bssm: bsm_ng – R documentation

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bsm_ng

Non-Gaussian Basic Structural (Time Series) Model

Description

Constructs a non-Gaussian basic structural model with local level or local trend component, a seasonal component, and regression component (or subset of these components).

Usage

bsm_ng(
  y,
  sd_level,
  sd_slope,
  sd_seasonal,
  sd_noise,
  distribution,
  phi,
  u = 1,
  beta,
  xreg = NULL,
  period = frequency(y),
  a1,
  P1,
  C
)

Arguments

`y`	Vector or a `ts` object of observations.
`sd_level`	A fixed value or a prior for the standard error of the noise in level equation. See priors for details.
`sd_slope`	A fixed value or a prior for the standard error of the noise in slope equation. See priors for details. If missing, the slope term is omitted from the model.
`sd_seasonal`	A fixed value or a prior for the standard error of the noise in seasonal equation. See priors for details. If missing, the seasonal component is omitted from the model.
`sd_noise`	Prior for the standard error of the additional noise term. See priors for details. If missing, no additional noise term is used.
`distribution`	Distribution of the observed time series. Possible choices are `"poisson"`, `"binomial"`, `"gamma"`, and `"negative binomial"`.
`phi`	Additional parameter relating to the non-Gaussian distribution. For negative binomial distribution this is the dispersion term, for gamma distribution this is the shape parameter, and for other distributions this is ignored.
`u`	Constant parameter vector for non-Gaussian models. For Poisson, gamma, and negative binomial distribution, this corresponds to the offset term. For binomial, this is the number of trials.
`beta`	Prior for the regression coefficients.
`xreg`	Matrix containing covariates.
`period`	Length of the seasonal component i.e. the number of observations per season. Default is `frequency(y)`.
`a1`	Prior means for the initial states (level, slope, seasonals). Defaults to vector of zeros.
`P1`	Prior covariance for the initial states (level, slope, seasonals). Default is diagonal matrix with 1e5 on the diagonal.
`C`	Intercept terms for state equation, given as a m times n matrix.

Value

Object of class bsm_ng.

Examples

model <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson",
  sd_level = halfnormal(0.01, 1),
  sd_seasonal = halfnormal(0.01, 1),
  beta = normal(0, 0, 10),
  xreg = Seatbelts[, "law"])
## Not run: 
set.seed(123)
mcmc_out <- run_mcmc(model, iter = 5000, particles = 10)
mcmc_out$acceptance_rate
theta <- expand_sample(mcmc_out, "theta")
plot(theta)
summary(theta)

library("ggplot2")
ggplot(as.data.frame(theta[,1:2]), aes(x = sd_level, y = sd_seasonal)) +
  geom_point() + stat_density2d(aes(fill = ..level.., alpha = ..level..),
  geom = "polygon") + scale_fill_continuous(low = "green", high = "blue") +
  guides(alpha = "none")
  
# Traceplot using as.data.frame method for MCMC output:
library("dplyr")
as.data.frame(mcmc_out) %>% 
  filter(variable == "sd_level") %>% 
  ggplot(aes(y = value, x = iter)) + geom_line()

## End(Not run)

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