cgeneric_generic0 {INLAtools} | R Documentation |
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
where |
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 |
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
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)