R: Defines prior options

prior.glm.control {geoRglm}

R Documentation

Defines prior options

Description

This auxiliary function defines prior options for pois.krige.bayes and binom.krige.bayes.

Usage

prior.glm.control(beta.prior = c("flat", "normal", "fixed"),
              beta = NULL, beta.var.std = NULL,
              sigmasq.prior = c("uniform", "sc.inv.chisq", "reciprocal", "fixed"),
              sigmasq = NULL, df.sigmasq = NULL,
              phi.prior = c("uniform", "exponential","fixed",
                            "squared.reciprocal", "reciprocal"),
              phi = NULL, phi.discrete = NULL,
              tausq.rel = 0)

Arguments

`beta.prior`	prior distribution for the mean (vector) parameter `\beta`. The options are `"flat"` (default), `"normal"` or `"fixed"`.
`beta`	hyper-parameter for the prior distribution of the mean (vector) parameter `\beta`. Only used if `beta.prior = "normal"` or `beta.prior = "fixed"`. For the latter `beta` defines the value of the known mean.
`beta.var.std`	standardised (co)variance hyperparameter(s) for the prior for the mean (vector) parameter beta. The (co)variance matrix for beta is given by the multiplication of this matrix by `sigma^2`. Only used if 'beta.prior = "normal"'.
`sigmasq.prior`	prior distribution for the parameter `\sigma^2`. The options are `"uniform"` (default), `"sc.inv.chisq"`, `"reciprocal"` (gives improper posterior), or `"fixed"`.
`sigmasq`	fixed value of the parameter `\sigma^2` when `sigmasq.prior = "fixed"`. Parameter `S^2_{\sigma}` in the scaled inverse-`\chi^2` prior distribution for `\sigma^2`.
`df.sigmasq`	parameter `n_{\sigma}` in the scaled inverse-`\chi^2` prior distribution for `\sigma^2`.
`phi.prior`	prior distribution for the range parameter `\phi`. Options are: `"uniform"` (`\propto 1`), `"exponential"` (`\exp(-\nu * \phi)`), `"fixed"` (known value of `\phi`), `"squared.reciprocal"` (`1/\phi^2`), `"reciprocal"` (`1/\phi`). Alternativelly, a user defined discrete distribution can be specified by providing a vector of probabilities. These probabilities corrresponds to a prior distribution with support `phi.discrete`. If the `"fixed"` the argument `\phi` should be provided and it is regarded as fixed when performing predictions.
`phi`	fixed value of the parameter `\phi` when `phi.prior = "fixed"`. Mean of the prior distribution when `phi.prior = "exponential"`.
`phi.discrete`	support points for the discretisation of the prior for the parameter `\phi`.
`tausq.rel`	the value of the relative nugget parameter `\tau_R^2`. Default is `tausq.rel = 0`.

Value

A list with processed arguments to be passed to the main function.

Author(s)