R/leroux.R

In INLABMA: Bayesian Model Averaging with INLA

#
#Code to fit some the model by Leroux et al. (1999)
#


#Simultaneous Autorregresive Model
#
#Return an INLA model
#Formula is for the FIXED effects ONLY
#'...' are passed to the INLA call
#areaid is the column to be used as area index in the random spatial effect
#

#W is a binary adjacency matrix
#lambda is the parameter in the mixture of precision matrices
#

leroux.inla<-function(formula, d, W, lambda, improve=TRUE, fhyper=NULL, ...)
{

	W2<-diag(apply(W, 1, sum))-W
	#Q<-lambda*diag(nrow(W))+(1-lambda)*W2
	Q<-(1-lambda)*diag(nrow(W))+lambda*W2

	#This is a fix to be able to use improve=TRUE later
	#environment(formula)<-environment()
	assign("Q", Q, environment(formula) )

	if(is.null(fhyper))
	{
	formula<-update(formula, 
	  . ~ . + f(idx, model="generic0", Cmatrix =Q) )
	}
	else
	{
	formula<-update(formula, 
	  . ~ . + f(idx, model="generic0", Cmatrix =Q, hyper=fhyper) )
	}


	res <- INLA::inla(formula, data=d, ...)


	if(improve)
		res <- INLA::inla.rerun(res)

	#Compute log-determinat to correct the marginal-loglikelihood
	res$logdet<-as.numeric(Matrix::determinant(Q)$modulus)
	res$mlik<-res$mlik+res$logdet/2

	return(res)
}