if(!require(pROC)) install.packages("pROC")
test data
data(aSAH)
if(!require(DT)) install.packages(DT)
DT::datatable(aSAH)
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Example 1
plot.roc(aSAH$outcome, aSAH$s100b, # data
percent = TRUE, # show all values in percent
partial.auc=c(100, 90),
partial.auc.correct=TRUE, # define a partial AUC (pAUC)
print.auc=TRUE,
#display pAUC value on the plot with following options:
print.auc.pattern = "Corrected pAUC (100-90%% SP):\n%.1f%%",
print.auc.col = "#1c61b6",
auc.polygon = TRUE,
auc.polygon.col = "#1c61b6", # show pAUC as a polygon
max.auc.polygon = TRUE,
max.auc.polygon.col = "#1c61b622", # also show the 100% polygon
main = "Partial AUC (pAUC)")
plot.roc(aSAH$outcome, aSAH$s100b,
percent = TRUE,
add = TRUE,
type = "n", # add to plot, but don't re-add the ROC itself (useless)
partial.auc = c(100, 90),
partial.auc.correct = TRUE,
partial.auc.focus = "se", # focus pAUC on the sensitivity
print.auc = TRUE,
print.auc.pattern = "Corrected pAUC (100-90%% SE):\n%.1f%%",
print.auc.col = "#008600",
print.auc.y = 40, # do not print auc over the previous one
auc.polygon = TRUE,
auc.polygon.col = "#008600",
max.auc.polygon = TRUE,
max.auc.polygon.col = "#00860022")
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Example 2
rocobj <- plot.roc(aSAH$outcome, aSAH$s100b,
main = "Confidence intervals",
percent=TRUE,
ci = TRUE, # compute AUC (of AUC by default)
print.auc = TRUE) # print the AUC (will contain the CI)
ciobj <- ci.se(rocobj, # CI of sensitivity
specificities = seq(0, 100, 5)) # over a select set of specificities
plot(ciobj, type = "shape", col = "#1c61b6AA") # plot as a blue shape
plot(ci(rocobj, of = "thresholds", thresholds = "best")) # add one threshold
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Example 3
data(aSAH)
rocobj1 <- plot.roc(aSAH$outcome, aSAH$s100,
main="Statistical comparison",
percent=TRUE,
col="#1c61b6")
rocobj2 <- lines.roc(aSAH$outcome, aSAH$ndka,
percent=TRUE,
col="#008600")
testobj <- roc.test(rocobj1, rocobj2)
text(50, 50, labels=paste("p-value =", format.pval(testobj$p.value)), adj=c(0, .5))
legend("bottomright", legend=c("S100B", "NDKA"), col=c("#1c61b6", "#008600"), lwd=2)
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