This auxiliary function defines prior options for
pois.krige.bayes and binom.krige.bayes.
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)prior distribution for the mean (vector) parameter \(\beta\). The options are "flat" (default),
"normal" or "fixed".
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.
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"'.
prior distribution for the parameter \(\sigma^2\). The options are "uniform" (default),
"sc.inv.chisq", "reciprocal" (gives improper posterior), or "fixed".
fixed value of the parameter \(\sigma^2\) when
sigmasq.prior = "fixed". Parameter \(S^2_{\sigma}\) in the scaled inverse-\(\chi^2\) prior distribution for \(\sigma^2\).
parameter \(n_{\sigma}\) in the scaled inverse-\(\chi^2\) prior distribution for \(\sigma^2\).
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.
fixed value of the parameter \(\phi\) when
phi.prior = "fixed". Mean of the prior distribution when phi.prior = "exponential".
support points for the discretisation of the prior for the parameter \(\phi\).
the value of the relative nugget parameter
\(\tau_R^2\). Default is tausq.rel = 0.
A list with processed arguments to be passed to the main function.