Description Usage Arguments Value Examples

Converts 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. Note though that if `run_mcmc`

used IS-MCMC
method, the resulting `weight`

column of the output is
ignored by the aforementioned packages, i.e. the results correspond to
approximate MCMC.

1 2 3 |

`x` |
An object of class |

A `draws_df`

object.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
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)
library("posterior")
draws <- as_draws(fit1)
head(draws, 4)
ess_bulk(draws$sd_y)
summary(fit1, return_se = TRUE)
# More chains:
model$theta[] <- c(50, 150) # change initial value
fit2 <- run_mcmc(model, iter = 2000)
model$theta[] <- c(150, 50) # change initial value
fit3 <- run_mcmc(model, iter = 2000)
draws <- bind_draws(as_draws(fit1),
as_draws(fit2), as_draws(fit3), along = "chain")
# it is actually enough to transform first mcmc_output to draws object,
# rest are transformed automatically inside bind_draws
rhat(draws$sd_y)
ess_bulk(draws$sd_y)
ess_tail(draws$sd_y)
``` |

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