run_mcstudy: Run the Monte Carlo study by KLTG (2021), or a smaller...

In scoringRules: Scoring Rules for Parametric and Simulated Distribution Forecasts

Run the Monte Carlo study by KLTG (2021), or a smaller version thereof

Description

Run the Monte Carlo study by KLTG (2021), or a smaller version thereof

Usage

run_mcstudy(
  s = 2,
  a = 0.5,
  n = 12,
  nr_iterations = 50,
  zoom = FALSE,
  random_seed = 816
)

Arguments

s, a, n	parameters characterizing the process from which data are simulated (see Section 4 and Table 4 of KLTG, 2021). Defaults to the values reported in the main text of the paper.
nr_iterations	number of Monte Carlo iterations (defaults to 50).
zoom	set to TRUE to produce results for a fine grid of small (MCMC) sample sizes, as in Figure 2 of KLTG (2021).
random_seed	seed used for running the simulation experiment. Defaults to 816.

Details

The full results in Section 4 of KLTG (2021) are based on s = 2, a = 0.5, n = 12 and nr_iterations = 1000. Producing these results takes about 140 minutes on an Intel i7 processor.

Value

Object of class "mcstudy", containing the results of the analysis. This object can be passed to plot for plotting, see the documentation for plot.mcstudy.

Author(s)

Fabian Krueger

References

Krueger, F., Lerch, S., Thorarinsdottir, T.L. and T. Gneiting (2021): ‘Predictive inference based on Markov chain Monte Carlo output’, International Statistical Review 89, 274-301. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/insr.12405")}

See Also

plot.mcstudy produces a summary plot of the results generated by run_mcstudy

scoringRules documentation built on Sept. 18, 2024, 5:09 p.m.