Change model parameters

Fill the model parameters of a particular instance of the model-class with the ones provided in a numeric vector. Used in objective_function to allow for predictions with the model while cohering to the input typical numerical optimizers such as DEoptim and nloptr provide within the objective function.

Usage

fill(model, parameters, ...)

# S4 method for class 'model'
fill(
  model,
  parameters,
  dynamics = "isotropic",
  covariance = "symmetric",
  parameters_only = TRUE,
  cholesky = TRUE,
  ...
)

Arguments

model

Instance of the model-class

parameters

Numeric vector containing the values of the parameters that should be assigned to the model. Importantly, the number of parameters within this vector should correspond exactly to the number of parameters needed by the model. Ideally, you let functions internal to this package handle this for you, rather than you defining these parameters manually.

...

Additional arguments passed on to the methods.

dynamics

Character denoting the structure of the dynamical matrices. Can either be "anisotropic" (completely free), "symmetric" (symmetric around the diagonal), and "isotropic" (diagonal). Note that this influences different parameters for different models, namely \(\Gamma\) for the exponential discounting model, \(N\) and \(K\) for the quasi-hyperbolic discounting model, and \(\Gamma\) and \(N\) for the double-exponential discounting model. Defaults to "isotropic".

covariance

Character denoting the structure of the covariance matrix. Can either be "symmetric" (symmetric around the diagonal) or "isotropic" (diagonal). Defaults to "symmetric".

parameters_only

Logical denoting whether to only fill the parameters in de parameter slot of the model (TRUE), or to fill the covariance matrix as well (FALSE). Defaults to TRUE.

cholesky

Logical denoting whether the idea is to use the Cholesky decomposition to create the values of the covariance matrix. In this case, the indices should only span the lower-triangular of the matrix. Defaults to TRUE.

Value

Instance of the model-class containing the values parameters in its parameter-slot.

Examples

# Create an empty instance of the exponential discounting model
my_model <- exponential()

# Assign the parameters of a vector to this exponential function
fill(
  my_model,
  c(1, 2, 0.5)
)
#> Model of class "exponential":
#> 
#> Dimension: 1
#> Number of predictors: 1
#> Number of parameters: 4
#> 
#> Parameters:
#>   alpha: |  1.00  |
#> 
#>   beta: | 2.00 |
#> 
#>   gamma: | 0.50 |
#> 
#> 
#> Covariance: | 0.00 |