# Takes a while on CRAN
set.seed(1)
mu <- -0.2
rho <- 0.7
sigma_y <- 0.1
sigma_x <- 1
x <- numeric(50)
x[1] <- rnorm(1, mu, sigma_x / sqrt(1 - rho^2))
for(i in 2:length(x)) {
x[i] <- rnorm(1, mu * (1 - rho) + rho * x[i - 1], sigma_x)
}
y <- rnorm(50, exp(x), sigma_y)
pntrs <- cpp_example_model("nlg_ar_exp")
model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1,
Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn,
Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn,
theta = c(mu= mu, rho = rho,
log_sigma_x = log(sigma_x), log_sigma_y = log(sigma_y)),
log_prior_pdf = pntrs$log_prior_pdf,
n_states = 1, n_etas = 1, state_names = "state")
out_iekf <- ekf(model_nlg, iekf_iter = 5)
out_ukf <- ukf(model_nlg, alpha = 0.01, beta = 2, kappa = 1)
ts.plot(cbind(x, out_iekf$att, out_ukf$att), col = 1:3)
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