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.