get_pars: Filter/Manipulate Parameters from emc Object

In EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice

Filter/Manipulate Parameters from emc Object

Description

Underlying function used in most plotting and object handling functions in EMC2. Can for example be used to filter/thin a parameter type (i.e, group-level means mu) and convert to an mcmc.list.

Usage

get_pars(
  emc,
  selection = "mu",
  stage = get_last_stage(emc),
  thin = 1,
  filter = 0,
  map = FALSE,
  add_recalculated = FALSE,
  length.out = NULL,
  by_subject = FALSE,
  return_mcmc = TRUE,
  merge_chains = FALSE,
  subject = NULL,
  flatten = FALSE,
  remove_dup = FALSE,
  remove_constants = TRUE,
  use_par = NULL,
  type = NULL,
  true_pars = NULL,
  chain = NULL,
  covariates = NULL
)

Arguments

emc	an emc object.
selection	A Character string. Indicates which parameter type to select (e.g., alpha, mu, sigma2, correlation).
stage	A character string. Indicates from which sampling stage(s) to take the samples from (i.e. preburn, burn, adapt, sample)
thin	An integer. By how much to thin the chains
filter	Integer or numeric vector. If an integer is supplied, iterations up until that integer are removed. If a vector is supplied, the iterations within the range are kept.
map	Boolean. If TRUE parameters will be mapped back to the cells of the experimental design using the design matrices. Otherwise the sampled parameters are returned. Only works for selection = mu or selection = alpha.
add_recalculated	Boolean. If TRUE will also add recalculated parameters, such as b in the LBA (b = B + A; see ?LBA), or z in the DDM z = Z*A (see ?DDM) only works when map = TRUE
length.out	Integer. Alternatively to thinning, you can also select a desired length of the MCMC chains, which will be thinned appropriately.
by_subject	Boolean. If TRUE for selections that include subject parameters (e.g. alpha), plot/stats are organized by subject, otherwise by parameter.
return_mcmc	Boolean. If TRUE returns an mcmc.list object, otherwise a matrix/array with the parameter type.
merge_chains	Boolean. If TRUE returns parameter type merged across chains.
subject	Integer (vector) or character (vector). If an integer will select the 'x'th subject(s), if a character it should match subject names in the data which will be selected.
flatten	Boolean. If FALSE for 3-dimensional samples (e.g., correlations: n-pars x n-pars x iterations). organizes by the dimension containing parameter names, otherwise collapses names across the first and second dimension. Does not apply for selection = "alpha"
remove_dup	Boolean. If TRUE removes duplicate values from the samples. Automatically set to TRUE if flatten = TRUE
remove_constants	Boolean. If TRUE removes constant values from the samples (e.g. 0s in the covariance matrix).
use_par	Character (vector). If specified, only these parameters are returned. Should match the parameter names (i.e. these are collapsed when flatten = TRUE and use_par should also be collapsed names).
type	Character indicating the group-level model selected. Only necessary if sampler isn't specified.
true_pars	Set of true_parameters can be specified to apply flatten or use_par on a set of true parameters
chain	Integer. Which of the chain(s) to return
covariates	Only needed with plot for priors and covariates in the design

Value

An mcmc.list object of the selected parameter types with the specified manipulations

Examples

# E.g. get the group-level mean parameters mapped back to the design
get_pars(samples_LNR, stage = "sample", map = TRUE, selection = "mu")

# Or return the flattened correlation, with 10 iterations per chain
get_pars(samples_LNR, stage = "sample", selection = "correlation", flatten = TRUE, length.out = 10)

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