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 logdensity 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

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 logdensity given the the states and parameter vector theta. 
prior_pdf 
An external pointer for the C++ function which computes the prior logdensity 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 ISMCMC helps a lot
out < run_mcmc(sde_model, L_c = 4, L_f = 8,
particles = 50, iter = 2e4,
threads = 4L)
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