ukf | R Documentation |
Function ukf
runs the unscented Kalman filter for the given
non-linear Gaussian model of class ssm_nlg
,
and returns the filtered estimates and one-step-ahead predictions of the
states α_t given the data up to time t.
ukf(model, alpha = 0.001, beta = 2, kappa = 0)
model |
Model of class |
alpha |
Positive tuning parameter of the UKF. Default is 0.001. Smaller the value, closer the sigma point are to the mean of the state. |
beta |
Non-negative tuning parameter of the UKF. The default value is 2, which is optimal for Gaussian states. |
kappa |
Non-negative tuning parameter of the UKF, which also affects the spread of sigma points. Default value is 0. |
List containing the log-likelihood,
one-step-ahead predictions at
and filtered
estimates att
of states, and the corresponding variances Pt
and
Ptt
.
# 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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