plot_roc_components: Plot the components of a ROC curve by the high risk...
In DecisionCurve: Calculate and Plot Decision Curves

Plot the components of the ROC curve –the true positive rates and false positive rates– by high risk thresholds.

plot_roc_components(x, cost.benefit.axis = TRUE, n.cost.benefits = 6,
  cost.benefits, confidence.intervals, col = "black", lty.fpr = 2,
  lty.tpr = 1, lwd = 2, xlim, ylim, xlab = "Risk Threshold", ylab,
  cost.benefit.xlab = "Cost:Benefit Ratio", legend.position = c("topright",
  "right", "bottomright", "bottom", "bottomleft", "left", "topleft", "top",
  "none"), ...)

`x`	decision_curve object to plot. Assumes output from function 'decision_curve'
`cost.benefit.axis`	logical (default TRUE) indicating whether to print an additional x-axis showing relative cost:benefit ratios in addition to risk thresholds.
`n.cost.benefits`	number of cost:benefit ratios to print if cost.benefit.axis = TRUE (default n.cost.benefit = 6).
`cost.benefits`	Character vector of the form c("c1:b1", "c2:b2", ..., "cn:bn") with integers ci, bi corresponding to specific cost:benefit ratios to print. Default allows the function to calculate these automatically.
`confidence.intervals`	logical indicating whether to plot confidence intervals.
`col`	vector of length two indicating the color for the true positive rates and false positive rates, respectively.
`lty.fpr`	linetype for the false positive rate curve.
`lty.tpr`	linetype for the true positive rate curve.
`lwd`	vector of linewidths. The first element corresponds to the tpr and the second to the fpr.
`xlim`	vector giving c(min, max) of x-axis. Defaults to c(min(thresholds), max(thresholds)).
`ylim`	vector giving c(min, max) of y-axis.
`xlab`	label of main x-axis.
`ylab`	label of y-axis.
`cost.benefit.xlab`	label of cost:benefit ratio axis.
`legend.position`	character vector giving position of legend. Options are "topright" (default), "right", "bottomright", "bottom", "bottomleft", "left", "topleft", "top", or "none".
`...`	other options directly send to plot()

data(dcaData)
set.seed(123)
baseline.model <- decision_curve(Cancer~Age + Female + Smokes,
                                data = dcaData,
                                thresholds = seq(0, .4, by = .001),
                                bootstraps = 25) #should use more bootstrap replicates in practice!

#plot using the defaults
plot_roc_components(baseline.model,  xlim = c(0, 0.4), col = c("black", "red"))