compare: Information Criteria and Marginal Likelihoods

In EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice

Information Criteria and Marginal Likelihoods

Description

Returns the BPIC/DIC or marginal deviance (-2*marginal likelihood) for a list of samples objects.

Usage

compare(
  sList,
  stage = "sample",
  filter = NULL,
  use_best_fit = TRUE,
  BayesFactor = TRUE,
  cores_for_props = 4,
  cores_per_prop = 1,
  print_summary = TRUE,
  digits = 0,
  digits_p = 3,
  ...
)

Arguments

sList	List of samples objects
stage	A string. Specifies which stage the samples are to be taken from "preburn", "burn", "adapt", or "sample"
filter	An integer or vector. If it's an integer, iterations up until the value set by filter will be excluded. If a vector is supplied, only the iterations in the vector will be considered.
use_best_fit	Boolean, defaults to TRUE, uses the minimal or mean likelihood (whichever is better) in the calculation, otherwise always uses the mean likelihood.
BayesFactor	Boolean, defaults to TRUE. Include marginal likelihoods as estimated using WARP-III bridge sampling. Usually takes a minute per model added to calculate
cores_for_props	Integer, how many cores to use for the Bayes factor calculation, here 4 is the default for the 4 different proposal densities to evaluate, only 1, 2 and 4 are sensible.
cores_per_prop	Integer, how many cores to use for the Bayes factor calculation if you have more than 4 cores available. Cores used will be cores_for_props * cores_per_prop. Best to prioritize cores_for_props being 4 or 2
print_summary	Boolean (default TRUE), print table of results
digits	Integer, significant digits in printed table for information criteria
digits_p	Integer, significant digits in printed table for model weights
...	Additional, optional arguments

Value

Matrix of effective number of parameters, mean deviance, deviance of mean, DIC, BPIC, Marginal Deviance (if BayesFactor=TRUE) and associated weights.

Examples

compare(list(samples_LNR), cores_for_props = 1)
# Typically we would define a list of two (or more) different models:
# # Here the full model is an emc object with the hypothesized effect
# # The null model is an emc object without the hypothesized effect
# design_full <- design(data = forstmann,model=DDM,
#                            formula =list(v~0+S,a~E, t0~1, s~1, Z~1, sv~1, SZ~1),
#                            constants=c(s=log(1)))
# # Now without a ~ E
# design_null <- design(data = forstmann,model=DDM,
#                            formula =list(v~0+S,a~1, t0~1, s~1, Z~1, sv~1, SZ~1),
#                            constants=c(s=log(1)))
#
# full_model <- make_emc(forstmann, design_full)
# full_model <- fit(full_model)
#
# null_model <- make_emc(forstmann, design_null)
# null_model <- fit(null_model)
# sList <- list(full_model, null_model)
# # By default emc uses 4 cores to parallelize marginal likelihood estimation across proposals
# # So cores_per_prop = 3 results in 12 cores used.
# compare(sList, cores_per_prop = 3)

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