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(Hierarchical) Bayesian Estimation

Source: R/core_bayes_estimate.R
estimate_bayesian.Rd

This function provides a wrapper around the implemented samplers for Bayesian inference in dRiftDM. For parameter estimation, Differential Evolution Markov-Chain Monte-Carlo (DE-MCMC) Turner et al. (2013) is used. An approximation of the marginal likelihood to calculate Bayes Factors can be obtained with the Thermodynamic Integration via Differential Evolution (TIDE) sampler Evans and Annis (2019) . However, TIDE is not yet supported fully, and is at an experimental stage.

Usage

estimate_bayesian(
  drift_dm_obj,
  obs_data_ids = NULL,
  sampler,
  n_chains,
  burn_in,
  samples,
  prob_migration,
  prob_re_eval,
  verbose = NULL,
  ...
)

Arguments

drift_dm_obj

an object of type drift_dm.

obs_data_ids

data.frame for the hierarchical case. An additional column ID is necessary that codes the individuals (see also obs_data).

sampler

character string, indicating the sampler to use. Must be either "DE-MCMC" (default) or "TIDE".

n_chains

numeric, number of chains for the MCMC-sampler. Default is 40.

burn_in

numeric, number of burn-in iterations. Default is 500.

samples

numeric, number of sampling iterations after burn-in. Default is 2000.

prob_migration

numeric, probability of performing a migration crossover step during burn-in. Default is 0.1 (i.e., 10%).

prob_re_eval

numeric, probability of re-evaluating the likelihood/posterior values of the previous iteration i-1 when deciding for the acceptance of the proposal in iteration i. Only considered during burn-in. Default is 0.1 (i.e., 10%).

verbose

integer, indicating verbosity of output: 0 (none), 1 (minimal text output), or 2 (text output and progress bar). Default is 2.

...

additional arguments passed forward to estimate_bayes_h() and estimate_bayes_one_subj().

Value

an object of type mcmc_dm containing posterior samples for parameters, log-posterior values, and log-likelihoods. In the hierarchical case, the respective values are available at both the group-level and the individual-level. The object contains two attributes: sampler and data_model. The former simply stores the type of sampler that was used and codes whether estimation was done in a hierarchical fashion or not. The latter either contains the model and the attached data (in the non-hierarchical case) or a named list of model copies with each individual's data attached.

Details

When a data.frame is supplied, a hierarchical approach to parameter estimation is done. In this case, the supplied data set must provide data for multiple individuals. To estimate the parameters for a single individual (i.e., pursue the non-hierarchical approach), then the supplied model drift_dm_obj must have data attached to it (see obs_data()).

References

Turner BM, Sederberg PB, Brown SD, Steyvers M (2013). “A method for efficiently sampling from distributions with correlated dimensions.” Psychological Methods, 18(3), 368–384. doi:10.1037/a0032222 . Evans NJ, Annis J (2019). “Thermodynamic integration via differential evolution: A method for estimating marginal likelihoods.” Behavior Research Methods, 51, 930–947. doi:10.3758/s13428-018-1172-y .

See also

summary.mcmc_dm(), estimate_bayes_h(), estimate_bayes_one_subj()