Mmodel_lcar {bigDM}R Documentation

Leroux et al. (1999) multivariate CAR latent effect


M-model implementation of the Leroux et al. (1999) multivariate CAR latent effect with different spatial smoothing parameters using the rgeneric model of INLA.


  cmd = c("graph", "Q", "mu", "initial", "log.norm.const", "log.prior", "quit"),
  theta = NULL



Internal functions used by the rgeneric model to define the latent effect.


Vector of hyperparameters.


This function considers a Leroux et al. (1999) CAR prior (denoted as LCAR) for the spatial latent effects of the different diseases and introduces correlation between them using the M-model proposal of Botella-Rocamora et al. (2015). Putting the spatial latent effects for each disease in a matrix, the between disease dependence is introduced through the M matrix as \Theta=\Phi M, where the columns of \Phi follow a LCAR prior distribution (within-disease correlation). A Wishart prior for the between covariance matrix M'M is considered using the Bartlett decomposition. Uniform prior distributions on the interval [alpha.min, alpha.max] are considered for all the spatial smoothing parameters.

The following arguments are required to be defined before calling the functions:


This is used internally by the INLA::inla.rgeneric.define() function.


The M-model implementation of this model using R-INLA requires the use of J \times (J+3)/2 hyperparameters. So, the results must be carefully checked.


Botella-Rocamora P, Martinez-Beneito MA, Banerjee S (2015). “A unifying modeling framework for highly multivariate disease mapping.” Statistics in Medicine, 34(9), 1548–1559. doi:10.1002/sim.6423.

Leroux BG, Lei X, Breslow N (1999). “Estimation of disease rates in small areas: A new mixed model for spatial dependence.” In Halloran ME, Berry D (eds.), Statistical Models in Epidemiology, the Environment, and Clinical Trials, 179–191. Springer-Verlag: New York.

[Package bigDM version 0.5.2 Index]