## An example with three independent AR(1)'s with separate means, but
## with the same hyperparameters. These are observed with three
## different likelihoods.
n = 100
x1 = arima.sim(n=n, model=list(ar=c(0.9))) + 0
x2 = arima.sim(n=n, model=list(ar=c(0.9))) + 1
x3 = arima.sim(n=n, model=list(ar=c(0.9))) + 2
## Binomial observations
Nt = 10 + rpois(n,lambda=1)
y1 = rbinom(n, size=Nt, prob = exp(x1)/(1+exp(x1)))
## Poisson observations
Ep = runif(n, min=1, max=10)
y2 = rpois(n, lambda = Ep*exp(x2))
## Gaussian observations
y3 = rnorm(n, mean=x3, sd=0.1)
## stack these in a 3-column matrix with NA's where not observed
y = matrix(NA, 3*n, 3)
y[1:n, 1] = y1
y[n + 1:n, 2] = y2
y[2*n + 1:n, 3] = y3
## define the model
r = c(rep(1,n), rep(2,n), rep(3,n))
rf = as.factor(r)
i = rep(1:n, 3)
formula = y ~ f(i, model="ar1", replicate=r, constr=TRUE) + rf -1
data = data.frame(y, i, r, rf)
## parameters for the binomial and the poisson
Ntrial = rep(NA, 3*n)
Ntrial[1:n] = Nt
E = rep(NA, 3*n)
E[1:n + n] = Ep
result = inla(formula, family = c("binomial", "poisson", "normal"),
data = data, Ntrial = Ntrial, E = E,
control.family = list(
list(),
list(),
list(initial=0)))
gives
Error in eval(expr, envir, enclos) : object 'X0.975quant' not found
gives