krige.mcgf: Obtain kriging forecasts for an `mcgf` object.

Description

Obtain kriging forecasts for an mcgf object.

Usage

# S3 method for mcgf
krige(
  x,
  newdata = NULL,
  model = c("all", "base", "empirical"),
  interval = FALSE,
  level = 0.95,
  ...
)

Value

A list of kriging forecasts (and prediction intervals).

Arguments

x: An mcgf object.
newdata: A data.frame with the same column names as x. If newdata is missing the forecasts at the original data points are returned.
model: Which model to use. One of all, base, or empirical.
interval: Logical; if TRUE, prediction intervals are computed.
level: A numeric scalar between 0 and 1 giving the confidence level for the intervals (if any) to be calculated. Used when interval = TRUE
...: Additional arguments. Give lag and horizon if they are not defined in x for the empirical model.

Details

It produces simple kriging forecasts for a zero-mean mcgf. It supports kriging for the empirical model, the base model, and the all model which is the general stationary model with the base and Lagrangian model from x.

When interval = TRUE, confidence interval for each forecasts and each horizon is given. Note that it does not compute confidence regions.

Examples

Run this code

data(sim1)
sim1_mcgf <- mcgf(sim1$data, dists = sim1$dists)
sim1_mcgf <- add_acfs(sim1_mcgf, lag_max = 5)
sim1_mcgf <- add_ccfs(sim1_mcgf, lag_max = 5)

# Fit a separable model and store it to 'sim1_mcgf'
fit_sep <- fit_base(
    sim1_mcgf,
    model = "sep",
    lag = 5,
    par_init = c(
        c = 0.001,
        gamma = 0.5,
        a = 0.3,
        alpha = 0.5
    ),
    par_fixed = c(nugget = 0)
)
sim1_mcgf <- add_base(sim1_mcgf, fit_base = fit_sep)

# Fit a Lagrangian model
fit_lagr <- fit_lagr(
    sim1_mcgf,
    model = "lagr_tri",
    par_init = c(v1 = 300, v2 = 300, lambda = 0.15),
    par_fixed = c(k = 2)
)

# Store the fitted Lagrangian model to 'sim1_mcgf'
sim1_mcgf <- add_lagr(sim1_mcgf, fit_lagr = fit_lagr)

# Calculate the simple kriging predictions and intervals
sim1_krige <- krige(sim1_mcgf, interval = TRUE)

# Calculate RMSE for each location
rmse <- sqrt(colMeans((sim1_mcgf - sim1_krige$fit)^2, na.rm = TRUE))
rmse

# Calculate MAE for each location
mae <- colMeans(abs(sim1_mcgf - sim1_krige$fit), na.rm = TRUE)
mae

# Calculate POPI for each location
popi <- colMeans(
    sim1_mcgf < sim1_krige$lower | sim1_mcgf > sim1_krige$upper,
    na.rm = TRUE
)
popi