Calculate and Optionally Plot a Design-Correct AUC for a Survey Model

This function calculates the Area Under the Curve (AUC) and its design-correct standard error and 95 the weighted ROC curve.

Usage

svyAUC(fit, design, plot = FALSE)

Arguments

fit: A fitted model object of class `svyglm`.
design: A replicate-weights survey design object, typically created with `as.svrepdesign`.
plot: A logical value. If `TRUE`, an ROC curve is plotted. Defaults to `FALSE`.

Value

If `plot = FALSE` (default), returns a `data.frame` with the AUC, SE, and 95 If `plot = TRUE`, invisibly returns a list containing the summary `data.frame` and another `data.frame` with the ROC curve coordinates (TPR and FPR).

Examples

if (FALSE) { # \dontrun{
# Ensure required packages are loaded
if (requireNamespace("survey", quietly = TRUE) &&
    requireNamespace("NHANES", quietly = TRUE) &&
    requireNamespace("dplyr", quietly = TRUE)) {

  # 1. Prepare Data
  data(NHANESraw, package = "NHANES")
  nhanes_data <- NHANESraw %>%
    dplyr::filter(Age >= 20) %>%
    dplyr::mutate(ObeseStatus = factor(ifelse(BMI >= 30, "Obese", "Not Obese"),
                                       levels = c("Not Obese", "Obese"))) %>%
    dplyr::filter(complete.cases(ObeseStatus, Age, Gender, Race1,
                                 WTMEC2YR, SDMVPSU, SDMVSTRA))

  # 2. Create a replicate design object
  std_design <- survey::svydesign(
    ids = ~SDMVPSU,
    strata = ~SDMVSTRA,
    weights = ~WTMEC2YR,
    nest = TRUE,
    data = nhanes_data
  )
  rep_design <- survey::as.svrepdesign(std_design)

  # 3. Fit a survey logistic regression model using the replicate design
  fit_obesity_rep <- survey::svyglm(
    ObeseStatus ~ Age + Gender + Race1,
    design = rep_design,
    family = quasibinomial()
  )

  # 4. Calculate the design-correct AUC
  auc_results <- svyAUC(fit_obesity_rep, rep_design)
  print(auc_results)
}
} # }