In sbalci/ClinicoPathJamoviModule: Analysis for Clinicopathological Research

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

This vignette shows how to perform Receiver Operating Characteristic (ROC) analysis with meddecide using the psychopdaroc() function.

Loading the Data

df_roc <- read.csv(system.file("extdata", "roc_example.csv", package = "meddecide"))
head(df_roc)

Creating the ROC Curve

roc_res <- psychopdaroc(data = df_roc, class = df_roc$class, value = df_roc$value)
roc_res$plot

The resulting plot shows the ROC curve along with the area under the curve (AUC). You can extract the AUC value and other statistics from the result object.

roc_res$AUC

Bootstrapping and Confidence Intervals

auc_ci() and bootstrap_ci() provide convenience wrappers for computing confidence intervals for diagnostic metrics.

auc_ci(roc_res)
bootstrap_ci(roc_res)

These utilities allow you to assess the stability of your ROC analysis.

sbalci/ClinicoPathJamoviModule documentation built on June 13, 2025, 9:34 a.m.