DecisionCurve (version 1.4)

plot_roc_components: Plot the components of a ROC curve by the high risk thresholds.

Description

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

Usage

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"), ...)

Arguments

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()

Examples

# NOT RUN {
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"))


# }