Simulated RS-MCGF for 20 locations.
Details
sim3 contains a simulated RS-MCGF for 20 locations. It is simulated with
the same base model and a regime-switching Lagrangian model. The true
parameters for the base model are: \(\text{nugget} = 0, c = 0.05,
\gamma = 0.5, a = 0.5, \alpha = 0.2\), and the true parameters for the
Lagrangian model are
$$\text{Regime 1}: \lambda = 0.2, v_1 = -100, v_2 = 100, k = 2,$$
$$\text{Regime 1}: \lambda = 0.2, v_1 = 200, v_2 = 200, k = 2.$$
For parameter estimation, the base model is assumed known and is used to
estimate the regime-switching prevailing winds.
Examples
# Code used to generate `sim3`
# \donttest{
library(mcgf)
set.seed(123)
x <- stats::rnorm(10, -110)
y <- stats::rnorm(10, 50)
locations <- cbind(x, y)
h <- find_dists(locations, longlat = TRUE)
# simulate regimes
K <- 2
N <- 1000
lag <- 5
tran_mat <- matrix(rnorm(K^2, mean = 0.06 / (K - 1), sd = 0.01), nrow = K)
diag(tran_mat) <- rnorm(K, mean = 0.94, sd = 0.1)
tran_mat <- sweep(abs(tran_mat), 1, rowSums(tran_mat), `/`)
tran_mat
#> [,1] [,2]
#> [1,] 0.94635675 0.05364325
#> [2,] 0.06973429 0.93026571
# [,1] [,2]
# [1,] 0.94635675 0.05364325
# [2,] 0.06973429 0.93026571
regime <- rep(NA, N)
regime[1] <- 1
for (n in 2:(N)) {
regime[n] <- sample(1:K, 1, prob = tran_mat[regime[n - 1], ])
}
table(regime)
#> regime
#> 1 2
#> 567 433
# regime
# 1 2
# 567 433
# simulate RS MCGF
par_base <- list(
par_s = list(nugget = 0, c = 0.05, gamma = 0.5),
par_t = list(a = 0.5, alpha = 0.2)
)
par_lagr1 <- list(v1 = -100, v2 = 100, k = 2)
par_lagr2 <- list(v1 = 200, v2 = 200, k = 2)
sim3 <- mcgf_rs_sim(
N = N, label = regime,
base_ls = list("sep"), lagrangian_ls = list("lagr_tri"),
par_base_ls = list(par_base),
par_lagr_ls = list(par_lagr1, par_lagr2),
lambda_ls = list(0.2, 0.2),
lag_ls = list(lag, lag),
dists_ls = list(h, h)
)
sim3 <- sim3[-c(1:(lag + 1)), ]
rownames(sim3) <- 1:nrow(sim3)
sim3 <- list(
data = sim3[, -1], locations = locations, dists = h,
label = sim3[, 1]
)
# }