Count number of parameters for a model

Usage

count_parameters(model, ...)

# S4 method for class 'exponential'
count_parameters(
  model,
  dynamics = "isotropic",
  covariance = "symmetric",
  parameters_only = FALSE
)

# S4 method for class 'quasi_hyperbolic'
count_parameters(
  model,
  dynamics = "isotropic",
  covariance = "symmetric",
  parameters_only = FALSE
)

# S4 method for class 'double_exponential'
count_parameters(
  model,
  dynamics = "isotropic",
  covariance = "symmetric",
  parameters_only = FALSE
)

Arguments

model: An instance of the model-class
...: 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 count the number of parameters in de parameter slot of the model (TRUE), or to count the number of parameters in the covariance matrix as well (FALSE). Defaults to FALSE.

Value

Integer denoting the number of parameters the model contains within the current specifications.

Examples

# Define a model with a particular dimensionality
my_model <- exponential(
  parameters = list(
    "alpha" = numeric(2),
    "beta" = matrix(0, nrow = 2, ncol = 5),
    "gamma" = matrix(0, nrow = 2, ncol = 2)
  ),
  covariance = matrix(0, nrow = 2, ncol = 2)
)

# Get the number of parameters for this model under no restrictions (i.e., 
# anisotropic forgetting factors and symmetric covariances)
count_parameters(
  my_model, 
  dynamics = "anisotropic",
  covariance = "symmetric"
)
#> [1] 19

# Get the number of parameters for this model in the most limited case 
# (i.e., isotropic forgetting factors and covariances)
count_parameters(
  my_model,
  dynamics = "isotropic",
  covariance = "isotropic"
)
#> [1] 16