tests/testthat/_snaps/cal-estimate.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
  Type: Binary
  Source class: Data Frame
  Data points: 1,010
  Truth variable: `Class`
  Estimate variables:
  `.pred_good` ==> good
  `.pred_poor` ==> poor

We can't connect the specified prediction columns to some factor levels (good). The selected columns were .pred_poor. Are there more columns to add in the function call?

The number of outcome factor levels isn't consistent with the calibration method. Only two class `truth` factors are allowed. The given levels were: 'VF', 'F', 'M', 'L'

Code
  print(sl_logistic_group)
Message

  -- Probability Calibration 
  Method: Logistic regression
  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 2 columns were selected:
i group1 and group2

Logistic estimates work - tune_results

Code
  print(tl_logistic)
Message

  -- Probability Calibration 
  Method: Logistic regression
  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 number of outcome factor levels isn't consistent with the calibration method. Only two class `truth` factors are allowed. The given levels were: '.pred_one', '.pred_two', '.pred_three'

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
  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
  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 2 columns were selected:
i group1 and group2

Logistic spline estimates work - tune_results

Code
  print(tl_gam)
Message

  -- Probability Calibration 
  Method: Generalized additive model
  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

Isotonic estimates work - data.frame

Code
  print(sl_isotonic)
Message

  -- Probability Calibration 
  Method: Isotonic regression
  Type: Binary
  Source class: Data Frame
  Data points: 1,010
  Unique Predicted Values: 66
  Truth variable: `Class`
  Estimate variables:
  `.pred_good` ==> good
  `.pred_poor` ==> poor

Code
  print(sl_isotonic_group)
Message

  -- Probability Calibration 
  Method: Isotonic regression
  Type: Binary
  Source class: Data Frame
  Data points: 1,010, split in 2 groups
  Unique Predicted Values: 59
  Truth variable: `Class`
  Estimate variables:
  `.pred_good` ==> good
  `.pred_poor` ==> poor

x `.by` cannot select more than one column.
i The following 2 columns were selected:
i group1 and group2

Isotonic estimates work - tune_results

Code
  print(tl_isotonic)
Message

  -- Probability Calibration 
  Method: Isotonic regression
  Type: Binary
  Source class:
  Data points: 4,000, split in 8 groups
  Unique Predicted Values: 86
  Truth variable: `class`
  Estimate variables:
  `.pred_class_1` ==> class_1
  `.pred_class_2` ==> class_2

Code
  print(mtnl_isotonic)
Message

  -- Probability Calibration 
  Method: Isotonic regression
  Type: Multiclass (1 v All)
  Source class:
  Data points: 5,000, split in 10 groups
  Truth variable: `class`
  Estimate variables:
  `.pred_one` ==> one
  `.pred_two` ==> two
  `.pred_three` ==> three

Isotonic estimates errors - grouped_df

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

Isotonic linear estimates work - data.frame

Code
  print(sl_logistic)
Message

  -- Probability Calibration 
  Method: Isotonic regression
  Type: Regression
  Source class: Data Frame
  Data points: 2,000
  Unique Predicted Values: 43
  Truth variable: `outcome`
  Estimate variables:
  `.pred` ==> predictions

Code
  print(sl_logistic_group)
Message

  -- Probability Calibration 
  Method: Isotonic regression
  Type: Regression
  Source class: Data Frame
  Data points: 2,000, split in 10 groups
  Unique Predicted Values: 11
  Truth variable: `outcome`
  Estimate variables:
  `.pred` ==> predictions

x `.by` cannot select more than one column.
i The following 2 columns were selected:
i group1 and group2

Isotonic Bootstrapped estimates work - data.frame

Code
  print(sl_boot)
Message

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

Code
  print(sl_boot_group)
Message

  -- Probability Calibration 
  Method: Bootstrapped isotonic regression
  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 2 columns were selected:
i group1 and group2

Isotonic Bootstrapped estimates work - tune_results

Code
  print(tl_isotonic)
Message

  -- Probability Calibration 
  Method: Bootstrapped isotonic regression
  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

Code
  print(mtnl_isotonic)
Message

  -- Probability Calibration 
  Method: Bootstrapped isotonic regression
  Type: Multiclass (1 v All)
  Source class: Tune Results
  Data points: 5,000, split in 10 groups
  Truth variable: `class`
  Estimate variables:
  `.pred_one` ==> one
  `.pred_two` ==> two
  `.pred_three` ==> three

Isotonic Bootstrapped estimates errors - grouped_df

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

Beta estimates work - data.frame

Code
  print(sl_beta)
Message

  -- Probability Calibration 
  Method: Beta 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_beta_group)
Message

  -- Probability Calibration 
  Method: Beta 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 2 columns were selected:
i group1 and group2

Beta estimates work - tune_results

Code
  print(tl_beta)
Message

  -- Probability Calibration 
  Method: Beta 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

Code
  print(mtnl_isotonic)
Message

  -- Probability Calibration 
  Method: Beta calibration
  Type: Multiclass (1 v All)
  Source class: Tune Results
  Data points: 5,000, split in 10 groups
  Truth variable: `class`
  Estimate variables:
  `.pred_one` ==> one
  `.pred_two` ==> two
  `.pred_three` ==> three

Beta estimates errors - grouped_df

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

Multinomial estimates work - data.frame

Code
  print(sp_multi)
Message

  -- Probability Calibration 
  Method: Multinomial regression
  Type: Multiclass
  Source class: Data Frame
  Data points: 110
  Truth variable: `Species`
  Estimate variables:
  `.pred_bobcat` ==> bobcat
  `.pred_coyote` ==> coyote
  `.pred_gray_fox` ==> gray_fox

Code
  print(sp_smth_multi)
Message

  -- Probability Calibration 
  Method: Generalized additive model
  Type: Multiclass
  Source class: Data Frame
  Data points: 110
  Truth variable: `Species`
  Estimate variables:
  `.pred_bobcat` ==> bobcat
  `.pred_coyote` ==> coyote
  `.pred_gray_fox` ==> gray_fox

Code
  print(sl_multi_group)
Message

  -- Probability Calibration 
  Method: Multinomial regression
  Type: Multiclass
  Source class: Data Frame
  Data points: 110, split in 2 groups
  Truth variable: `Species`
  Estimate variables:
  `.pred_bobcat` ==> bobcat
  `.pred_coyote` ==> coyote
  `.pred_gray_fox` ==> gray_fox

x `.by` cannot select more than one column.
i The following 2 columns were selected:
i group1 and group2

Multinomial estimates work - tune_results

Code
  print(tl_multi)
Message

  -- Probability Calibration 
  Method: Multinomial regression
  Type: Multiclass
  Source class: Tune Results
  Data points: 5,000, split in 10 groups
  Truth variable: `class`
  Estimate variables:
  `.pred_one` ==> one
  `.pred_two` ==> two
  `.pred_three` ==> three

Code
  print(tl_smth_multi)
Message

  -- Probability Calibration 
  Method: Generalized additive model
  Type: Multiclass
  Source class: Tune Results
  Data points: 5,000, split in 10 groups
  Truth variable: `class`
  Estimate variables:
  `.pred_one` ==> one
  `.pred_two` ==> two
  `.pred_three` ==> three

Multinomial estimates errors - grouped_df

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

Linear estimates work - data.frame

Code
  print(sl_logistic)
Message

  -- Regression Calibration 
  Method: Linear
  Source class: Data Frame
  Data points: 2,000
  Truth variable: `outcome`
  Estimate variable: `.pred`

Code
  print(sl_logistic_group)
Message

  -- Regression Calibration 
  Method: Linear
  Source class: Data Frame
  Data points: 2,000, split in 2 groups
  Truth variable: `outcome`
  Estimate variable: `.pred`

x `.by` cannot select more than one column.
i The following 2 columns were selected:
i group1 and group2

Linear estimates work - tune_results

Code
  print(tl_linear)
Message

  -- Regression Calibration 
  Method: Linear
  Source class: Tune Results
  Data points: 750, split in 10 groups
  Truth variable: `outcome`
  Estimate variable: `.pred`

Linear estimates errors - grouped_df

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

Linear spline estimates work - data.frame

Code
  print(sl_gam)
Message

  -- Regression Calibration 
  Method: Generalized additive model
  Source class: Data Frame
  Data points: 2,000
  Truth variable: `outcome`
  Estimate variable: `.pred`

Code
  print(sl_gam_group)
Message

  -- Regression Calibration 
  Method: Generalized additive model
  Source class: Data Frame
  Data points: 2,000, split in 2 groups
  Truth variable: `outcome`
  Estimate variable: `.pred`

x `.by` cannot select more than one column.
i The following 2 columns were selected:
i group1 and group2

Linear spline estimates work - tune_results

Code
  print(tl_gam)
Message

  -- Regression Calibration 
  Method: Generalized additive model
  Source class: Tune Results
  Data points: 750, split in 10 groups
  Truth variable: `outcome`
  Estimate variable: `.pred`

Non-default names used for estimate columns

Code
  cal_estimate_isotonic(new_segment, Class, c(good, poor))
Message

  -- Probability Calibration 
  Method: Isotonic regression
  Type: Binary
  Source class: Data Frame
  Data points: 1,010
  Unique Predicted Values: 66
  Truth variable: `Class`
  Estimate variables:
  `good` ==> good
  `poor` ==> poor

topepo/probably documentation built on April 6, 2024, 7:32 p.m.