compute_auc_val_ord: Internal function: Compute mean AUC for ordinal outcomes...

In AutoScore: An Interpretable Machine Learning-Based Automatic Clinical Score Generator

Internal function: Compute mean AUC for ordinal outcomes based on validation set for plotting parsimony

Description

Compute mean AUC based on validation set for plotting parsimony

Usage

compute_auc_val_ord(
  train_set_1,
  validation_set_1,
  variable_list,
  link,
  categorize,
  quantiles,
  max_cluster,
  max_score
)

Arguments

train_set_1	Processed training set
validation_set_1	Processed validation set
variable_list	List of included variables
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".
categorize	Methods for categorize continuous variables. Options include "quantile" or "kmeans"
quantiles	Predefined quantiles to convert continuous variables to categorical ones. Available if categorize = "quantile".
max_cluster	The max number of cluster (Default: 5). Available if categorize = "kmeans".
max_score	Maximum total score

Value

A list of mAUC for parsimony plot

AutoScore documentation built on Oct. 16, 2022, 1:06 a.m.