the estimated posterior probability that the individual
components of the regression coefficients \code{beta} is 
nonzero  
\item[\code{m}] a \code{summary} the model order \code{x\$m}: the
number of non-zero regression coefficients \code{beta} 
\item[\code{pi}] the estimated Binomial proportion in the prior for
the model order when 2-vector input is provided for
\code{mprior}

\end{ldescription}
\end{Value}
\begin{Author}\relax
Robert B. Gramacy \email{bobby@statslab.cam.ac.uk}
\end{Author}
\begin{References}\relax
\url{http://www.statslab.cam.ac.uk/~bobby/monomvn.html}
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{blasso}{blasso}}
\end{SeeAlso}

