as.data.frame.mcmc_output: Convert MCMC Output to data.frame

In bssm: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

Convert MCMC Output to data.frame

Description

Converts the MCMC output of run_mcmc to data.frame.

Usage

## S3 method for class 'mcmc_output'
as.data.frame(
  x,
  row.names,
  optional,
  variable = c("theta", "states"),
  times,
  states,
  expand = TRUE,
  use_times = TRUE,
  ...
)

Arguments

x	Object of class mcmc_output from run_mcmc.
row.names	Ignored.
optional	Ignored.
variable	Return samples of "theta" (default) or "states"?
times	A vector of indices. In case of states, what time points to return? Default is all.
states	A vector of indices. In case of states, what states to return? Default is all.
expand	Should the jump-chain be expanded? Defaults to TRUE. For expand = FALSE and always for IS-MCMC, the resulting data.frame contains variable weight (= counts * IS-weights).
use_times	If TRUE (default), transforms the values of the time variable to match the ts attribute of the input to define. If FALSE, time is based on the indexing starting from 1.
...	Ignored.

See Also

as_draws which converts the output for as_draws object.

Examples

data("poisson_series")
model <- bsm_ng(y = poisson_series, 
sd_slope = halfnormal(0.1, 0.1), 
sd_level = halfnormal(0.1, 1),
  distribution = "poisson")
  
out <- run_mcmc(model, iter = 2000, particles = 10)
head(as.data.frame(out, variable = "theta"))
head(as.data.frame(out, variable = "state"))

# don't expand the jump chain:
head(as.data.frame(out, variable = "theta", expand = FALSE))

# IS-weighted version:
out_is <- run_mcmc(model, iter = 2000, particles = 10, 
  mcmc_type  = "is2")
head(as.data.frame(out_is, variable = "theta"))

bssm documentation built on May 4, 2022, 1:06 a.m.