eml_bandit2arm: Expectation-Maximization with Laplace approximation (EML) of...
In psuthaharan/bandit2arm: Modeling Behavior from the Two-Armed Bandit Task

Description Usage Arguments Value Examples

View source: R/estimate_bandit2arm.R

This function runs the EML technique to recover behavior from the simulated task data.

eml_bandit2arm(
  data = bandit2arm_data,
  param = list(init.x1, init.x2),
  prior.mean = c(0, 0),
  prior.sd = c(5, 5),
  fn = "eml.objectiveFunction",
  opt = "TRM",
  nRes = 5,
  iter = iter,
  eml_data = eml_data
)

`data`	simulated two-armed bandit data
`param`	randomly generated initial parameters.
`prior.mean`	mean priors for x1 and x2, respectively. Defaults to mean 0 for both parameters.
`prior.sd`	standard deviation priors for x1 and x2, respectively. Defaults to 5 for both parameters.
`fn`	objective function being minimized
`opt`	optimization algorithm used. Defaults to trust-region method (trm)
`nRes`	number of restarts. Defaults to 5.
`iter`	iteration of algorithm
`eml_data`	data to be passed for succeeding iterations

A list containing the sum log-likelihood, eml estimates of parameter 1 per subject, eml estimates of parameter 2 per subject, laplace values of parameter 1 per subject, laplace values of parameter 2 per subject.