Get bounds of the parameters of a model

Usage

get_bounds(model, ...)

# S4 method for class 'exponential'
get_bounds(
  model,
  dynamics = "isotropic",
  covariance = "symmetric",
  parameters_only = TRUE,
  lower = NULL,
  upper = NULL
)

# S4 method for class 'quasi_hyperbolic'
get_bounds(
  model,
  dynamics = "isotropic",
  covariance = "symmetric",
  parameters_only = TRUE,
  lower = NULL,
  upper = NULL
)

# S4 method for class 'double_exponential'
get_bounds(
  model,
  dynamics = "isotropic",
  covariance = "symmetric",
  parameters_only = TRUE,
  lower = NULL,
  upper = NULL
)

Arguments

model: Instance of the model-class.
...: 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 get bounds for the parameters in de parameter slot of the model (TRUE), or to fill the covariance matrix as well (FALSE). Defaults to TRUE.
lower: Numeric vector of the same length as the substantive parameters in the model, denoting the lower bounds for each one. For the exponential discounting model, for example, a value of `c(0, 0, 0, 0)` would denote a minimal value of 0 for all values in \(\bm{\alpha}\), \(B\), \(\Gamma\), and \(\Sigma\). Defaults depend on the model itself and have been chosen while accounting for natural bounds in the data used in this project. Note that if parameters_only = TRUE, there is no need to specify a bound for the covariances.
upper: Numeric vector that functions the same as lower, but specifying the upper bounds instead.

Value

Named list containing numeric vectors denoting the lower and upper bounds for the parameters (under "lower" and "upper" resp.). The order maintained in this

Examples

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

# Get the bounds for the model
get_bounds(
  my_model,
  dynamics = "anisotropic",
  covariance = "isotropic",
  parameters_only = FALSE
)
#> $lower
#> [1] -1e+00 -5e+00  0e+00  1e-05
#> 
#> $upper
#> [1] 1 5 1 1
#> 

# Get the bounds for the model when specifying your personal lower and upper
# bounds
get_bounds(
  my_model,
  dynamics = "anisotropic",
  covariance = "isotropic",
  parameters_only = FALSE,
  lower = c(0, -100, 0, 0.01),
  upper = c(1, 100, 0.5, 1)
)
#> $lower
#> [1]  0e+00 -1e+02  0e+00  1e-02
#> 
#> $upper
#> [1]   1.0 100.0   0.5   1.0
#>