fasi (version 1.0.0)

predict.fasi: Prediction of a FASI Object

Description

After a model is trained with the fasi function, predict estimates the r-scores and classification of all observations in the test data set.

Usage

# S3 method for fasi
predict(
  object,
  test_data,
  alpha_1,
  alpha_2,
  rscore_plus = TRUE,
  ptd_group_var = "a",
  class_label = "y",
  ranking_score_calibrate,
  ranking_score_test,
  indecision_choice = "2",
  ...
)

Arguments

object

An object of class fasi. It can be created from the fasi function.

test_data

The test data set that contains new observations to be classified.

alpha_1

User specified group and overall FSR control for class 1.

alpha_2

User specified group and overall FSR control for class 2.

rscore_plus

A logical variable that indicates if the r-score or r-score plus is calculated. By default the r-score plus is calculated.

ptd_group_var

The name of the protected group variable in your data set. Defaults to "a".

class_label

The name of the class label variable in your data set. Defaults to "y".

ranking_score_calibrate

A vector of ranking scores for the calibration data set. This should only be used if the built in ranking score algorithms are not used.

ranking_score_test

A vector of ranking scores for the test data set. This should only be used if the built in ranking score algorithms are not used.

indecision_choice

A number, 1, 2, or 3. This determines how the indecision cases are treated if we are equally confident in placing them in both class 1 and 2. Defaults to the scenario where class 2 is preferred

...

Additional arguments

Value

A list where the first element is the r-scores for both class 1 and class 2. The second element is the actual classifications, class 1, class 2 or the indecision class. The third element is a logical value True/False that denotes if the r-score or r-score plus was calculated. The last element in the list is the values of alpha for both classes. Alpha can directly be compared to the r-scores to obtain the classifications.

Examples

# NOT RUN {
fasi_object <- fasi(observed_data, model_formula, split_p=0.5, alg="gam", class_label="y")
predict(fasi_object, test_data, alpha_1=0.1, alpha_2=0.1)
# }