A plot is created to assess whether the observed rate of the event is about
the sample as the predicted probability of the event from some model. This
is similar to `cal_plot_breaks()`

, except that the bins are overlapping.

A sequence of bins are created from zero to one. For each bin, the data whose predicted probability falls within the range of the bin is used to calculate the observed event rate (along with confidence intervals for the event rate).

If the predictions are well calibrated, the fitted curve should align with the diagonal line.

## Usage

```
cal_plot_windowed(
.data,
truth = NULL,
estimate = dplyr::starts_with(".pred"),
window_size = 0.1,
step_size = window_size/2,
conf_level = 0.9,
include_ribbon = TRUE,
include_rug = TRUE,
include_points = TRUE,
event_level = c("auto", "first", "second"),
...
)
# S3 method for data.frame
cal_plot_windowed(
.data,
truth = NULL,
estimate = dplyr::starts_with(".pred"),
window_size = 0.1,
step_size = window_size/2,
conf_level = 0.9,
include_ribbon = TRUE,
include_rug = TRUE,
include_points = TRUE,
event_level = c("auto", "first", "second"),
...,
.by = NULL
)
# S3 method for tune_results
cal_plot_windowed(
.data,
truth = NULL,
estimate = dplyr::starts_with(".pred"),
window_size = 0.1,
step_size = window_size/2,
conf_level = 0.9,
include_ribbon = TRUE,
include_rug = TRUE,
include_points = TRUE,
event_level = c("auto", "first", "second"),
...
)
# S3 method for grouped_df
cal_plot_windowed(
.data,
truth = NULL,
estimate = NULL,
window_size = 0.1,
step_size = window_size/2,
conf_level = 0.9,
include_ribbon = TRUE,
include_rug = TRUE,
include_points = 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.- window_size
The size of segments. Used for the windowed probability calculations. It defaults to 10% of segments.

- step_size
The gap between segments. Used for the windowed probability calculations. It defaults to half the size of

`window_size`

- conf_level
Confidence level to use in the visualization. It defaults to 0.9.

- include_ribbon
Flag that indicates if the ribbon layer is to be included. It defaults to

`TRUE`

.- 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_points
Flag that indicates if the point layer is to be included.

- 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.