bayesparams {tsxtreme}

Parameters for the semi-parametric approach

Description

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

Usage

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 \alpha and \beta.

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 \alpha. 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 \beta. 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 \alpha and [0;1] in 3 regions for \beta. 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.

See Also

bayesfit, depmeasure

Examples

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