blasso.s3 {monomvn} | R Documentation |
Summarizing, printing, and plotting the contents of a
"blasso"
-class object containing samples from
the posterior distribution of a Bayesian lasso model
## 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", "tau2i", "omega2", "nu", "m", "pi"), subset = NULL, burnin = 0, ... ) ## S3 method for class 'summary.blasso': print(x, ...)
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$T |
which |
indicates the parameter whose characteristics
should be plotted; does not apply to the summary |
... |
passed to print.blasso , or
plot.default |
print.blasso
prints the call
followed by a
brief summary of the MCMC run and a suggestion to try
the summary and plot commands.
plot.blasso
uses an appropriate
plot
command on the list
entries of the
"blasso"
-class object thus
visually summarizing the samples from the posterior distribution of
each parameter in the model depending on the which
argument supplied.
summary.blasso
uses the summary
command
on the list entries of the "blasso"
-class object thus
summarizing the samples from the posterior distribution of each
parameter in the model.
print.summary.monomvn
calls print.blasso
on the object
and then prints the result of
summary.blasso
summary.blasso
returns a "summary.blasso"
-class
object, which is a list
containing (a subset of) the items below.
The other functions do not return values.
B |
a copy of the input argument thin |
T |
total number of MCMC samples to be collected from x$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$lambda2 , the penalty
parameter, when lasso or ridge regression is active |
tau2i |
a summary of the columns of the latent
x$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
|
Robert B. Gramacy bobby@statslab.cam.ac.uk
http://www.statslab.cam.ac.uk/~bobby/monomvn.html