Run a Group-level Model. — run

Separate function for running only the group-level model. This can be useful in a two-step analysis. Works similar in functionality to make_emc, except also does the fitting and returns an emc object that works with most posterior checking tests (but not the data generation/posterior predictives).

Usage

run_hyper(
  type = "standard",
  data,
  prior = NULL,
  iter = 1000,
  n_chains = 3,
  ...
)

Arguments

type: A string indicating whether to run a standard group-level, blocked, diagonal, factor, or single (i.e., non-hierarchical) model.
data: A data frame, or a list of data frames. Needs to have the variable subjects as participant identifier.
prior: an emc.prior object.
iter: Number of MCMC samples to collect.
n_chains: An integer. Specifies the number of mcmc chains to be run (has to be more than 1 to compute rhat).
...: Additional, optional arguments.

Value

an emc object with only group-level samples