Process: Optimizing Parameters

This is an internal helper function for `fit_p`. Its primary purpose is to provide a unified interface for users to interact with various optimization algorithm packages. It adapts the inputs and outputs to be compatible with eight distinct algorithms, ensuring a seamless experience regardless of the underlying solver used.

The function provides several optimization algorithms:

• 1. L-BFGS-B (from stats::optim)

• 2. Simulated Annealing (GenSA::GenSA)

• 3. Genetic Algorithm (GA::ga)

• 4. Differential Evolution (DEoptim::DEoptim)

• 5. Particle Swarm Optimization (pso::psoptim)

• 6. Bayesian Optimization (mlrMBO::mbo)

• 7. Covariance Matrix Adapting Evolutionary Strategy (cmaes::cma_es)

• 8. Nonlinear Optimization (nloptr::nloptr)

For more information, please refer to the homepage of this package: https://yuki-961004.github.io/binaryRL/

Usage

optimize_para(
  data,
  id,
  obj_func,
  n_params,
  n_trials,
  lower,
  upper,
  initial_params = NA,
  initial_size = 50,
  iteration = 10,
  seed = 123,
  algorithm
)

Arguments

data

[data.frame] This data should include the following mandatory columns:

• "sub"

• "time_line" (e.g., "Block", "Trial")

• "L_choice"

• "R_choice"

• "L_reward"

• "R_reward"

• "sub_choose"

id

[character] Specifies the ID of the subject whose optimal parameters will be fitted. This parameter accepts either string or numeric values. The provided ID must correspond to an existing subject identifier within the raw dataset provided to the function.

obj_func

[function] The objective function that the optimization algorithm package accepts. This function must strictly take only one argument, `params` (a vector of model parameters). Its output must be a single numeric value representing the loss function to be minimized. For more detailed requirements and examples, please refer to the relevant documentation ( TD, RSTD, Utility ).

n_params

[integer] The number of free parameters in your model.

n_trials

[integer] The total number of trials in your experiment.

lower

[vector] Lower bounds of free parameters

upper

[vector] Upper bounds of free parameters

initial_params

[vector] Initial values for the free parameters that the optimization algorithm will search from. These are primarily relevant when using algorithms that require an explicit starting point, such as L-BFGS-B. If not specified, the function will automatically generate initial values close to zero.

default: initial_params = NA.

initial_size

[integer] This parameter corresponds to the population size in genetic algorithms (GA). It specifies the number of initial candidate solutions that the algorithm starts with for its evolutionary search. This parameter is only required for optimization algorithms that operate on a population, such as `GA` or `DEoptim`.

default: initial_size = 50.

iteration

[integer] The number of iterations the optimization algorithm will perform when searching for the best-fitting parameters during the fitting phase. A higher number of iterations may increase the likelihood of finding a global optimum but also increases computation time.

seed

[integer] Random seed. This ensures that the results are reproducible and remain the same each time the function is run.

default: seed = 123

algorithm

[character] Choose an algorithm package from `L-BFGS-B`, `GenSA`, `GA`, `DEoptim`, `PSO`, `Bayesian`, `CMA-ES`.

In addition, any algorithm from the `nloptr` package is also supported. If your chosen `nloptr` algorithm requires a local search, you need to input a character vector. The first element represents the algorithm used for global search, and the second element represents the algorithm used for local search.

Value

the result of binaryRL with optimal parameters

Examples

if (FALSE) { # \dontrun{
binaryRL.res <- binaryRL::optimize_para(
  data = binaryRL::Mason_2024_Exp2,
  id = 1,
  obj_func = binaryRL::RSTD,
  n_params = 3,
  n_trials = 360,
  lower = c(0, 0, 0),
  upper = c(1, 1, 1),
  iteration = 10,
  seed = 123,
  algorithm = "L-BFGS-B"   # Gradient-Based (stats)
  #algorithm = "GenSA"    # Simulated Annealing (GenSA)
  #algorithm = "GA"       # Genetic Algorithm (GA)
  #algorithm = "DEoptim"  # Differential Evolution (DEoptim)
  #algorithm = "PSO"      # Particle Swarm Optimization (pso)
  #algorithm = "Bayesian" # Bayesian Optimization (mlrMBO)
  #algorithm = "CMA-ES"   # Covariance Matrix Adapting (cmaes)
  #algorithm = c("NLOPT_GN_MLSL", "NLOPT_LN_BOBYQA")
)
summary(binaryRL.res)
} # }