adjust-numeric-calibration.md
In tailor: Iterative Steps for Postprocessing Model Predictions

adjustment printing

Code
  adjust_numeric_calibration(tailor())
Message

  -- tailor ----------------------------------------------------------------------
  A regression postprocessor with 1 adjustment:

  * Re-calibrate numeric predictions using method.

Code
  adjust_numeric_calibration(tailor(), method = "isotonic")
Message

  -- tailor ----------------------------------------------------------------------
  A regression postprocessor with 1 adjustment:

  * Re-calibrate numeric predictions using isotonic method.

Code
  adjust_numeric_calibration(tailor(), method = hardhat::tune())
Message

  -- tailor ----------------------------------------------------------------------
  A regression postprocessor with 1 adjustment:

  * Re-calibrate numeric predictions (method marked for optimization).

Code
  fit(adjust_numeric_calibration(tailor()), mtcars, outcome = mpg, estimate = disp)
Message

  -- tailor ----------------------------------------------------------------------
  A regression postprocessor with 1 adjustment:

  * Re-calibrate numeric predictions using method. [trained]

errors informatively with bad input

Code
  adjust_numeric_calibration(tailor(), "boop")
Condition
  Error in `adjust_numeric_calibration()`:
  ! `method` must be one of "linear", "isotonic", "isotonic_boot", or "none", not "boop".

Code
  adjust_numeric_calibration(tailor(), "binary")
Condition
  Error in `adjust_numeric_calibration()`:
  ! `method` must be one of "linear", "isotonic", "isotonic_boot", or "none", not "binary".
  i Did you mean "linear"?

tuning the calibration method

Code
  fit(tlr, d_reg_calibration, outcome = y, estimate = y_pred)
Condition
  Error in `fit()`:
  ! The calibration method cannot be a value of `tune()` at `fit()` time.

too few data

Code
  fit(tlr, d_reg_calibration[0, ], outcome = y, estimate = y_pred)
Condition
  Warning:
  The calibration data has 0 rows. There is not enough data for calibration so `method` is changed from "linear" to "none".
Message

  -- tailor ----------------------------------------------------------------------
  A regression postprocessor with 1 adjustment:

  * Re-calibrate numeric predictions using linear method. [trained]

Code
  fit(tlr, d_reg_calibration[1, ], outcome = y, estimate = y_pred)
Condition
  Warning:
  The calibration data has 1 row. There is not enough data for calibration so `method` is changed from "linear" to "none".
Message

  -- tailor ----------------------------------------------------------------------
  A regression postprocessor with 1 adjustment:

  * Re-calibrate numeric predictions using linear method. [trained]

passing arguments to adjust_numeric_calibration

Code
  tlr_fit <- adjust_numeric_calibration(tailor(), method = "linear", FALSE)
Condition
  Error in `adjust_numeric_calibration()`:
  ! All calibration arguments passed to `...` should have names.

Code
  tlr_fit <- adjust_numeric_calibration(tailor(), method = "linear", FALSE,
  select = TRUE)
Condition
  Error in `adjust_numeric_calibration()`:
  ! All calibration arguments passed to `...` should have names.

harden against calibration model failure

Code
  pred_fit <- fit(tlr, d_calibration_pred, outcome = y, estimate = y_pred)
Condition
  Warning:
  Too few unique observations for spline-based calibrator. Setting `smooth = FALSE`.
  Warning in `glm.fit()`:
  no observations informative at iteration 1
  Warning:
  glm.fit: algorithm did not converge
  Warning:
  The linear calibration failed. No calibration is applied.
  i Error in fit$rank : object of type 'closure' is not subsettable

Code
  y_fit <- fit(tlr, d_calibration_y, outcome = y, estimate = y_pred)
Condition
  Warning:
  The linear calibration failed. No calibration is applied.
  i Error in mgcv::gam(f_model, data = .data, ...) : Not enough (non-NA) data to do anything meaningful