Regression calibration plots — cal_plot

A scatter plot of the observed and predicted values is computed where the axes are the same. When smooth = TRUE, a generalized additive model fit is shown. If the predictions are well calibrated, the fitted curve should align with the diagonal line.

Usage

cal_plot_regression(.data, truth = NULL, estimate = NULL, smooth = TRUE, ...)

# S3 method for data.frame
cal_plot_regression(
  .data,
  truth = NULL,
  estimate = NULL,
  smooth = TRUE,
  ...,
  .by = NULL
)

# S3 method for tune_results
cal_plot_regression(.data, truth = NULL, estimate = NULL, smooth = TRUE, ...)

# S3 method for grouped_df
cal_plot_regression(.data, truth = NULL, estimate = NULL, smooth = TRUE, ...)

Arguments

.data: An ungrouped data frame object containing a prediction column.
truth: The column identifier for the true results (numeric). This should be an unquoted column name.
estimate: The column identifier for the predictions. This should be an unquoted column name
smooth: A logical: should a smoother curve be added.
...: Additional arguments passed to ggplot2::geom_point().
.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.

Value

A ggplot object.

Examples

cal_plot_regression(boosting_predictions_oob, outcome, .pred)


cal_plot_regression(boosting_predictions_oob, outcome, .pred,
  alpha = 1 / 6, cex = 3, smooth = FALSE
)


cal_plot_regression(boosting_predictions_oob, outcome, .pred,
  .by = id,
  alpha = 1 / 6, cex = 3, smooth = FALSE
)