Index model parameters — index • discounting

Fill the model parameters with indices denoting which values to use for a particular model.

Usage

index(model, ...)

# S4 method for class 'exponential'
index(
  model,
  dynamics = "isotropic",
  parameters_only = TRUE,
  full = TRUE,
  cholesky = TRUE,
  ...
)

# S4 method for class 'quasi_hyperbolic'
index(
  model,
  dynamics = "isotropic",
  parameters_only = TRUE,
  full = TRUE,
  cholesky = TRUE,
  ...
)

# S4 method for class 'double_exponential'
index(
  model,
  dynamics = "isotropic",
  parameters_only = TRUE,
  full = TRUE,
  cholesky = TRUE,
  ...
)

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".
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.
full: Logical denoting whether to provide the full matrices or whether a partial fill is sufficient. Makes the distinction between a symmetric matrix being lower-triangular or fully filled. Defaults to TRUE as for most purposes, you would want the complete matrix. An example of where this is FALSE can be found in parameters. Note that if full = FALSE and parameters_only = FALSE, this will automatically let the cholesky argument become TRUE, ensuring only the lower-triangular of the covariance matrix is indexed (as required by full).
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 with the parameters being indexed

Examples

index(
  double_exponential(d = 2, k = 3),
  full = TRUE
)
#> Model of class "double_exponential":
#> 
#> Dimension: 2
#> Number of predictors: 3
#> Number of parameters: 20
#> 
#> Parameters:
#>   alpha: |  1.00  |
#>          |  2.00  |
#> 
#>   beta: | 3.00  5.00  7.00 |
#>         | 4.00  6.00  8.00 |
#> 
#>   omega: |  9.00  |
#> 
#>   gamma: | 10.00  0.00 |
#>          | 0.00  11.00 |
#> 
#>   nu: | 12.00  0.00 |
#>       | 0.00  13.00 |
#> 
#> 
#> Covariance: | 0.00  0.00 |
#>             | 0.00  0.00 |

index(
  double_exponential(d = 2, k = 3),
  full = FALSE
)
#> Model of class "double_exponential":
#> 
#> Dimension: 2
#> Number of predictors: 3
#> Number of parameters: 20
#> 
#> Parameters:
#>   alpha: |  1.00  |
#>          |  2.00  |
#> 
#>   beta: | 3.00  5.00  7.00 |
#>         | 4.00  6.00  8.00 |
#> 
#>   omega: |  9.00  |
#> 
#>   gamma: | 10.00  0.00 |
#>          | 0.00  11.00 |
#> 
#>   nu: | 12.00  0.00 |
#>       | 0.00  13.00 |
#> 
#> 
#> Covariance: | 0.00  0.00 |
#>             | 0.00  0.00 |