bssm: importance_sample – R documentation

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importance_sample

Importance Sampling from non-Gaussian State Space Model

Description

Returns nsim samples from the approximating Gaussian model with corresponding (scaled) importance weights. Probably mostly useful for comparing KFAS and bssm packages.

Usage

importance_sample(model, nsim, use_antithetic, max_iter, conv_tol, seed, ...)

## S3 method for class 'nongaussian'
importance_sample(
  model,
  nsim,
  use_antithetic = TRUE,
  max_iter = 100,
  conv_tol = 1e-08,
  seed = sample(.Machine$integer.max, size = 1),
  ...
)

Arguments

`model`	of class `bsm_ng`, `ar1_ng` `svm`, `ssm_ung`, or `ssm_mng`.
`nsim`	Number of samples.
`use_antithetic`	Logical. If `TRUE` (default), use antithetic variable for location in simulation smoothing. Ignored for `ssm_mng` models.
`max_iter`	Maximum number of iterations used for the approximation.
`conv_tol`	Convergence threshold for the approximation. Approximation is claimed to be converged when the mean squared difference of the modes is less than `conv_tol`.
`seed`	Seed for the random number generator.
`...`	Ignored.

Examples

data("sexratio", package = "KFAS")
model <- bsm_ng(sexratio[, "Male"], sd_level = 0.001, u = sexratio[, "Total"],
  distribution = "binomial")

imp <- importance_sample(model, nsim = 1000)

est <- matrix(NA, 3, nrow(sexratio))
for(i in 1:ncol(est)) {
  est[, i] <- Hmisc::wtd.quantile(exp(imp$alpha[i, 1, ]), imp$weights, 
    prob = c(0.05,0.5,0.95), normwt=TRUE)
}

ts.plot(t(est),lty = c(2,1,2))

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