View source: R/cal-plot-windowed.R

cal_plot_windowed | R Documentation |

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.

```
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 class '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 class '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 class '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"),
...
)
```

`.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 |

`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 |

`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 |

`include_rug` |
Flag that indicates if the Rug layer is to be included.
It defaults to |

`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 |

`.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 |

A ggplot object.

https://www.tidymodels.org/learn/models/calibration/,
`cal_plot_logistic()`

, `cal_plot_breaks()`

`cal_plot_breaks()`

, `cal_plot_logistic()`

```
library(ggplot2)
library(dplyr)
cal_plot_windowed(
segment_logistic,
Class,
.pred_good
)
# More breaks
cal_plot_windowed(
segment_logistic,
Class,
.pred_good,
window_size = 0.05
)
```

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