cgeneric_generic0: Build an 'cgeneric' object for a 'generic0' model. See...

In INLAtools: Functionalities for the 'INLA' Package

Build an cgeneric object for a generic0 model. See details.

Description

Build data needed to implement a model whose precision has a conditional precision parameter. This uses the C interface in the 'INLA' package, that can be used as a linear predictor model component with an 'f' term.

Usage

cgeneric_generic0(R, param, constr = TRUE, scale = TRUE, ...)

cgeneric_iid(n, param, constr = FALSE, ...)

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 model. See detais.
...	arguments (debug,useINLAprecomp,libpath) passed on to cgeneric().
n	integer required to specify the model size

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 cgeneric object, see cgeneric().

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.

See Also

prior.cgeneric()

Examples

## structured precision matrix model definition
R <- Matrix(toeplitz(c(2,-1,0,0,0)))
R
mR <- cgeneric("generic0", R = R,
  param = c(1, 0.05), scale = FALSE)
graph(mR)
prec(mR, theta = 0)

INLAtools documentation built on June 23, 2025, 5:09 p.m.