simul_fun_generalized_2d: A Special Case of simulation_generalized in 2 Dimensions
In STCCGEV: Conditional Copula Model for Crop Yield Forecasting

View source: R/simul_fun_generalized_2d.R

simul_fun_generalized_2d

R Documentation

A Special Case of simulation_generalized in 2 Dimensions

Description

A Special Case of simulation_generalized in 2 Dimensions

Usage

simul_fun_generalized_2d(
  nsim,
  n_train,
  n_test,
  copula,
  init_params,
  fn,
  u1,
  u2,
  z1_train,
  z2_train,
  X_t,
  y1_test,
  y2_test,
  BSTS_1,
  BSTS_2
)

Arguments

`nsim`	Integer, number of simulation replications.
`n_train`	Integer, number of training observations.
`n_test`	Integer, number of test observations.
`copula`	Character, specifying the copula type: "Clayton", "Frank", "Gumbel", "Joe", or "Gaussian".
`init_params`	Numeric vector, initial parameter values for optimization.
`fn`	Function, log-likelihood function for parameter estimation.
`u1`	Numeric vector (n_train), first pseudo-observation for the copula.
`u2`	Numeric vector (n_train), second pseudo-observation for the copula.
`z1_train`	Numeric matrix (n_train x M), observed data for the first margin.
`z2_train`	Numeric matrix (n_train x M), observed data for the second margin.
`X_t`	Numeric matrix (n_train x M), risk factors for the dynamic copula parameter.
`y1_test`	Numeric vector (n_test), true future values for the first response variable.
`y2_test`	Numeric vector (n_test), true future values for the second response variable.
`BSTS_1`	Fitted BSTS model for the first response variable.
`BSTS_2`	Fitted BSTS model for the second response variable.

Value

A list containing:

`theta_simulated`	Simulated copula parameters across replications.
`y1_simulated`	Simulated values for the first response variable.
`y2_simulated`	Simulated values for the second response variable.
`MSE`	Mean squared error for each simulation run.
`optim_results`	Results from the optimization process.