as_draws_df.mcmc_output | R Documentation |

`run_mcmc`

Output to `draws_df`

FormatConverts MCMC output from `run_mcmc`

call to a
`draws_df`

format of the `posterior`

package. This enables the use
of diagnostics and plotting methods of `posterior`

and `bayesplot`

packages.

```
## S3 method for class 'mcmc_output'
as_draws_df(x, times, states, ...)
## S3 method for class 'mcmc_output'
as_draws(x, times, states, ...)
```

`x` |
An object of class |

`times` |
A vector of indices defining which time points to return? Default is all. If 0, no samples for the states are extracted. |

`states` |
A vector of indices defining which states to return. Default is all. If 0, no samples for the states are extracted. |

`...` |
Ignored. |

A `draws_df`

object.

The jump chain representation is automatically expanded by
`as_draws`

, but if `run_mcmc`

used IS-MCMC method, the output
contains additional `weight`

column corresponding to the IS-weights
(without counts), which is ignored by `posterior`

and `bayesplot`

,
i.e. those results correspond to approximate MCMC.

```
model <- bsm_lg(Nile,
sd_y = tnormal(init = 100, mean = 100, sd = 100, min = 0),
sd_level = tnormal(init = 50, mean = 50, sd = 100, min = 0),
a1 = 1000, P1 = 500^2)
fit1 <- run_mcmc(model, iter = 2000)
draws <- as_draws(fit1)
head(draws, 4)
estimate_ess(draws$sd_y)
summary(fit1, return_se = TRUE)
# More chains:
model$theta[] <- c(50, 150) # change initial value
fit2 <- run_mcmc(model, iter = 2000, verbose = FALSE)
model$theta[] <- c(150, 50) # change initial value
fit3 <- run_mcmc(model, iter = 2000, verbose = FALSE)
# it is actually enough to transform first mcmc_output to draws object,
# rest are transformed automatically inside bind_draws
draws <- posterior::bind_draws(as_draws(fit1),
as_draws(fit2), as_draws(fit3), along = "chain")
posterior::rhat(draws$sd_y)
```

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