Samples new coefficients via Gibbs sampling in a spectral GP object
following the Gibbs sampling scheme of Wikle (2002), which involves an
extra variance component (`sig2e`

and a noisy version of the
process (`z`

).

1 2 3 | ```
## S3 method for class 'gp'
Gibbs.sample.coeff(object, z, sig2e, meanVal=0,
sdVal=1,returnHastings=FALSE, ...)
``` |

`object` |
A GP object, created by |

`z` |
Vector of values for |

`sig2e` |
Noise variance component that distorts |

`meanVal` |
Optional mean value for |

`sdVal` |
Optional standard deviation value for |

`returnHastings` |
Optional argument telling whether to return the logdensity of the proposal for use in a Metropolis-Hastings correction calculation. |

`...` |
Other arguments. |

This function can be used in an MCMC context to take Gibbs samples
of the process coefficients, as part of the algorithm of Wikle
(2002). The function modifies the GP object, updating the `coeff`

and
`process`

components.

The function modifies the GP object, which is essentially a pointer
(an R environment in this case), so NULL is returned, unless `returnHastings=TRUE`

.

Christopher Paciorek paciorek@alumni.cmu.edu

Type 'citation("spectralGP")' for references.

`gp`

, `propose.coeff.gp`

, `updateprocess.gp`

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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