ssm_sde | R Documentation |
Constructs an object of class ssm_sde
by defining the functions for
the drift, diffusion and derivative of diffusion terms of univariate SDE,
as well as the log-density of observation equation. We assume that the
observations are measured at integer times (missing values are allowed).
ssm_sde( y, drift, diffusion, ddiffusion, obs_pdf, prior_pdf, theta, x0, positive )
y |
Observations as univariate time series (or vector) of length n. |
drift, diffusion, ddiffusion |
An external pointers for the C++ functions which define the drift, diffusion and derivative of diffusion functions of SDE. |
obs_pdf |
An external pointer for the C++ function which computes the observational log-density given the the states and parameter vector theta. |
prior_pdf |
An external pointer for the C++ function which computes the prior log-density given the parameter vector theta. |
theta |
Parameter vector passed to all model functions. |
x0 |
Fixed initial value for SDE at time 0. |
positive |
If |
As in case of ssm_nlg
models, these general models need a bit more
effort from the user, as you must provide the several small C++ snippets
which define the model structure. See vignettes for an example and
cpp_example_model
.
An object of class ssm_sde
.
# Takes a while on CRAN library("sde") set.seed(1) # theta_0 = rho = 0.5 # theta_1 = nu = 2 # theta_2 = sigma = 0.3 x <- sde.sim(t0 = 0, T = 50, X0 = 1, N = 50, drift = expression(0.5 * (2 - x)), sigma = expression(0.3), sigma.x = expression(0)) y <- rpois(50, exp(x[-1])) # source c++ snippets pntrs <- cpp_example_model("sde_poisson_OU") sde_model <- ssm_sde(y, pntrs$drift, pntrs$diffusion, pntrs$ddiffusion, pntrs$obs_density, pntrs$prior, c(rho = 0.5, nu = 2, sigma = 0.3), 1, positive = FALSE) est <- particle_smoother(sde_model, L = 12, particles = 500) ts.plot(cbind(x, est$alphahat, est$alphahat - 2*sqrt(c(est$Vt)), est$alphahat + 2*sqrt(c(est$Vt))), col = c(2, 1, 1, 1), lty = c(1, 1, 2, 2)) # Takes time with finer mesh, parallelization with IS-MCMC helps a lot out <- run_mcmc(sde_model, L_c = 4, L_f = 8, particles = 50, iter = 2e4, threads = 4L)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.