compute_auc_val_ord {AutoScore}

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