AutoScore_testing_Ordinal {AutoScore}

AutoScore STEP(v) for ordinal outcomes: Evaluate the final score (AutoScore Module 6)

Description

AutoScore STEP(v) for ordinal outcomes: Evaluate the final score (AutoScore Module 6)

Usage

AutoScore_testing_Ordinal(
  test_set,
  final_variables,
  link = "logit",
  cut_vec,
  scoring_table,
  with_label = TRUE,
  n_boot = 100
)

Arguments

test_set

A processed data.frame that contains data for testing purpose. This data.frame should have same format as train_set (same variable names and outcomes)

final_variables

A vector containing the list of selected variables, selected from Step(ii) AutoScore_parsimony_Ordinal.

link

The link function used to model ordinal outcomes. Default is "logit" for proportional odds model. Other options are "cloglog" (proportional hazards model) and "probit".

cut_vec

Generated from STEP(iii) AutoScore_weighting_Ordinal.

scoring_table

The final scoring table after fine-tuning, generated from STEP(iv) AutoScore_fine_tuning_Ordinal.Please follow the guidebook

with_label

Set to TRUE if there are labels in the test_set and performance will be evaluated accordingly (Default:TRUE).

n_boot

Number of bootstrap cycles to compute 95% CI for performance metrics.

Value

A data frame with predicted score and the outcome for downstream visualization.

References

• Saffari SE, Ning Y, Feng X, Chakraborty B, Volovici V, Vaughan R, Ong ME, Liu N, AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes, arXiv:2202.08407

See Also

AutoScore_rank_Ordinal, AutoScore_parsimony_Ordinal, AutoScore_weighting_Ordinal, AutoScore_fine_tuning_Ordinal.

Examples

## Please see the guidebook or vignettes