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bssm

logLik.ssm_nlg

Log-likelihood of a Non-linear State Space Model

Description

Computes the log-likelihood of a state space model of class ssm_nlg package.

Usage

## S3 method for class 'ssm_nlg'
logLik(
  object,
  particles,
  method = "bsf",
  max_iter = 100,
  conv_tol = 1e-08,
  iekf_iter = 0,
  seed = sample(.Machine$integer.max, size = 1),
  ...
)

Arguments

object

Model model.
particles

Number of samples for particle filter. If 0, approximate log-likelihood is returned either based on the Gaussian approximation or EKF, depending on the method argument.
method

Sampling method. Default is the bootstrap particle filter ("bsf"). Other choices are "psi" which uses psi-auxiliary filter (or approximating Gaussian model in the case of particles = 0), and "ekf" which uses EKF-based particle filter (or just EKF approximation in the case of particles = 0).
max_iter

Maximum number of iterations for gaussian approximation algorithm.
conv_tol

Tolerance parameter for the approximation algorithm.
iekf_iter

If iekf_iter > 0, iterated extended Kalman filter is used with iekf_iter iterations in place of standard EKF. Defaults to zero.
seed

Seed for the random number generator.
...

Ignored.
bssm

Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

v1.1.4
GPL (>= 2)
Authors
Jouni Helske [aut, cre] (<https://orcid.org/0000-0001-7130-793X>), Matti Vihola [aut] (<https://orcid.org/0000-0002-8041-7222>)
Initial release
2021-04-13

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