dlmGibbsDIG {dlm}R Documentation

Gibbs sampling for d-inverse-gamma model

Description

The function implements a Gibbs sampler for a univariate DLM having one or more unknown variances in its specification.

Usage

dlmGibbsDIG(y, mod, a.y, b.y, a.theta, b.theta, shape.y, rate.y,
            shape.theta, rate.theta, n.sample = 1,
            thin = 0, ind, save.states = TRUE)

Arguments

y data vector or univariate time series
mod a dlm for univariate observations
a.y prior mean of observation variance
b.y prior variance of observation variance
a.theta prior mean of system variances
b.theta prior variance of system variances
shape.y shape parameter of the prior of observation variance
rate.y rate parameter of the prior of observation variance
shape.theta shape parameter of the prior of system variances
rate.theta rate parameter of the prior of system variances
n.sample requested number of Gibbs iterations
thin discard thin iterations for every saved iteration
ind indicator of the system variances that need to be estimated
save.states should the simulated states be included in the output

Details

The d-inverse-gamma model is a constant univariate DLM with unknown observation variance, diagonal system variance with unknown diagonal entries. Some of these entries may be known, in which case they are typically zero. Independent inverse gamma priors are assumed for the unknown variances. These can be specified be mean and variance or, alternatively, by shape and rate. Recycling is applied for the prior parameters of unknown system variances. The argument ind can be used to specify the index of the unknown system variances, in case some of the diagonal elements of W are known. The unobservable states are generated in the Gibbs sampler and are returned if save.states = TRUE. For more details on the model and usage examples, see the package vignette.

Value

The function returns a list of simulated values.

dV simulated values of the observation variance.
dW simulated values of the unknown diagonal elements of the system variance.
theta simulated values of the state vectors.

Author(s)

Giovanni Petris GPetris@uark.edu

Examples

## See the package vignette for an example

[Package dlm version 0.99-0 Index]