cal_plot_regression {probably}

Regression calibration plots

Description

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 class 'data.frame'
cal_plot_regression(
  .data,
  truth = NULL,
  estimate = NULL,
  smooth = TRUE,
  ...,
  .by = NULL
)

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

## S3 method for class '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
)