krige_new.mcgf: Obtain kriging forecasts for new locations for an `mcgf` object.

Description

Obtain kriging forecasts for new locations for an mcgf object.

Usage

# S3 method for mcgf
krige_new(
  x,
  newdata = NULL,
  locations_new = NULL,
  dists_new = NULL,
  newdata_new = NULL,
  sds_new = 1,
  model = c("all", "base"),
  interval = FALSE,
  level = 0.95,
  dists_new_base,
  ...
)

Value

A list of kriging forecasts (and prediction intervals) for all locations.

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.
locations_new: A matrix of data.frame of 2D points of new locations, first column longitude, second column latitude, both in decimal degrees. Supply only if x contains locations. Required when dists_new is not supplied.
dists_new: List of signed distance matrices (vectors) with names h, h1, and 'h2' for all locations, with new locations in the end. Each matrix must have the same number of columns. Required when locations_new is not supplied.
newdata_new: Optional; a data.frame with the same number of rows as newdata. It contains the data of the new locations.
sds_new: The standard deviations of the new locations. Default is 1.
model: Which model to use. One of all or base.
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.
dists_new_base: Optional; a distance array for all locations for the base model, with new locations in the end. Used for the base model.
...: Additional arguments. Not in use.

Details

It produces simple kriging forecasts for a zero-mean mcgf for new locations given their coordinates or relative distances. It supports kriging for the base model and the all model which is the general stationary model with the base and Lagrangian model from x.

Users can either supply the coordinates via locations_new, or a list of distance for all locations via dists_new, with new locations at the end. dists_new will be used to calculate the new covariance matrices. When locations_new is used, make sure x contains the attribute locations of the coordinates of the old locations. When dists_new is used, it should be a list of signed distance matrices of the same dimension, where each row corresponds to the relative distances between a new location and old locations in the same order as they appear in x.

If data for the new locations are available, use newdata_new to include them and they will be used to calculate the kriging forecasts for the new locations; otherwise only data of the old locations will be used via newdata.

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$data, locations = sim1$locations)
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 for all locations
locations_new <- rbind(c(-110, 55), c(-109, 54))
sim1_krige <- krige_new(sim1_mcgf,
    locations_new = locations_new,
    interval = TRUE
)