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Random effects have a multivariate Gaussian distribution with covariance matrix calculated using centroid_covariance. Kernel hyperparameters are given a prior and learnt.

Usage

ck_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

ck_tmbstan(mw, nsim_warm = 0, nsim_iter = 100, cores = 2)
#> Error in sf::st_centroid(sf): object 'mw' not found