krige_new.mcgf_rs {mcgf}

Obtain kriging forecasts for new locations for an mcgf_rs object.

Description

Obtain kriging forecasts for new locations for an mcgf_rs object.

Usage

## S3 method for class 'mcgf_rs'
krige_new(
  x,
  newdata = NULL,
  locations_new = NULL,
  dists_new_ls = NULL,
  newdata_new = NULL,
  sds_new_ls = 1,
  newlabel,
  soft = FALSE,
  prob,
  dists_new_base,
  model = c("all", "base"),
  interval = FALSE,
  level = 0.95,
  ...
)

Arguments

x

An mcgf_rs 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_ls is not supplied.

dists_new_ls

List of signed distance matrices (vectors) with names h, h1, and 'h2' for all locations(and for each regime), 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_ls

List of the standard deviations of the new locations for each regime. Format must be the same as the output from sds.mcgf_rs(). Default is 1 for all regimes.

newlabel

A vector of new regime labels.

soft

Logical; if true, soft forecasts (and bounds) are produced.

prob

Matrix with simplex rows. Number of columns must be the same as unique labels in x.

dists_new_base

Optional, list of distance matrices for the base model. Used when the base model is non-regime switching. Default is h from the first list of dists_new_ls.

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.

Details

It produces simple kriging forecasts for a zero-mean mcgf for new locations given theri 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_ls, with new locations at the end. dists_new_ls 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_ls is used, it should be a list of 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 only one list is provided, it will be used for all regimes.

When soft = TRUE, prob will be used to compute the soft forecasts (weighted forecasts). The number of columns must match the number of unique levels in x. The column order must be the same as the order of regimes as in levels(attr(x, "label", exact = TRUE)). If not all regimes are seen in newlabel, then only relevant columns in prob are used.

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

Value

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

See Also

Other functions on fitting an mcgf_rs: add_base.mcgf_rs(), add_lagr.mcgf_rs(), fit_base.mcgf_rs(), fit_lagr.mcgf_rs(), krige.mcgf_rs()

Examples

data(sim2)
sim2_mcgf <- mcgf_rs(sim2$data,
    locations = sim2$locations,
    label = sim2$label
)
sim2_mcgf <- add_acfs(sim2_mcgf, lag_max = 5)
sim2_mcgf <- add_ccfs(sim2_mcgf, lag_max = 5)

# Fit a regime-switching separable model
fit_sep <- fit_base(
    sim2_mcgf,
    lag_ls = 5,
    model_ls = "sep",
    par_init_ls = list(list(
        c = 0.00005,
        gamma = 0.5,
        a = 0.5,
        alpha = 0.5
    )),
    par_fixed_ls = list(c(nugget = 0))
)

# Store the fitted separable models to 'sim2_mcgf'
sim2_mcgf <- add_base(sim2_mcgf, fit_base_ls = fit_sep)

# Calculate the simple kriging predictions and intervals for all locations
locations_new <- rbind(c(-110, 55), c(-109, 54))
sim2_krige <- krige_new(sim2_mcgf,
    locations_new = locations_new,
    model = "base", interval = TRUE
)