This function fits the model by Leroux et al. for a given value
of the parameter lambda, i.e., the mixture parameter that
appears in the variance..
leroux.inla(formula, d, W, lambda, improve = TRUE, fhyper = NULL, ...)Formula of the fixed effects.
A data.frame with the data to be used.
Adjacency matrix.
Parameter used in the mixture of the two precission matrices.
Logical. Whether to improve the fitted models to obtain better estimates of the marginal likelihoods.
Extra arguments passed to the definition of the hyperparameters.
Extra arguments passed to function inla.
An INLA object.
This function fits the model proposed by Leroux et al. (1999)
for a given value of parameter lambda. This parameter
controls the mixture between a diagonal precission (lambda=1)
and an intrinsic CAR precission (lambda=0).
The marginal log-likelihood is corrected to add half the log-determinant of the precission matrix.
Leroux B, Lei X, Breslow N (1999). Estimation of Disease Rates in Small Areas: A New Mixed Model for Spatial Dependence. In M Halloran, D Berry (eds.), Statistical Models in Epidemiology, the Environment and Clinical Trials, pp. 135-178. Springer-Verlag, New York.
Roger S. Bivand, Virgilio G<U+000ED97A>-Rubio, H<e5>vard Rue (2014). Approximate Bayesian inference for spatial econometrics models. Spatial Statistics, Volume 9, 146-165.
Roger S. Bivand, Virgilio G<U+000ED97A>-Rubio, H<e5>vard Rue (2015). Spatial Data Analysis with R-INLA with Some Extensions. Journal of Statistical Software, 63(20), 1-31. URL http://www.jstatsoft.org/v63/i20/.