bayesplot (version 1.16.0)

PPC-calibration: PPC calibration

Description

Assess the calibration of the predictions, or predictive probabilities in relation to binary observations. See the Plot Descriptions and Details sections below and also the PPC Calibration vignette for more details and examples.

Usage

ppc_calibration_overlay(y, prep, ..., linewidth = 0.25, alpha = 0.2)
ppc_calibration_overlay_grouped(
  y,
  prep,
  group,
  ...,
  linewidth = 0.25,
  alpha = 0.2
)
ppc_calibration(
  y,
  prep = NULL,
  yrep = NULL,
  prob = 0.95,
  interval = c("confidence", "consistency"),
  help_text = TRUE,
  B = 200,
  show_mean = TRUE,
  show_qdots = TRUE,
  qdots_quantiles = 100,
  ...,
  linewidth = 1,
  alpha = 0.1
)
ppc_calibration_grouped(
  y,
  yrep = NULL,
  prep = NULL,
  group,
  prob = 0.95,
  interval = c("confidence", "consistency"),
  help_text = TRUE,
  B = 200,
  show_mean = TRUE,
  show_qdots = TRUE,
  qdots_quantiles = 100,
  ...,
  linewidth = 1,
  alpha = 0.1
)
ppc_loo_calibration(
  y,
  yrep,
  lw = NULL,
  psis_object = NULL,
  prob = 0.95,
  interval = c("confidence", "consistency"),
  help_text = TRUE,
  B = 200,
  show_mean = TRUE,
  show_qdots = TRUE,
  qdots_quantiles = 100,
  ...,
  linewidth = 1,
  alpha = 0.1
)
ppc_loo_calibration_grouped(
  y,
  yrep,
  lw = NULL,
  psis_object = NULL,
  group,
  prob = 0.95,
  interval = c("confidence", "consistency"),
  help_text = TRUE,
  B = 200,
  show_mean = TRUE,
  show_qdots = TRUE,
  qdots_quantiles = 100,
  ...,
  linewidth = 1,
  alpha = 0.1
)
ppc_calibration_data(
  y,
  prep = NULL,
  yrep = NULL,
  group = NULL,
  type = c("overlay", "interval"),
  prob = 0.95,
  interval = c("confidence", "consistency"),
  B = 200
)

Value

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.

Arguments

y

A vector of observations. See Details.

prep

For ppc_calibration(), ppc_calibration_grouped(), ppc_calibration_overlay(), and ppc_calibration_overlay_grouped(), an S by N matrix of predicted probabilities in [0, 1], where S is the number of draws and N the number of observations (N = length(y)).

...

Currently unused.

linewidth, alpha

Arguments passed to geoms controlling line width and opacity.

group

A grouping variable of the same length as y. Will be coerced to factor if not already a factor. Each value in group is interpreted as the group level pertaining to the corresponding observation.

yrep

An S by N matrix of draws from the posterior (or prior) predictive distribution, or a posterior::draws object. The number of rows, S, is the size of the posterior (or prior) sample used to generate yrep. The number of columns, N is the number of predicted observations (length(y)). The columns of yrep should be in the same order as the data points in y for the plots to make sense. See the Details and Plot Descriptions sections for additional advice specific to particular plots.

prob

For ppc_calibration(), ppc_calibration_grouped(), ppc_loo_calibration(), and ppc_loo_calibration_grouped(). Probability used to compute the uncertainty intervals. Defaults to 0.95.

interval

For ppc_calibration(), ppc_calibration_grouped(), ppc_loo_calibration(), and ppc_loo_calibration_grouped(), pointwise uncertainty interval around the calibration curve. Choose "confidence" (default) to answer the question: "Where does the calibration curve of the model lie?" or "consistency" to answer the question: "If the model is correctly specified, where would we expect the calibration curve to fall?".

help_text

For ppc_calibration(), ppc_calibration_grouped(), ppc_loo_calibration(), and ppc_loo_calibration_grouped(), if TRUE (default) display a label in the plot indicating the interval type as CI (confidence) or CsI (consistency) with the selected prob.

B

For ppc_calibration(), ppc_calibration_grouped(), ppc_loo_calibration(), and ppc_loo_calibration_grouped() that use yrep with interval = "confidence", the number of bootstrap samples. Default is 200. Ignored if prep is used or interval = "consistency".

show_mean

For ppc_calibration(), ppc_calibration_grouped(), ppc_loo_calibration(), and ppc_loo_calibration_grouped(), if TRUE (default), draw the estimated calibration curve.

show_qdots

For ppc_calibration(), ppc_calibration_grouped(), ppc_loo_calibration(), and ppc_loo_calibration_grouped(), if TRUE (default) add a quantile dot plot at the bottom of the panel to show the marginal distribution of predicted probabilities.

qdots_quantiles

For ppc_calibration(), ppc_calibration_grouped(), ppc_loo_calibration(), and ppc_loo_calibration_grouped(), positive integer indicating the number of dots in the quantile dot plot. Default is 100.

lw

For ppc_loo_calibration() and ppc_loo_calibration_grouped(), a matrix of log weights with the same dimensions as yrep. Either psis_object or lw has to be specified.

psis_object

For ppc_loo_calibration() and ppc_loo_calibration_grouped(), an object of class "psis" that is created when the loo() function calls psis() internally to do the PSIS procedure. Either psis_object or lw has to be specified.

type

For ppc_calibration_data(), the data structure to compute: "overlay" for ppc_calibration_overlay() or "interval" for ppc_calibration() and their corresponding _grouped and _loo variants.

Plot Descriptions

ppc_calibration(),ppc_calibration_grouped()

PAV-adjusted calibration plots showing the relationship between the predicted event probabilities and the conditional event probabilities. The interval parameter controls whether confidence intervals, or consistency intervals are computed around the calibration curve.

ppc_calibration_overlay(),ppc_calibration_overlay_grouped()

Overlay plots showing posterior samples of PAV-adjusted calibration curves for each posterior draw, which can be used to visually assess the uncertainty in the calibration curve.

ppc_loo_calibration(),ppc_loo_calibration_grouped()

PAV-adjusted calibration plots to assess the calibration of the leave-one-out (LOO) predictive probabilities, computed by resampling each observation's posterior predictive draws using LOO importance weights.

ppc_calibration_data()

Data frame containing the data underlying the calibration plots, which can be used to build custom calibration plots. The type argument controls whether the data frame for ppc_calibration_overlay() and its _grouped`` variant is computed (type = "overlay"), or the data frame for ppc_calibration()and its_groupedor_loo variant is computed (type = "interval"`).

Details

The PPC calibration functions are designed to assess the calibration of a model with binary outcomes. In this context, calibration refers to the agreement between predicted probabilities and conditional event probabilities (CEPs) see Dimitriadis et al. (2021) and Säilynoja et al. (2025) for details.

The required inputs are y, representing binary observations (0 or 1), and either yrep or prep. Specifically, ppc_calibration_overlay() and ppc_calibration_overlay_grouped() require prep, while ppc_calibration(), ppc_calibration_grouped(), ppc_loo_calibration(), and ppc_loo_calibration_grouped() accept either prep or yrep.

prep or yrep.

A document with detailed explanations and examples is available in the vignettes.

References

Dimitriadis, T., Gneiting, T., & Jordan, A. I. (2021). Stable reliability diagrams for probabilistic classifiers. Proceedings of the National Academy of Sciences, 118(8). https://doi.org/10.1073/pnas.2016191118

Säilynoja, T., Johnson, A. R., Martin, O. A., & Vehtari, A. (2025). Recommendations for visual predictive checks in Bayesian workflow. (Preprint). arXiv. https://doi.org/10.48550/arXiv.2503.01509

See Also

Other PPCs: PPC-censoring, PPC-discrete, PPC-distributions, PPC-errors, PPC-intervals, PPC-loo, PPC-overview, PPC-scatterplots, PPC-test-statistics

Examples

color_scheme_set("brightblue")

# Make an example dataset of binary observations
ymin <- range(example_y_data(), example_yrep_draws())[1]
ymax <- range(example_y_data(), example_yrep_draws())[2]
y <- rbinom(length(example_y_data()), 1, (example_y_data() - ymin) / (ymax - ymin))
prep <- (example_yrep_draws() - ymin) / (ymax - ymin)

ppc_calibration_overlay(y, prep[1:50, ])

# Compare confidence vs consistency intervals
ppc_calibration(y, prep, interval = "confidence")
ppc_calibration(y, prep, interval = "consistency")