tests/testthat/_snaps/cal-estimate-logistic.md

In topepo/probably: Tools for Post-Processing Predicted Values

Logistic estimates work - data.frame

Code
  print(sl_logistic)
Message

  -- Probability Calibration 
  Method: Logistic regression calibration
  Type: Binary
  Source class: Data Frame
  Data points: 1,010
  Truth variable: `Class`
  Estimate variables:
  `.pred_good` ==> good
  `.pred_poor` ==> poor

The selectors in `estimate` resolves to 1 values (".pred_poor") but there are 2 class levels ("good" and "poor").

The `truth` column has 4 levels ("VF", "F", "M", and "L"), but only two-class factors are allowed for this calibration method.

Code
  print(sl_logistic_group)
Message

  -- Probability Calibration 
  Method: Logistic regression calibration
  Type: Binary
  Source class: Data Frame
  Data points: 1,010, split in 2 groups
  Truth variable: `Class`
  Estimate variables:
  `.pred_good` ==> good
  `.pred_poor` ==> poor

x `.by` cannot select more than one column.
i The following columns were selected:
i group1 and group2

Logistic estimates work - tune_results

Code
  print(tl_logistic)
Message

  -- Probability Calibration 
  Method: Logistic regression calibration
  Type: Binary
  Source class: Tune Results
  Data points: 4,000, split in 8 groups
  Truth variable: `class`
  Estimate variables:
  `.pred_class_1` ==> class_1
  `.pred_class_2` ==> class_2

The `truth` column has 3 levels ("one", "two", and "three"), but only two-class factors are allowed for this calibration method.

Logistic estimates errors - grouped_df

x This function does not work with grouped data frames.
i Apply `dplyr::ungroup()` and use the `.by` argument.

Logistic spline estimates work - data.frame

Code
  print(sl_gam)
Message

  -- Probability Calibration 
  Method: Generalized additive model calibration
  Type: Binary
  Source class: Data Frame
  Data points: 1,010
  Truth variable: `Class`
  Estimate variables:
  `.pred_good` ==> good
  `.pred_poor` ==> poor

Code
  print(sl_gam_group)
Message

  -- Probability Calibration 
  Method: Generalized additive model calibration
  Type: Binary
  Source class: Data Frame
  Data points: 1,010, split in 2 groups
  Truth variable: `Class`
  Estimate variables:
  `.pred_good` ==> good
  `.pred_poor` ==> poor

x `.by` cannot select more than one column.
i The following columns were selected:
i group1 and group2

Logistic spline estimates work - tune_results

Code
  print(tl_gam)
Message

  -- Probability Calibration 
  Method: Generalized additive model calibration
  Type: Binary
  Source class: Tune Results
  Data points: 4,000, split in 8 groups
  Truth variable: `class`
  Estimate variables:
  `.pred_class_1` ==> class_1
  `.pred_class_2` ==> class_2

Logistic spline switches to linear if too few unique

Code
  sl_gam <- cal_estimate_logistic(segment_logistic, Class, smooth = TRUE)
Condition
  Warning:
  Too few unique observations for spline-based calibrator. Setting `smooth = FALSE`.

Code
  sl_gam <- cal_estimate_logistic(segment_logistic, Class, .by = id, smooth = TRUE)
Condition
  Warning:
  Too few unique observations for spline-based calibrator. Setting `smooth = FALSE`.
  Warning:
  Too few unique observations for spline-based calibrator. Setting `smooth = FALSE`.

topepo/probably documentation built on June 8, 2025, 4:23 a.m.