PPD-intervals {bayesplot} | R Documentation |

Medians and central interval estimates of posterior or prior predictive
distributions. Each of these functions makes the same plot as the
corresponding `ppc_`

function but without plotting any
observed data `y`

. The **Plot Descriptions** section at PPC-intervals has
details on the individual plots.

```
ppd_intervals(
ypred,
x = NULL,
...,
prob = 0.5,
prob_outer = 0.9,
alpha = 0.33,
size = 1,
fatten = 2.5,
linewidth = 1
)
ppd_intervals_grouped(
ypred,
x = NULL,
group,
...,
facet_args = list(),
prob = 0.5,
prob_outer = 0.9,
alpha = 0.33,
size = 1,
fatten = 2.5,
linewidth = 1
)
ppd_ribbon(
ypred,
x = NULL,
...,
prob = 0.5,
prob_outer = 0.9,
alpha = 0.33,
size = 0.25
)
ppd_ribbon_grouped(
ypred,
x = NULL,
group,
...,
facet_args = list(),
prob = 0.5,
prob_outer = 0.9,
alpha = 0.33,
size = 0.25
)
ppd_intervals_data(
ypred,
x = NULL,
group = NULL,
...,
prob = 0.5,
prob_outer = 0.9
)
ppd_ribbon_data(
ypred,
x = NULL,
group = NULL,
...,
prob = 0.5,
prob_outer = 0.9
)
```

`ypred` |
An |

`x` |
A numeric vector to use as the x-axis
variable. For example, |

`...` |
Currently unused. |

`prob` , `prob_outer` |
Values between |

`alpha` , `size` , `fatten` , `linewidth` |
Arguments passed to geoms. For ribbon
plots |

`group` |
A grouping variable of the same length as |

`facet_args` |
A named list of arguments (other than |

The plotting functions return a ggplot object that can be further
customized using the **ggplot2** package. The functions with suffix
`_data()`

return the data that would have been drawn by the plotting
function.

Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and
Gelman, A. (2019), Visualization in Bayesian workflow.
*J. R. Stat. Soc. A*, 182: 389-402. doi:10.1111/rssa.12378.
(journal version,
arXiv preprint,
code on GitHub)

Other PPDs:
`PPD-distributions`

,
`PPD-overview`

,
`PPD-test-statistics`

```
color_scheme_set("brightblue")
ypred <- example_yrep_draws()
x <- example_x_data()
group <- example_group_data()
ppd_intervals(ypred[, 1:50])
ppd_intervals(ypred[, 1:50], fatten = 0)
ppd_intervals(ypred[, 1:50], fatten = 0, linewidth = 2)
ppd_intervals(ypred[, 1:50], prob_outer = 0.75, fatten = 0, linewidth = 2)
# put a predictor variable on the x-axis
ppd_intervals(ypred[, 1:100], x = x[1:100], fatten = 1) +
ggplot2::labs(y = "Prediction", x = "Some variable of interest")
# with a grouping variable too
ppd_intervals_grouped(
ypred = ypred[, 1:100],
x = x[1:100],
group = group[1:100],
size = 2,
fatten = 0,
facet_args = list(nrow = 2)
)
# even reducing size, ppd_intervals is too cluttered when there are many
# observations included (ppd_ribbon is better)
ppd_intervals(ypred, size = 0.5, fatten = 0.1, linewidth = 0.5)
ppd_ribbon(ypred)
ppd_ribbon(ypred, size = 0) # remove line showing median prediction
```

[Package *bayesplot* version 1.10.0 Index]