compute_prob_observed: Internal function: Based on given labels and scores, compute...

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

Internal function: Based on given labels and scores, compute proportion of subjects observed in each outcome category in given score intervals.

Description

Internal function: Based on given labels and scores, compute proportion of subjects observed in each outcome category in given score intervals.

Usage

compute_prob_observed(
  pred_score,
  link = "logit",
  max_score = 100,
  score_breaks = seq(from = 5, to = 70, by = 5)
)

Arguments

pred_score	A data.frame with outcomes and final scores generated from AutoScore_fine_tuning_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".
max_score	Maximum attainable value of final scores.
score_breaks	A vector of score breaks to group scores. The average predicted risk will be reported for each score interval in the lookup table. Users are advised to first visualise the predicted risk for all attainable scores to determine scores (see plot_predicted_risk)

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