The result from function bsl is saved as class ``BSL''.
Summarise a ``bsl'' class object.
Plot the univariate marginal posterior plot of a ``bsl'' class object.
# S4 method for bsl
show(object)summary(object, ...)
# S4 method for bsl
summary(object, thetaNames = NULL)
plot(x, y, ...)
# S4 method for bsl,missing
plot(x, which = 1L, thin = 1,
thetaTrue = NULL, options.plot = NULL,
top = "Approximate Univariate Posteriors", options.density = list(),
options.theme = list())
A ``bsl'' class object to be displayed.
Other arguments.
Parameter names to be shown in the summary table. If not given, parameter names of the ``bsl'' object will be used by default.
A ``bsl'' class object to plot.
Ignore.
An integer argument indicating which plot function to be used. The default, 1L, uses
the plain plot to visualise the result. 2L uses ggplot2 to draw the plot.
A numeric argument indicating the gap between samples to be taken when thinning the MCMC
draws. The default is 1, which means no thinning is used.
A set of values to be included on the plots as a reference line. The default is NULL.
A list of additional arguments to pass into the plot function. Only use when
which is 1L.
A character argument of the combined plot title if which is 2L.
A list of additional arguments to pass into the geom_density function. Only use
when which is 2L.
A list of additional arguments to pass into the theme function. Only use
when which is 2L.
A vector of the number of simulations per iteration, acceptance rate of the Markov chain annd scaled effective sample size for each parameter.
show: Display the basic information of a ``bsl'' object. See show.bsl.
summary: Summarise a bsl class object. See summary.bsl.
plot: Plot the univariate marginal posterior plot of a ``bsl'' class object. See plot.bsl.
thetaObject of class ``matrix''. MCMC samples from the joint approximate posterior distribution of the parameters.
loglikeObject of class ``numeric''. Accepted MCMC samples of the estimated log-likelihood values.
callObject of class ``call''. The original code that was used to call the method.
modelObject of class ``BSLMODEL''.
acceptanceRateObject of class ``numeric''. The acceptance rate of the MCMC algorithm.
earlyRejectionRateObject of class ``numeric''. The early rejection rate of the algorithm (early rejection may occur when using bounded prior distributions).
errorRateObject of class ``numeric''. The error rate. If any infinite summary statistic or positive infinite loglike occurs during the process, it is marked as an error and the proposed parameter will be rejected.
yObject of class ``ANY''. The observed data.
nObject of class ``numeric''. The number of simulations from the model per MCMC iteration.
MObject of class ``numeric''. The number of MCMC iterations.
covRandWalkObject of class ``matrix''. The covariance matrix used in multivariate normal random walk proposals.
methodObject of class ``character''. The character argument indicating the used method.
shrinkageObject of class ``characterOrNULL''. The character argument indicating the shrinkage method.
penaltyObject of class ``numericOrNULL''. The penalty value.
GRCObject of class ``logical''. Whether the Gaussian rank correlation matrix is used.
logitTransformObject of class ``logical''. The logical argument indicating whether a logit transformation is used in the algorithm.
logitTransformBoundObject of class ``matrixOrNULL''. The matrix of logitTransformBound.
standardiseObject of class ``logical''. The logical argument that determines whether to standardise the summary statistics.
parallelObject of class ``logical''. The logical value indicating whether parallel computing is used in the process.
parallelArgsObject of class ``listOrNULL''. The list of additional arguments to pass into the foreach function.
timeObject of class ``difftime''. The running time.