bayesparams: Parameters for the semi-parametric approach In tsxtreme: Bayesian Modelling of Extremal Dependence in Time Series

Description

Create, test or show objects of class "bayesparams".

Usage

 ```1 2 3 4 5 6 7 8``` ```bayesparams(prop.a = 0.02, prop.b = 0.02, prior.mu = c(0, 10), prior.nu = c(2, 1/2), prior.eta = c(2, 2), trunc = 100, comp.saved = 15, maxit = 30000, burn = 5000, thin = 1, adapt = 5000, batch.size = 125, mode = 1) is.bayesparams(x) ```

Arguments

 ```prop.a, prop.b``` standard deviation for the Gaussian proposal of the Heffernan–Tawn parameters. `prior.mu` mean and standard deviation of the Gaussian prior for the components' means. `prior.nu` shape and rate of the inverse gamma prior for the components' variances. `prior.eta` shape and scale of the gamma prior for the precision parameter of the Dirichlet process. `trunc` integer; value of the truncation for the approximation of the infinite sum in the stick-breaking representation. `comp.saved` number of first components to be saved and returned. `maxit` maximum number of iterations. `burn` number of first iterations to discard. `thin` positive integer; spacing between iterations to be saved. Default is 1, i.e., all iterations are saved. `adapt` integer; number of iterations during which an adaption algorithm is applied to the proposal variances of α and β. `batch.size` size of batches used in the adaption algorithm. It has no effect if `adapt==0`. `mode` verbosity; 0 for debug mode, 1 (default) for standard output, and 2 for silent. `x` an arbitrary R object.

Details

`prop.a` is a vector of length 5 with the standard deviations for each region of the RAMA for the (Gaussian) proposal for α. If a scalar is given, 5 identical values are assumed.

`prop.b` is a vector of length 3 with the standard deviations for each region of the RAMA for the (Gaussian) proposal for β. If a scalar is provided, 3 identical values are assumed.

`comp.saved` has no impact on the calculations: its only purpose is to prevent from storing huge amounts of empty components.

The regional adaption scheme targets a 0.44 acceptance probability. It splits [-1;1] in 5 regions for α and [0;1] in 3 regions for β. The decision to increase/decrease the proposal standard deviation is based on the past `batch.size` MCMC iterations, so too low values yield inefficient adaption, while too large values yield slow adaption.

Default values for the hyperparameters are chosen to get reasonably uninformative priors.

`bayesfit`, `depmeasure`
 ```1 2 3``` ```is.bayesparams(bayesparams()) # TRUE ## use defaults, change max number of iteration of MCMC par <- bayesparams(maxit=1e5) ```