This is the main function for selecting the shrinkage (graphical lasso or Warton's estimation) penalty parameter
for method BSL or semiBSL based on a point estimate of the parameters. Parallel computing is supported with the R package
foreach.
selectPenalty(ssy, n, lambda_all, M, sigma, model, theta = model@theta0,
method = c("BSL", "semiBSL")[1], shrinkage = c("glasso",
"Warton")[1], standardise = FALSE, GRC = FALSE,
parallelSim = FALSE, parallelSimArgs = NULL, parallelMain = FALSE,
verbose = TRUE, fnSim, fnSum, simArgs, sumArgs)A summary statistic vector for the observed data.
A vector of possible values of n, the number of simulations from the model per MCMC iteration for estimating the synthetic likelihood.
A list, with each entry containing the vector of penalty values to test for the corresponding choice of n.
The number of repeats to use in estimating the standard deviation of the estimated log synthetic likelihood.
The standard deviation of the log synthetic likelihood estimator to aim for, usually a value between 1 and 2. This reflects the mixing of a Markov chain.
A ``BSLMODEL'' object generated with function BSLModel. See BSLModel.
A point estimate of the parameter value which all of the simulations will be based on.
A string argument indicating the method to be used. The default, ``BSL'', runs BSL. ``semiBSL'' runs the semi-parametric BSL algorithm and is more robust to non-normal summary statistics.
A string argument indicating which shrinkage method to be used. Current options are ``glasso'' for the graphical lasso method of Friedman et al (2008) and ``Warton'' for the ridge regularisation method of Warton (2008).
A logical argument that determines whether to standardise the summary statistics before applying
the graphical lasso. This is only valid if method is ``BSL'', shrinkage is ``glasso'' and penalty is not NULL. The
diagonal elements will not be penalised if the shrinkage method is ``glasso''. The default is FALSE.
A logical argument indicating whether the Gaussian rank correlation matrix (Boudt et al., 2012)
should be used to estimate the covariance matrix in ``BSL'' method. The default is FALSE, which uses the
sample covariance by default.
A logical value indicating whether parallel computing should be used for simulation and summary statistic evaluation. Default is FALSE.
A list of additional arguments to pass into the foreach function. Only used when parallelSim is TRUE, default is NULL.
A logical value indicating whether parallel computing should be used to computing the graphical lasso function. Default is FALSE.
A logical argument indicating whether the iteration numbers (1:M) should be printed to track progress. The default is FALSE.
Deprecated, will be removed in the future, use model instead. A function that simulates data
for a given parameter value. The first argument should be the parameters. Other necessary arguments (optional) can be
specified with simArgs.
Deprecated, will be removed in the future, use model instead. A function for computing summary
statistics of data. The first argument should be the observed or simulated dataset. Other necessary arguments (optional)
can be specified with sumArgs.
Deprecated, will be removed in the future, use model instead. A list of additional arguments
to pass into the simulation function. Only use when the input fnSim requires additional arguments. The default is
NULL.
Deprecated, will be removed in the future, use model instead. A list of additional arguments
to pass into the summary statistics function. Only use when the input fnSum requires additional arguments. The
An object of class penbsl is returned, containing the following components:
resultsDF: A data frame containing the following:
n: The choices of n that were specified.
penalty: The choices of the penalty that were specified.
sigma: The standard deviation of the log synthetic likelihood estimator under the above choices.
sigmaOpt: An indicator of whether it was the closest sigma to the desired one for each choice of n.
call: The original code that was used to call the method.
The functions print() and plot() are both available for types of class penbsl.
An, Z., South, L. F., Nott, D. J. & Drovandi, C. C. (2019). Accelerating Bayesian synthetic likelihood with the graphical lasso. Journal of Computational and Graphical Statistics. https://doi.org/10.1080/10618600.2018.1537928
Warton, D. I. (2008). Penalized Normal Likelihood and Ridge Regularization of Correlation and Covariance Matrices, Journal of the American Statistical Association. https://doi.org/10.1198/016214508000000021
ma2, cell and mgnk for examples.
bsl for a function to run BSLasso after selecting the tuning parameter
and penbsl for functions related to visualisation.