draw_data_roc: Generate binary data (ROC model)

In cases: Stratified Evaluation of Subgroup Classification Accuracy

Generate binary data (ROC model)

Description

Generate binary data (ROC model)

Usage

draw_data_roc(
  n = 100,
  prev = c(0.5, 0.5),
  random = FALSE,
  m = 10,
  auc = seq(0.85, 0.95, length.out = 5),
  rho = c(0.25, 0.25),
  dist = c("normal", "exponential"),
  e = 10,
  k = 100,
  delta = 0,
  modnames = paste0("model", 1:m),
  corrplot = FALSE,
  ...
)

Arguments

n	(numeric) total sample size
prev	(numeric) disease and healthy prevalence (adds up to 1)
random	(logical) random sampling (TRUE) or fixed prevalence (FALSE)
m	(numeric) integer, number of models
auc	(numeric) vector of AUCs of biomarkers
rho	(numeric) vector (length 2) of correlations between biomarkers
dist	(character) either "normal" or "exponential" specifying the subgroup biomarker distributions
e	(numeric) emulates better (worse) model selection quality with higher (lower) values of e
k	(numeric) technical parameter which adjusts grid size
delta	(numeric) specify importance of sensitivity and specificity (default 0)
modnames	(character) model names (length m)
corrplot	(logical) if TRUE do not return data but instead plot correlation matrices for final binary data (default: FALSE)
...	(any) further arguments (currently unused)

Value

(list)
list of matrices including generated binary datasets (1: correct prediction, 0: incorrect prediction) for each subgroup (specificity, sensitivity)

Examples

data <- draw_data_roc()
head(data)

cases documentation built on April 3, 2025, 9:24 p.m.