multiRL.input

Usage

process_1_input(
  data,
  colnames = list(),
  funcs = list(),
  params = list(),
  priors,
  settings = list(),
  ...
)

data: A data frame in which each row represents a single trial, see data
colnames: Column names in the data frame, see colnames
funcs: The functions forming the reinforcement learning model, see funcs
params: Parameters used by the model’s internal functions, see params
priors: Prior probability density function of the free parameters, see priors
settings: Other model settings, see settings
...: Additional arguments passed to internal functions.

An S4 object of class multiRL.input.

data: A DataFrame containing the trial-level raw data.
colnames: An S4 object of class multiRL.colnames, specifying the column names used in the input data.
features: An S4 object of class multiRL.features, containing standardized representations of states and actions transformed from the raw data.
params: An S4 object of class multiRL.params, containing model parameters.
priors: A List specifying prior distributions for free parameters.
funcs: An S4 object of class multiRL.funcs, containing functions used in model.
settings: An S4 object of class multiRL.settings, storing global settings for model estimation.
elements: A int indicating the number of elements within states.
subid: A Character string identifying the subject.
n_block: A int value indicating the number of blocks.
n_trial: A int value indicating the number of trials.
n_rows: A int value indicating the number of rows in the data.
extra: A List containing additional user-defined information.