cgeneric_generic0: Build an 'inla.cgeneric' to implement a model whose precision...

In graphpcor: Models for Correlation Matrices Based on Graphs

Build an inla.cgeneric to implement a model whose precision has a conditional precision parameter. See details. This uses the cgeneric interface that can be used as a model in a INLA f() model component.

Description

Build an inla.cgeneric to implement a model whose precision has a conditional precision parameter. See details. This uses the cgeneric interface that can be used as a model in a INLA f() model component.

Usage

cgeneric_generic0(
  R,
  param,
  constr = TRUE,
  scale = TRUE,
  debug = FALSE,
  useINLAprecomp = TRUE,
  libpath = NULL
)

cgeneric_iid(
  n,
  param,
  constr = FALSE,
  scale = TRUE,
  debug = FALSE,
  useINLAprecomp = TRUE,
  libpath = NULL
)

Arguments

R	the structure matrix for the model definition.
param	length two vector with the parameters a and p for the PC-prior distribution defined from P(\sigma > a) = p where \sigma can be interpreted as marginal standard deviation of the process if scale = TRUE. See details.
constr	logical indicating if it is to add a sum-to-zero constraint. Default is TRUE.
scale	logical indicating if it is to scale the mnodel. See detais.
debug	integer, default is zero, indicating the verbose level. Will be used as logical by INLA.
useINLAprecomp	logical, default is TRUE, indicating if it is to be used the shared object pre-compiled by INLA. This is not considered if 'libpath' is provided.
libpath	string, default is NULL, with the path to the shared object.
n	size of the model

Details

The precision matrix is defined as

Q = \tau R

where the structure matrix R is supplied by the user and \tau is the precision parameter. Following Sørbie and Rue (2014), if scale = TRUE the model is scaled so that

Q = \tau s R

where s is the geometric mean of the diagonal elements of the generalized inverse of R.

s = \exp{\sum_i \log((R^{-})_{ii})/n}

If the model is scaled, the geometric mean of the marginal variances, the diagonal of Q^{-1}, is one. Therefore, when the model is scaled, \tau is the marginal precision, otherwise \tau is the conditional precision.

Value

a inla.cgeneric, cgeneric() object.

Functions

• cgeneric_iid(): The cgeneric_iid() uses the cgeneric_generic0 with the structure matrix as the identity.

References

Sigrunn Holbek Sørbye and Håvard Rue (2014). Scaling intrinsic Gaussian Markov random field priors in spatial modelling. Spatial Statistics, vol. 8, p. 39-51.

graphpcor documentation built on June 8, 2025, 10:37 a.m.