Step 3: Optimizing parameters to fit real data

Usage

fit_p(
  estimate,
  data,
  colnames,
  behrule,
  ids = NULL,
  funcs = NULL,
  priors = NULL,
  settings = NULL,
  models,
  algorithm,
  lowers,
  uppers,
  control,
  ...
)

Arguments

estimate: Estimate method that you want to use, see estimate
data: A data frame in which each row represents a single trial, see data
colnames: Column names in the data frame, see colnames
behrule: The agent’s implicitly formed internal rule, see behrule
ids: The Subject ID of the participant whose data needs to be fitted.
funcs: The functions forming the reinforcement learning model, see funcs
priors: Prior probability density function of the free parameters, see priors
settings: Other model settings, see settings
models: Reinforcement Learning Models
algorithm: Algorithm packages that multiRL supports, see algorithm
lowers: Lower bound of free parameters in each model.
uppers: Upper bound of free parameters in each model.
control: Settings manage various aspects of the iterative process, see control
...: Additional arguments passed to internal functions.

Example

 # fitting
 fitting.MLE <- multiRL::fit_p(
   estimate = "MLE",

   data = multiRL::TAB,
   colnames = list(
     object = c("L_choice", "R_choice"),
     reward = c("L_reward", "R_reward"),
     action = "Sub_Choose"
   ),
   behrule = list(
     cue = c("A", "B", "C", "D"),
     rsp = c("A", "B", "C", "D")
   ),

   models = list(multiRL::TD, multiRL::RSTD, multiRL::Utility),
   settings = list(name = c("TD", "RSTD", "Utility")),

   algorithm = "NLOPT_GN_MLSL",
   lowers = list(c(0, 0), c(0, 0, 0), c(0, 0, 0)),
   uppers = list(c(1, 5), c(1, 1, 5), c(1, 5, 1)),
   control = list(core = 10, iter = 100)
 )