fit_lagr.mcgf {mcgf} | R Documentation |
Parameter estimation for Lagrangian correlation functions for an mcgf
object.
Description
Parameter estimation for Lagrangian correlation functions for an mcgf
object.
Usage
## S3 method for class 'mcgf'
fit_lagr(
x,
model = c("lagr_tri", "lagr_askey", "lagr_exp", "none"),
method = c("wls", "mle"),
optim_fn = c("nlminb", "optim", "other"),
par_fixed = NULL,
par_init,
lower = NULL,
upper = NULL,
other_optim_fn = NULL,
dists_base = FALSE,
dists_lagr = NULL,
...
)
Arguments
x |
An |
model |
Base model, one of |
method |
Parameter estimation methods, weighted least square ( |
optim_fn |
Optimization functions, one of |
par_fixed |
Fixed parameters. |
par_init |
Initial values for parameters to be optimized. |
lower |
Optional; lower bounds of parameters lambda, v1, v2, and k. |
upper |
Optional: upper bounds of parameters lambda, v1, v2, and k. |
other_optim_fn |
Optional, 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 |
dists_base |
Logical; if TRUE |
dists_lagr |
List of distance matrices/arrays. Used when |
... |
Additional arguments passed to |
Details
This function fits the Lagrangian models using weighted least squares and
maximum likelihood estimation. The base model must be fitted first using
add_base()
or base<-
. Optimization functions such as nlminb
and optim
are supported. Other functions are also supported by setting
optim_fn = "other"
and supplying other_optim_fn
. lower
and upper
are
lower and upper bounds of parameters in par_init
and default bounds are
used if they are not specified.
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.
Since parameters for the base model and the Lagrangian model are estimated sequentially, more accurate estimation may be obtained if the full model is fitted all at once.
Value
A list containing outputs from optimization functions of optim_fn
.
See Also
Other functions on fitting an mcgf:
add_base.mcgf()
,
add_lagr.mcgf()
,
fit_base.mcgf()
,
krige.mcgf()
,
krige_new.mcgf()
Examples
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)
)
fit_lagr$fit