cal_plot_logistic function

Probability calibration plots via logistic regression

A logistic regression model is fit where the original outcome data are used as the outcome and the estimated class probabilities for one class are used as the predictor. If smooth = TRUE, a generalized additive model is fit using mgcv::gam() and the default smoothing method. Otherwise, a simple logistic regression is used.

If the predictions are well calibrated, the fitted curve should align with the diagonal line. Confidence intervals for the fitted line are also shown.

cal_plot_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  conf_level = 0.9,
  smooth = TRUE,
  include_rug = TRUE,
  include_ribbon = TRUE,
  event_level = c("auto", "first", "second"),
  ...
)

## S3 method for class 'data.frame'
cal_plot_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  conf_level = 0.9,
  smooth = TRUE,
  include_rug = TRUE,
  include_ribbon = TRUE,
  event_level = c("auto", "first", "second"),
  ...,
  .by = NULL
)

## S3 method for class 'tune_results'
cal_plot_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  conf_level = 0.9,
  smooth = TRUE,
  include_rug = TRUE,
  include_ribbon = TRUE,
  event_level = c("auto", "first", "second"),
  ...
)

## S3 method for class 'grouped_df'
cal_plot_logistic(
  .data,
  truth = NULL,
  estimate = NULL,
  conf_level = 0.9,
  smooth = TRUE,
  include_rug = TRUE,
  include_ribbon = TRUE,
  event_level = c("auto", "first", "second"),
  ...
)

Arguments

• .data: An ungrouped data frame object containing predictions and probability columns.
• truth: The column identifier for the true class results (that is a factor). This should be an unquoted column name.
• estimate: A vector of column identifiers, or one of dplyr selector functions to choose which variables contains the class probabilities. It defaults to the prefix used by tidymodels (.pred_). The order of the identifiers will be considered the same as the order of the levels of the truth variable.
• conf_level: Confidence level to use in the visualization. It defaults to 0.9.
• smooth: A logical for using a generalized additive model with smooth terms for the predictor via mgcv::gam() and mgcv::s().
• include_rug: Flag that indicates if the Rug layer is to be included. It defaults to TRUE. In the plot, the top side shows the frequency the event occurring, and the bottom the frequency of the event not occurring.
• include_ribbon: Flag that indicates if the ribbon layer is to be included. It defaults to TRUE.
• event_level: single string. Either "first" or "second" to specify which level of truth to consider as the "event". Defaults to "auto", which allows the function decide which one to use based on the type of model (binary, multi-class or linear)
• ...: Additional arguments passed to the tune_results object.
• .by: The column identifier for the grouping variable. This should be a single unquoted column name that selects a qualitative variable for grouping. Default to NULL. When .by = NULL no grouping will take place.

Returns

A ggplot object.

Examples

library(ggplot2)
library(dplyr)

cal_plot_logistic(
  segment_logistic,
  Class,
  .pred_good
)

cal_plot_logistic(
  segment_logistic,
  Class,
  .pred_good,
  smooth = FALSE
)

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_plot_windowed(), cal_plot_breaks()

cal_plot_breaks(), cal_plot_windowed()