cal_estimate_logistic function

Uses a logistic regression model to calibrate probabilities

cal_estimate_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  smooth = TRUE,
  parameters = NULL,
  ...
)

## S3 method for class 'data.frame'
cal_estimate_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  smooth = TRUE,
  parameters = NULL,
  ...,
  .by = NULL
)

## S3 method for class 'tune_results'
cal_estimate_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  smooth = TRUE,
  parameters = NULL,
  ...
)

## S3 method for class 'grouped_df'
cal_estimate_logistic(
  .data,
  truth = NULL,
  estimate = NULL,
  smooth = TRUE,
  parameters = NULL,
  ...
)

Arguments

• .data: An ungrouped data.frame object, or tune_results object, that contains 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.
• smooth: Applies to the logistic models. It switches between logistic spline when TRUE, and simple logistic regression when FALSE.
• parameters: (Optional) An optional tibble of tuning parameter values that can be used to filter the predicted values before processing. Applies only to tune_results objects.
• ...: Additional arguments passed to the models or routines used to calculate the new probabilities.
• .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.

Details

This function uses existing modeling functions from other packages to create the calibration:

• stats::glm() is used when smooth is set to FALSE
• mgcv::gam() is used when smooth is set to TRUE

Multiclass Extension

This method has not been extended to multiclass outcomes. However, the natural multiclass extension is cal_estimate_multinomial().

Examples

# It will automatically identify the probability columns
# if passed a model fitted with tidymodels
cal_estimate_logistic(segment_logistic, Class)

# Specify the variable names in a vector of unquoted names
cal_estimate_logistic(segment_logistic, Class, c(.pred_poor, .pred_good))

# dplyr selector functions are also supported
cal_estimate_logistic(segment_logistic, Class, dplyr::starts_with(".pred_"))

See Also

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