inst/doc/Predicting-Crop-Yields-Using-STCCGEV-Method.R

In STCCGEV: Conditional Copula Model for Crop Yield Forecasting

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup--------------------------------------------------------------------
library(STCCGEV)

## -----------------------------------------------------------------------------
bsts_Dufferin <- fit_bsts(yy_train[,1], zz_train[,1,], lags = 2, MCMC.iter = 10)
bsts_Wellington <- fit_bsts(yy_train[,2], zz_train[,2,], lags = 2, MCMC.iter = 10)
list_bsts_sample <- list(bsts_Dufferin, bsts_Wellington)
Gaussianforecasts_G <- simulation_generalized(nsim = 10,
                                           n_train = n_train,
                                            n_test = n_test,
                                            copula = "Gaussian",
                                       init_params = init_params_full_G,
                                                fn = log_likelihood_Generalized,
                                           U_train = uu,
                                           Z_train = zz_train,
                                                 X = xx_train,
                                            Y_test = yy_test,
                                         BSTS_list = list_bsts_sample)
Dufferin_Gaussian_plot<- plot_forecast(forecast = Gaussianforecasts_G[[3]][,,1],
                                     data_train = yy_train[,1],
                                      data_test = yy_test[,1],
                                           time = time_all,
                                     quant_high = 0.95,
                                      quant_low = 0.05,
                                   observed_col = "#e23345",
                                   forecast_col = "#CF9FFF",
                                          title = "Dufferin - Gaussian copula forecast")
Wellington_Gaussian_plot<- plot_forecast(forecast = Gaussianforecasts_G[[3]][,,2],
                                       data_train = yy_train[,2],
                                        data_test = yy_test[,2],
                                             time = time_all,
                                       quant_high = 0.95,
                                        quant_low = 0.05,
                                     observed_col = "#6195c4",
                                     forecast_col = "#CF9FFF",
                                            title = "Wellington - Gaussian copula forecast")
print(Dufferin_Gaussian_plot)
print(Wellington_Gaussian_plot)