roc_neat: Difference of Two Areas Under the Curves

Description

Comparison of two areas under the receiver operating characteristic curves (AUCs) and plotting any number of ROC curves.

Usage

roc_neat(
  roc1,
  roc2 = NULL,
  pair = FALSE,
  greater = NULL,
  ci = NULL,
  hush = FALSE,
  plot_rocs = FALSE,
  roc_labels = "",
  cutoff_auto = TRUE,
  cutoff_custom = NULL
)

Value

Prints DeLong's test results for the comparison of the two given AUCs in APA style, as well as corresponding CI for the AUC difference. Furthermore, when assigned, returns a list with stat (D value),

p (p value), and, when plot is added, ROC plot.

Arguments

roc1: Receiver operating characteristic (ROC) object, or, for plotting only, a list including any number of such ROC objects.
roc2: Receiver operating characteristic (ROC) object, or, for plotting only, leave it as NULL (default) and provide list for the first parameter (roc1).
pair: Logical. If TRUE, the test is conducted for paired samples. Otherwise (default) for independent samples.
greater: NULL or string (or number); optionally specifies one-sided test: either "1" (roc1 AUC expected to be greater than roc2 AUC) or "2" (roc2 AUC expected to be greater than roc2 AUC). If NULL (default), the test is two-sided.
ci: Numeric; confidence level for the returned CIs (raw difference).
hush: Logical. If TRUE, prevents printing any details to console (and plotting).
plot_rocs: Logical. If TRUE, plots and returns ROC curves.
roc_labels: Optional character vector to provide legend label texts (in the order of the provided ROC objects) for the ROC plot.
cutoff_auto: Logical. If TRUE (default), optimal cutoffs on the ROC plots are displayed.
cutoff_custom: Custom cutoff to be indicated on the plot can be given here in a list. The list index must exactly correspond to the index of the list index of the AUC (given in roc1) for which the given cutoff is intended.

References

Altman, D. G., & Bland, J. M. (2011). How to obtain the confidence interval from a P value. Bmj, 343(d2090). tools:::Rd_expr_doi("https://doi.org/10.1136/bmj.d2090")

DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics, 44(3), 837-845. tools:::Rd_expr_doi("https://doi.org/10.2307/2531595")

Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J. C., & Muller, M. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC bioinformatics, 12(1), 77. tools:::Rd_expr_doi("https://doi.org/10.1186/1471-2105-12-77")

Examples

Run this code


# calculate first AUC (from v1 and v2)
v1 = c(191, 115, 129, 43, 523,-4, 34, 28, 33,-1, 54)
v2 = c(4,-2, 23, 13, 32, 16, 3, 29, 37,-4, 65)
results1 = t_neat(v1, v2, auc_added = TRUE)

# calculate second AUC (from v3 and v4)
v3 = c(14.1, 58.5, 25.5, 42.2, 13, 4.4, 55.5, 28.5, 25.6, 37.1)
v4 = c(36.2, 45.2, 41, 24.6, 30.5, 28.2, 40.9, 45.1, 31, 16.9)
results2 = t_neat(v3, v4, auc_added = TRUE)

# one-sided comparison of the two AUCs
roc_neat(results1$roc_obj, results2$roc_obj, greater = "1")


# create a list of randomlz generated AUCs
set.seed(1)
aucs_list = list()
for (i in 1:4) {
    aucs_list[[i]] = t_neat(rnorm(50, (i-1)),
                            rnorm(50),
                            auc_added = TRUE,
                            hush = TRUE)$roc_obj
}
# depict AUCs (recognized as list)
roc_neat(aucs_list)

# \donttest{
# with custom cutoffs depicted
roc_neat(aucs_list,
         cutoff_custom = list(0.2),
         cutoff_auto = FALSE)
roc_neat(aucs_list,
         cutoff_custom = list(.1, c(-.5, 0), NULL, c(.7, 1.6)),
         cutoff_auto = FALSE)
roc_neat(aucs_list,
         cutoff_custom = list(.6, NULL, NULL, 1.1))
# }