Description Usage Arguments Examples

Interface to bayesplot's `mcmc_pairs`

function
for use with rstanarm models. Be careful not to specify too
many parameters to include or the plot will be both hard to read and slow to
render.

1 2 3 |

`x` |
A fitted model object returned by one of the
rstanarm modeling functions. See |

`pars` |
An optional character vetor of parameter names. All parameters are included by default, but for models with more than just a few parameters it may be far too many to visualize on a small computer screen and also may require substantial computing time. |

`regex_pars` |
An optional character vector of regular
expressions to use for parameter selection. |

`condition` |
Same as the |

`...` |
Optional arguments passed to |

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 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ```
if (!exists("example_model")) example(example_model)
bayesplot::color_scheme_set("purple")
# see 'condition' argument above for details on the plots below and
# above the diagonal. default is to split by accept_stat__.
pairs(example_model, pars = c("(Intercept)", "log-posterior"))
pairs(
example_model,
regex_pars = "herd:[2,7,9]",
diag_fun = "dens",
off_diag_fun = "hex"
)
# for demonstration purposes, intentionally fit a model that
# will (almost certainly) have some divergences
fit <- stan_glm(
mpg ~ ., data = mtcars,
iter = 1000,
# this combo of prior and adapt_delta should lead to some divergences
prior = hs(),
adapt_delta = 0.9
)
pairs(fit, pars = c("wt", "sigma", "log-posterior"))
pairs(
fit,
pars = c("wt", "sigma", "log-posterior"),
transformations = list(sigma = "log"), # show log(sigma) instead of sigma
off_diag_fun = "hex" # use hexagonal heatmaps instead of scatterplots
)
bayesplot::color_scheme_set("brightblue")
pairs(
fit,
pars = c("(Intercept)", "wt", "sigma", "log-posterior"),
transformations = list(sigma = "log"),
off_diag_args = list(size = 3/4, alpha = 1/3), # size and transparency of scatterplot points
np_style = pairs_style_np(div_color = "black", div_shape = 2) # color and shape of the divergences
)
# Using the condition argument to show divergences above the diagonal
pairs(
fit,
pars = c("(Intercept)", "wt", "log-posterior"),
condition = pairs_condition(nuts = "divergent__")
)
``` |

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