Function ekpf_filter performs a extended Kalman particle filtering
with stratification resampling, based on Van Der Merwe et al (2001).
ekpf_filter(model, particles, ...)# S3 method for ssm_nlg
ekpf_filter(
model,
particles,
seed = sample(.Machine$integer.max, size = 1),
...
)
A list containing samples, filtered estimates and the corresponding covariances, weights, and an estimate of log-likelihood.
Model of class ssm_nlg.
Number of particles as a positive integer. Suitable values depend on the model and the data, and while larger values provide more accurate estimates, the run time also increases with respect to the number of particles, so it is generally a good idea to test the filter first with a small number of particles, e.g., less than 100.
Ignored.
Seed for the C++ RNG (positive integer).
Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. (2001). The unscented particle filter. In Advances in neural information processing systems (pp. 584-590).
# Takes a while
set.seed(1)
n <- 50
x <- y <- numeric(n)
y[1] <- rnorm(1, exp(x[1]), 0.1)
for(i in 1:(n-1)) {
x[i+1] <- rnorm(1, sin(x[i]), 0.1)
y[i+1] <- rnorm(1, exp(x[i+1]), 0.1)
}
pntrs <- cpp_example_model("nlg_sin_exp")
model_nlg <- ssm_nlg(y = y, a1 = pntrs$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$T_gn,
theta = c(log_H = log(0.1), log_R = log(0.1)),
log_prior_pdf = pntrs$log_prior_pdf,
n_states = 1, n_etas = 1, state_names = "state")
out <- ekpf_filter(model_nlg, particles = 100)
ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3)
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