Fit Besag Small Area Estimation model using tmbstan.

Random effects have an improper conditional autoregressive (ICAR) distribution with (generalised) precision matrix produced using the nb_to_precision function with input nb, the neighbourhood structure of sf.

Usage

besag_tmbstan(
  sf,
  nsim_warm = 100,
  nsim_iter = 1000,
  chains = 4,
  cores = parallel::detectCores(),
  ii = NULL
)

Arguments

sf

A simple features object with some geometry.

nsim_warm

Number of warmup samples, passed to rstan.

nsim_iter

Number of samples, passed to rstan.

chains

Number of chains, each of which gets nsim_warm + nsim_iter samples, passed to rstan.

cores

Number of cores, passed to rstan, defaults to parallel::detectCores().

ii

The (zero-indexed) indices of the observations held-out.

Examples

besag_tmbstan(mw, nsim_warm = 0, nsim_iter = 100, cores = 2)
#> Error in .class1(object): object 'mw' not found