fit_base.mcgf_rs function

Parameter estimation for symmetric correlation functions for an mcgf_rs

object.

## S3 method for class 'mcgf_rs'
fit_base(
  x,
  lag_ls,
  horizon = 1,
  model_ls,
  method_ls = "wls",
  optim_fn_ls = "nlminb",
  par_fixed_ls = list(NULL),
  par_init_ls,
  lower_ls = list(NULL),
  upper_ls = list(NULL),
  other_optim_fn_ls = list(NULL),
  dists_base_ls = list(NULL),
  scale_time = 1,
  rs = TRUE,
  ...
)

Arguments

• x: An mcgf_rs object containing attributes dists, acfs, ccfs, and sds.

• lag_ls: List of integer time lags.

• horizon: Integer forecast horizon.

• model_ls: List of base models, each element must be one of spatial, temporal, sep, fs. Only sep and fs are supported when mle is used in model_ls.

• method_ls: List of parameter estimation methods, weighted least square (wls) or maximum likelihood estimation (mle).

• optim_fn_ls: List of optimization functions, each element must be one of nlminb, optim, other. When use other, supply other_optim_fn_ls.

• par_fixed_ls: List of fixed parameters.

• par_init_ls: List of initial values for parameters to be optimized.

• lower_ls: Optional; list of lower bounds of parameters.

• upper_ls: Optional: list of upper bounds of parameters.

• other_optim_fn_ls: Optional, list of other optimization functions. The first two arguments must be initial values for the parameters and a function to be minimized respectively (same as that of optim and nlminb).

• dists_base_ls: List of lists of distance matrices. If NULL, dists(x)

  is used. Each element must be a matrix or an array of distance matrices.

• scale_time: Scale of time unit, default is 1. lag is divided by scale_time for parameter estimation.

• rs: Logical; if TRUE x is treated as a regime-switching, FALSE if the parameters need to be estimated in a non-regime-switching setting.

• ...: Additional arguments passed to all optim_fn_ls.

Returns

A list containing outputs from optimization functions of optim_fn

for each regime.

Details

This functions is the regime-switching variant of fit_base.mcgf(). Arguments are in lists. The length of arguments that end in _ls must be 1 or the same as the number of regimes in x. If the length of an argument is 1, then it is set the same for all regimes. Refer to fit_base.mcgf() for more details of the arguments.

Note that both wls and mle are heuristic approaches when x contains observations from a subset of the discrete spatial domain, though estimation results are close to that using the full spatial domain for large sample sizes.

Examples

data(sim2)
sim2_mcgf <- mcgf_rs(sim2$data, dists = sim2$dists, 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 pure spatial model
fit_spatial <-
    fit_base(
        sim2_mcgf,
        lag_ls = 5,
        model_ls = "spatial",
        par_init_ls = list(c(c = 0.00005, gamma = 0.5)),
        par_fixed_ls = list(c(nugget = 0))
    )
lapply(fit_spatial[1:2], function(x) x$fit)

# Fit a regime-switching pure temporal model
fit_temporal <-
    fit_base(
        sim2_mcgf,
        lag_ls = 5,
        model_ls = "temporal",
        par_init_ls = list(
            list(a = 0.8, alpha = 0.8),
            list(a = 0.1, alpha = 0.1)
        )
    )
lapply(fit_temporal[1:2], function(x) x$fit)

# 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))
)
lapply(fit_sep[1:2], function(x) x$fit)

See Also

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