Description Usage Arguments Details See Also Examples
Create, test or show objects of class "bayesparams".
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)
|
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 |
mode |
verbosity; 0 for debug mode, 1 (default) for standard output, and 2 for silent. |
x |
an arbitrary R object. |
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.
1 2 3 | is.bayesparams(bayesparams()) # TRUE
## use defaults, change max number of iteration of MCMC
par <- bayesparams(maxit=1e5)
|
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