SBC_test: Simulation based calibration

In mcmcsae: Markov Chain Monte Carlo Small Area Estimation

Simulation based calibration

Description

Simulation based calibration

Usage

SBC_test(
  ...,
  pars,
  n.draws = 25L,
  n.sim = 20L * n.draws,
  burnin = 25L,
  thin = 2L,
  show.progress = TRUE
)

Arguments

...	passed to create_sampler (can be all parameters except prior.only)
pars	named list with univariate functions of the parameters to use in test. This list is passed to argument pred of MCMCsim.
n.draws	number of posterior draws to retain in posterior simulations.
n.sim	number of simulation iterations.
burnin	burnin to use in posterior simulations, passed to MCMCsim.
thin	thinning to use in posterior simulations, passed to MCMCsim.

Value

A matrix with ranks.

References

S. Talts, M. Betancourt, D. Simpson, A. Vehtari and A. Gelman (2018). Validating Bayesian inference algorithms with simulation-based calibration. arXiv:1804.06788.

Examples

## Not run: 
# this example takes a long time
n <- 10L  # data size
dat <- data.frame(x=runif(n))
SBC_test(~ reg(~ 1 + x, prior=pr_normal(mean=c(0.25, 1), precision=1), name="beta"),
  sigma.mod=pr_invchisq(df=1, scale=list(df=1, scale=1)), data=dat,
  pars=list(mu="beta[1]", beta_x="beta[2]", sigma="sigma_"),
  n.draws=25L, n.sim=100L*25L, thin=3L, burnin=50L
)

## End(Not run)

mcmcsae documentation built on Oct. 11, 2023, 1:06 a.m.