BSLclass {BSL}  R Documentation 
S4 class “BSL”.
Description
The S4 class “BSL” is produced by running function
bsl
and contains the result of a BSL run. Basic S4 methods
show
, summary
and plot
are provided. theta
and
loglike
returns the MCMC samples of parameter values and estimated
loglikelihoods.
Usage
## S4 method for signature 'BSL'
show(object)
## S4 method for signature 'BSL'
summary(object, burnin = 0, thetaNames = NULL)
## S4 method for signature 'BSL,ANY'
plot(
x,
which = 1L,
thin = 1,
burnin = 0,
thetaTrue = NULL,
options.plot = NULL,
top = "Approximate Univariate Posteriors",
options.density = list(),
options.theme = list()
)
## S4 method for signature 'BSL'
getTheta(object, burnin = 0, thin = 1)
## S4 method for signature 'BSL'
getLoglike(object, burnin = 0, thin = 1)
## S4 method for signature 'BSL'
getGamma(object, burnin = 0, thin = 1)
Arguments
object 
A “BSL” class object. 
burnin 
the number of MCMC burnin steps to be taken. 
thetaNames 
Parameter names to be shown in the summary table. If not given, parameter names of the “BSL” object will be used by default. 
x 
A “BSL” class object to plot. 
which 
An integer argument indicating which plot function to be
used. The default, 
thin 
A numeric argument indicating the gap between samples to
be taken when thinning the MCMC draws. The default is 
thetaTrue 
A set of true parameter values to be included on the plots
as a reference line. The default is 
options.plot 
A list of additional arguments to pass into the

top 
A character argument of the combined plot title if

options.density 
A list of additional arguments to pass into the

options.theme 
A list of additional arguments to pass into the

Slots
theta
Object of class “matrix”. MCMC samples from the joint approximate posterior distribution of the parameters.
loglike
Object of class “numeric”. Accepted MCMC samples of the estimated loglikelihood values.
call
Object of class “call”. The original code that was used to call the method.
model
Object of class “MODEL”.
acceptanceRate
Object of class “numeric”. The acceptance rate of the MCMC algorithm.
earlyRejectionRate
Object of class “numeric”. The early rejection rate of the algorithm (early rejection may occur when using bounded prior distributions).
errorRate
Object of class “numeric”. The error rate. If any infinite summary statistic or infinite loglikelihood estimate occurs during the process, it is marked as an error and the proposed parameter will be rejected.
y
Object of class “ANY”. The observed data.
n
Object of class “numeric”. The number of simulations from the model per MCMC iteration to estimate the synthetic likelihood.
M
Object of class “numeric”. The number of MCMC iterations.
covRandWalk
Object of class “matrix”. The covariance matrix used in multivariate normal random walk proposals.
method
Object of class “character”. The character argument indicating the used method.
shrinkage
Object of class “characterOrNULL”. The character argument indicating the shrinkage method.
penalty
Object of class “numericOrNULL”. The penalty value.
GRC
Object of class “logical”. Whether the Gaussian rank correlation matrix is used.
logitTransform
Object of class “logical”. The logical argument indicating whether a logit transformation is used in the algorithm.
logitTransformBound
Object of class “matrixOrNULL”. The matrix of logitTransformBound.
standardise
Object of class “logical”. The logical argument that determines whether to standardise the summary statistics.
parallel
Object of class “logical”. The logical value indicating whether parallel computing is used in the process.
parallelArgs
Object of class “listOrNULL”. The list of additional arguments to pass into the
foreach
function.time
Object of class “difftime”. The running time.
gamma
Object of class “numeric”. MCMC samples of gamma parameter values of the mean adjustment or variance inflation for method “BSLmisspec”.
misspecType
Object of class “characterOrNULL”. The character argument indicating whether mean adjustment ("mean") or variance inflation ("variance") to be used in "BSLmisspec" method.
tau
Object of class “numeric”. Parameter of the prior distribution for "BSLmisspec" method. For mean adjustment,
tau
is the scale of the Laplace distribution. For variance inflation,tau
is the mean of the exponential distribution.whitening
Object of class “logicalOrMatrixOrNULL”. A logical argument determines whether Whitening transformation is used in “BSL” method with Warton's shrinkage, or just the Whitening matrix used.
Examples
## Not run:
# a toy example
toy_simVec < function(n, theta) matrix(rnorm(n, theta), nrow = n) # the simulation function
toy_sum < function(x) x # the summary statistic function
model < newModel(fnSimVec = toy_simVec, fnSum = toy_sum, theta0 = 0) # create the model object
result_toy < bsl(y = 1, n = 100, M = 1e4, model = model, covRandWalk = matrix(1))
summary(result_toy)
plot(result_toy)
## End(Not run)