predict.fasi: Prediction of a FASI Object
In fasi: Fair Adjusted Selective Inference

Description Usage Arguments Value Author(s) Examples

View source: R/fasi.R

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

## S3 method for class '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",
  ...
)

`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

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.

Bradley Rava. PhD Candidate at the University of Southern California's Marshall School of Business. Department of Data Sciences and Operations.

1 2	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)