# plot_roc_components: Plot the components of a ROC curve by the high risk... In mdbrown/rmda: Risk Model Decision Analysis

## Description

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

## Usage

 ```1 2 3 4 5 6``` ```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

 ```1 2 3 4 5 6 7 8 9``` ```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")) ```

mdbrown/rmda documentation built on May 30, 2019, 6:19 p.m.