cgeneric for treepcor())This set the necessary data to implement the penalized complexity prior for a correlation matrix considering a three as proposed in Sterrantino et. al. 2025
cgeneric_treepcor(
graph,
lambda,
sigma.prior.reference,
sigma.prior.probability,
debug = FALSE,
useINLAprecomp = TRUE,
libpath = NULL
)a inla.cgeneric, cgeneric() object.
object of class treepcor for the model specification.
the lambda parameter for the graph correlation prior.
a vector with the reference values to define the prior for the standard deviation parameters.
a vector with the probability values to define the prior for the standard deviation parameters.
integer, default is zero, indicating the verbose level. Will be used as logical by INLA.
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.
string, default is NULL, with the path to the shared object.
The correlation prior as in the paper depends on the lambda value.
The prior for each \(sigma_i\) is the Penalized-complexity prior
which can be defined from the following probability statement
P(sigma > U) = a.
where "U" is a reference value and "a" is a probability.
The values "U" and probabilities "a" for each \(sigma_i\)
are passed in the sigma.prior.reference and sigma.prior.probability
arguments.
If a=0 then U is taken to be the fixed value of the corresponding sigma.
E.g. if there are three sigmas in the model and one supply
sigma.prior.reference = c(1, 2, 3) and
sigma.prior.probability = c(0.05, 0.0, 0.01)
then the sigma is fixed to 2 and not estimated.
treepcor() and cgeneric()