leroux.inla {INLABMA} | R Documentation |
Fit Leroux et al's spatial model.
Description
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..
Usage
leroux.inla(formula, d, W, lambda, improve = TRUE, fhyper = NULL, ...)
Arguments
formula |
Formula of the fixed effects. |
d |
A data.frame with the data to be used. |
W |
Adjacency matrix. |
lambda |
Parameter used in the mixture of the two precission matrices. |
improve |
Logical. Whether to improve the fitted models to obtain better estimates of the marginal likelihoods. |
fhyper |
Extra arguments passed to the definition of the hyperparameters. |
... |
Extra arguments passed to function |
Details
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.
Value
An INLA object.
Author(s)
Virgilio Gómez-Rubio <virgilio.gomez@uclm.es>
References
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 Gómez-Rubio, Hĺvard Rue (2014). Approximate Bayesian inference for spatial econometrics models. Spatial Statistics, Volume 9, 146-165.
Roger S. Bivand, Virgilio Gómez-Rubio, Hĺvard 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/.