Importance-sampling diagnostics for an MCML fit — mc

The MCML estimate reweights latent samples drawn at the anchor towards the optimum. When the optimum is far from the anchor the weights become uneven and the estimate unreliable. This reports the effective sample size of the importance weights at the maximiser and a Monte Carlo standard error for the maximised log-likelihood, \(\mathrm{SE}\approx\sqrt{1/\mathrm{ESS}-1/B}\).

Usage

mc_diagnostics(object, warn_frac = 0.1)

Arguments

object: a fitted "SDALGCP2" object.
warn_frac: warn if the ESS falls below this fraction of \(B\).

Value

invisibly, a list with B, ESS, ESS_frac and se_loglik.

Examples

# \donttest{
data(sdalgcp_data)
fit <- sdalgcp(cases ~ x1 + offset(log(pop)), data = sdalgcp_data,
               control = sdalgcp_control(n_sim = 2000, burnin = 500, thin = 5,
                                         reanchor = 0))
d <- mc_diagnostics(fit)
#> Warning: Low importance-sampling ESS (1 of 300, 0.4%): consider re-anchoring (iterate = TRUE) or a par0 closer to the optimum.
d$ESS_frac           # importance-sampling ESS as a fraction of the draws
#> [1] 0.003682337
# }