mcgf_sim {mcgf} | R Documentation |
Simulate Markov chain Gaussian field
Description
Simulate Markov chain Gaussian field
Usage
mcgf_sim(
N,
base = c("sep", "fs"),
lagrangian = c("none", "lagr_tri", "lagr_askey"),
par_base,
par_lagr,
lambda,
dists,
sd = 1,
lag = 1,
scale_time = 1,
horizon = 1,
init = 0,
mu_c = 0,
mu_p = 0,
return_all = FALSE
)
Arguments
N |
Sample size. |
base |
Base model, |
lagrangian |
Lagrangian model, "none" or |
par_base |
Parameters for the base model (symmetric). |
par_lagr |
Parameters for the Lagrangian model. |
lambda |
Weight of the Lagrangian term, |
dists |
Distance matrices or arrays. |
sd |
Standard deviation for each location. |
lag |
Time lag. |
scale_time |
Scale of time unit, default is 1. |
horizon |
Forecast horizon, default is 1. |
init |
Initial samples, default is 0. |
mu_c , mu_p |
Means of current and past. |
return_all |
Logical; if TRUE the joint covariance matrix, arrays of distances and time lag are returned. |
Value
Simulated Markov chain Gaussian field with user-specified covariance
structure. The simulation is done by kriging. The output data is in
space-wide format. dists
must contain h
for symmetric models, and h1
and h2
for general stationary models. horizon
controls forecasting
horizon. sd
, mu_c
, mu_p
, and init
must be vectors of appropriate
sizes.
See Also
Other simulations of Markov chain Gaussian fields:
mcgf_rs_sim()
Examples
par_s <- list(nugget = 0.5, c = 0.01, gamma = 0.5)
par_t <- list(a = 1, alpha = 0.5)
par_base <- list(par_s = par_s, par_t = par_t)
par_lagr <- list(v1 = 5, v2 = 10)
h1 <- matrix(c(0, 5, -5, 0), nrow = 2)
h2 <- matrix(c(0, 8, -8, 0), nrow = 2)
h <- sqrt(h1^2 + h2^2)
dists <- list(h = h, h1 = h1, h2 = h2)
set.seed(123)
X <- mcgf_sim(
N = 1000, base = "sep", lagrangian = "lagr_tri", lambda = 0.5,
par_base = par_base, par_lagr = par_lagr, dists = dists
)
plot.ts(X)