run_sbc: Simulation-Based Calibration

In EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice

Simulation-Based Calibration

Description

Runs SBC for an EMC2 model and associated design. Returns normalized rank (between 0 and 1) and prior samples. For hierarchical models the group-level mean and the (implied) group-level (co-)variance are returned. For non-hierarchical models only the subject-level parameters rank is returned.

Usage

run_sbc(
  design_in,
  prior_in,
  replicates = 250,
  trials = 100,
  n_subjects = 30,
  plot_data = FALSE,
  verbose = TRUE,
  fileName = NULL,
  ...
)

Arguments

design_in	An emc design list. The design of the model to be used in SBC
prior_in	An emc prior list. The prior for the design to be used in SBC
replicates	Integer. The number of samples to draw from the prior
trials	Integer. The number of trials of the simulated data (per subject)
n_subjects	Integer. Only used for hierarchical models. The number of subjects to be used in data generation of each replicate
plot_data	Boolean. Whether to plot the data simulated (aggregated across subjects)
verbose	Verbose. Whether to print progress related messages
fileName	Character. Highly recommended, saves temporary results to the fileName
...	A list of optional additional arguments that can be passed to fit and make_emc

Value

The ranks and prior samples. For hierarchical models also the prior-generated subject-level parameters.

EMC2 documentation built on April 11, 2025, 5:50 p.m.