CV: Multivariate mortality data from Comunidad Valenciana (Spain)

Description

Simulated multivariate mortality data from Comunidad Valenciana (Spain). The data set contains (simulated) observed and expected deaths for Cirrhosis, Lung cancer and Cirrhosis for the Valencian municipalities. The supplied data have been simulated mimicking the original data set which has privacy restrictions. Additional details on the generation of the supplied dataset can be found at the original book.

Usage

data(CV)

Arguments

Format

A SpatialPolygonsDataFrame with the boundaries of the municipalities in Comunidad Valenciana with the following columns:

CODMUNI: Municipality code.
NOMBRE: Name of the municipality.
Exp.Cirrhosis: Expected number of cases of cirrhosis.
Exp.Lung: Expected number of cases of lung cancer.
Exp.Oral: Expected number of cases of oral cavity cancer.
Obs.Cirrhosis: Observed number of cases of cirrhosis.
Obs.Lung: Observed number of cases of lung cancer.
Obs.Oral: Observed number of cases of oral cavity cancer.

References

Martinez-Beneito, M A & Botella Rocamora, P. Disease mapping: from foundations to multidimensional modeling. CRC/Chapman & Hall, 2019.

Examples

Run this code

# NOT RUN {
# }
# NOT RUN {
if(require(INLA, quietly = TRUE)) {
require(sp)
require(spdep)
data(CV)
W <- as(nb2mat(CV.nb, style = "B"), "Matrix")

#Data (two diseases only)
d <- list(OBS = c(CV$Obs.Cirrhosis, CV$Obs.Lung),
 EXP = c(CV$Exp.Cirrhosis, CV$Exp.Lung))

 # Index for latent effect
d$idx <- 1:length(d$OBS)

k <- 2  #Number of diseases

# Linear constraint for models
A <- kronecker(Diagonal(k, 1), Matrix(1, ncol = nrow(W), nrow = 1))
e = rep(0, k)

# Two independent ICAR models
#model <- inla.rgeneric.define(inla.rgeneric.indep.IMCAR.model,
#  k = k, W = W)
model <- inla.INDIMCAR.model(k = k, W = W)
r.simcar <- try(
  inla(OBS ~ 1 + f(idx, model = model, extraconstr = list(A = as.matrix(A), e = e)),
    data = d, E = EXP, family = "poisson",
     # To run faster, REMOVE in real applications
     control.mode = list(theta = c(1.4, 2.1), restart = TRUE),
    control.predictor = list(compute = TRUE))
)
summary(r.simcar)

# IMCAR model
#model <- inla.rgeneric.define(inla.rgeneric.IMCAR.model,
#  k = k, W = W, alpha.min = 0, alpha.max = 1)
model <- inla.IMCAR.model(k = k, W = W)
r.imcar <- try(
  inla(OBS ~ 1 + f(idx, model = model, extraconstr = list(A = as.matrix(A), e = e)),
    data = d, E = EXP, family = "poisson",
     # To run faster, REMOVE in real applications
     control.mode = list(theta = c(1.77, 2.01, 0.93),
       restart = TRUE),
    control.compute = list(config = TRUE),
    control.predictor = list(compute = TRUE))
)
summary(r.imcar)

# Transform parameters
summary.post <- inla.MCAR.transform(r.imcar, k = k)

# Posterior of variance matrix
summary.post$VAR.p # Using point estimates
summary.post$VAR.m # Using posterior sampling

} #if(require(INLA))
# }