bssm (version 2.0.2)

negbin_model: Estimated Negative Binomial Model of Helske and Vihola (2021)

Description

This model was used in Helske and Vihola (2021), but with larger number of iterations. Here only 2000 iterations were used in order to reduce the size of the model object in CRAN.

Format

A object of class mcmc_output.

References

Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R. The R Journal (2021) 13:2, 578-589. https://doi.org/10.32614/RJ-2021-103

Examples

# reproducing the model:
data("negbin_series")
# Construct model for bssm
bssm_model <- bsm_ng(negbin_series[, "y"],
  xreg = negbin_series[, "x"],
  beta = normal(0, 0, 10),
  phi = halfnormal(1, 10),
  sd_level = halfnormal(0.1, 1),
  sd_slope = halfnormal(0.01, 0.1),
  a1 = c(0, 0), P1 = diag(c(10, 0.1)^2),
  distribution = "negative binomial")

# \donttest{
# In the paper we used 60000 iterations with first 10000 as burnin
fit_bssm <- run_mcmc(bssm_model, iter = 2000, particles = 10, seed = 1)
fit_bssm
# }