pROC package

以下是本包中常用的一些缩写

ROC: receiver operating characteristic,ROC曲线
AUC: area under the ROC curve,曲线下面积
pAUC: partial area under the ROC curve 部分曲线下面积
CI: confidence interval 可信区间
SP: specificity 特异度
SE: sensitivity 灵敏度

require(pROC)

## Loading required package: pROC

## Type 'citation("pROC")' for a citation.

## 
## Attaching package: 'pROC'

## The following objects are masked from 'package:stats':
## 
##     cov, smooth, var

data(aSAH)
if(!require(DT)) install.packages(DT)

## Loading required package: DT

DT::datatable(aSAH)

aSAH[1:5,1:5]

##    gos6 outcome gender age wfns
## 29    5    Good Female  42    1
## 30    5    Good Female  37    1
## 31    5    Good Female  42    1
## 32    5    Good Female  27    1
## 33    1    Poor Female  42    3

roc函数建立roc曲线

支持在管道中运
参数分别为data, event, predict marker

library(dplyr)

## Warning: package 'dplyr' was built under R version 3.6.1

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

aSAH %>% 
    filter(gender == "Female") %>%
    roc(outcome, s100b)

## Setting levels: control = Good, case = Poor

## Setting direction: controls < cases

## 
## Call:
## roc.data.frame(data = ., response = outcome, predictor = s100b)
## 
## Data: s100b in 50 controls (outcome Good) < 21 cases (outcome Poor).
## Area under the curve: 0.72

coords函数中筛选有效的的坐标

transpose参数指返回值的格式,FALSE 为row
这样筛选出了敏感度和特异度>0.6的坐标

library(dplyr)
aSAH %>% 
    filter(gender == "Female") %>%
    roc(outcome, s100b) %>%
    coords(transpose=FALSE) %>%
    filter(sensitivity > 0.6, 
           specificity > 0.6)

## Setting levels: control = Good, case = Poor

## Setting direction: controls < cases

##   threshold specificity sensitivity
## 1     0.155        0.68   0.6666667
## 2     0.165        0.74   0.6666667
## 3     0.175        0.76   0.6666667
## 4     0.185        0.78   0.6666667
## 5     0.215        0.80   0.6666667
## 6     0.245        0.82   0.6666667
## 7     0.255        0.82   0.6190476

建立roc 对象的方法

# Build a ROC object and compute the AUC
roc(aSAH$outcome, aSAH$s100b)

## Setting levels: control = Good, case = Poor

## Setting direction: controls < cases

## 
## Call:
## roc.default(response = aSAH$outcome, predictor = aSAH$s100b)
## 
## Data: aSAH$s100b in 72 controls (aSAH$outcome Good) < 41 cases (aSAH$outcome Poor).
## Area under the curve: 0.7314

roc(outcome ~ s100b, aSAH)

## Setting levels: control = Good, case = Poor
## Setting direction: controls < cases

## 
## Call:
## roc.formula(formula = outcome ~ s100b, data = aSAH)
## 
## Data: s100b in 72 controls (outcome Good) < 41 cases (outcome Poor).
## Area under the curve: 0.7314

建立光滑曲线

# Smooth ROC curve
roc(outcome ~ s100b, aSAH, smooth=TRUE)

## Setting levels: control = Good, case = Poor

## Setting direction: controls < cases

## 
## Call:
## roc.formula(formula = outcome ~ s100b, data = aSAH, smooth = TRUE)
## 
## Data: s100b in 72 controls (outcome Good) < 41 cases (outcome Poor).
## Smoothing: binormal 
## Area under the curve: 0.74

可信区间与绘图

# more options, CI and plotting
roc1 <- roc(aSAH$outcome,
            aSAH$s100b, percent=TRUE,
            # arguments for auc
            partial.auc=c(100, 90), partial.auc.correct=TRUE,
            partial.auc.focus="sens",
            # arguments for ci
            ci=TRUE, boot.n=100, ci.alpha=0.9, stratified=FALSE,
            # arguments for plot
            plot=TRUE, auc.polygon=TRUE, max.auc.polygon=TRUE, grid=TRUE,
            print.auc=TRUE, show.thres=TRUE)

## Setting levels: control = Good, case = Poor

## Setting direction: controls < cases

## 在原有图形上继续绘制
roc2 <- roc(aSAH$outcome, aSAH$wfns,
            plot=TRUE, add=TRUE, percent=roc1$percent)

## Setting levels: control = Good, case = Poor
## Setting direction: controls < cases

找出感兴趣的坐标

## Coordinates of the curve ##
coords(roc1, "best", ret=c("threshold", "specificity", "1-npv"),transpose = FALSE
       )

##      threshold specificity 1-npv
## best     0.075    22.22222    20

coords(roc2, "local maximas", ret=c("threshold", "sens", "spec", "ppv", "npv"),transpose = FALSE)

##                 threshold sensitivity specificity      ppv      npv
## local.maximas        -Inf   100.00000     0.00000 36.28319      NaN
## local.maximas.1       1.5    95.12195    51.38889 52.70270 94.87179
## local.maximas.2       2.5    65.85366    79.16667 64.28571 80.28169
## local.maximas.3       3.5    63.41463    83.33333 68.42105 80.00000
## local.maximas.4       4.5    43.90244    94.44444 81.81818 74.72527
## local.maximas.5       Inf     0.00000   100.00000      NaN 63.71681

计算AUC可信区间

# CI of the AUC
ci(roc2)

## 95% CI: 74.85%-89.88% (DeLong)

plot在原有图形上增加

add=TRUE参数

roc1 <- roc(aSAH$outcome,
            aSAH$s100b, percent=TRUE,
            # arguments for auc
            partial.auc=c(100, 90), partial.auc.correct=TRUE,
            partial.auc.focus="sens",
            # arguments for ci
            ci=TRUE, boot.n=100, ci.alpha=0.9, stratified=FALSE,
            # arguments for plot
            plot=TRUE, auc.polygon=TRUE, max.auc.polygon=TRUE, grid=TRUE,
            print.auc=TRUE, show.thres=TRUE)

## Setting levels: control = Good, case = Poor

## Setting direction: controls < cases

plot(roc2, add=TRUE)

比较AUC

看是否有统计学意义

# Test on the whole AUC
roc.test(roc1, roc2, reuse.auc=FALSE)

## 
##  DeLong's test for two correlated ROC curves
## 
## data:  roc1 and roc2
## Z = -2.209, p-value = 0.02718
## alternative hypothesis: true difference in AUC is not equal to 0
## sample estimates:
## AUC of roc1 AUC of roc2 
##    73.13686    82.36789

绘制ROC曲线-基于ggplot2

创建roc对象
ggroc绘图

# Create a basic roc object
data(aSAH)
rocobj <- roc(aSAH$outcome, aSAH$s100b)

## Setting levels: control = Good, case = Poor

## Setting direction: controls < cases

rocobj2 <- roc(aSAH$outcome, aSAH$wfns)

## Setting levels: control = Good, case = Poor
## Setting direction: controls < cases

绘图

基础绘图

library(ggplot2)
g <- ggroc(rocobj)
g

美化参数设置

ggroc(rocobj, alpha = 0.5, colour = "red", linetype = 2, size = 2)

支持gglot2语法的美化

# You can then your own theme, etc.
g + theme_minimal() + ggtitle("My ROC curve") + 
    geom_segment(aes(x = 1, xend = 0, y = 0, yend = 1), color="grey", linetype="dashed")

修改横纵坐标

# And change axis labels to FPR/FPR
gl <- ggroc(rocobj, legacy.axes = TRUE)
gl

gl + xlab("FPR") + ylab("TPR") + 
    geom_segment(aes(x = 0, xend = 1, y = 0, yend = 1), color="darkgrey", linetype="dashed")

绘制多条曲线

1. ggroc以list格式包裹roc对象

# Multiple curves:
g2 <- ggroc(list(s100b=rocobj, wfns=rocobj2, ndka=roc(aSAH$outcome, aSAH$ndka)))

## Setting levels: control = Good, case = Poor

## Setting direction: controls < cases

g2

1. 也可先构建好公式，再绘制

# This is equivalent to using roc.formula:
roc.list <- roc(outcome ~ s100b + ndka + wfns, data = aSAH)

## Setting levels: control = Good, case = Poor

## Setting direction: controls < cases

## Setting levels: control = Good, case = Poor

## Setting direction: controls < cases

## Setting levels: control = Good, case = Poor

## Setting direction: controls < cases

g.list <- ggroc(roc.list)
g.list

美化修改

size设置线条粗细
alpha设置透明度

# with additional aesthetics:
g3 <- ggroc(roc.list, size = 1.2,alpha=.6)
g3+ggsci::scale_color_lancet()

改变参数

aes即按什么属性进行区分

g4 <- ggroc(roc.list, aes="linetype", color="red")
g4

按多种属性区分ROC曲线

# changing multiple aesthetics:
g5 <- ggroc(roc.list, aes=c("linetype", "color"))
g5

分面绘制ROC曲线

# OR faceting
g.list + facet_grid(.~name) + theme(legend.position="none")

所有曲线有相同颜色

group参数

# To have all the curves of the same color, use aes="group":
g.group <- ggroc(roc.list, aes="group",color="red")
g.group

g.group + facet_grid(.~name)

pROC package

Chard Liu

2019年7月30日

pROC package

roc函数建立roc曲线

coords函数中筛选有效的的坐标

建立roc 对象的方法

建立光滑曲线

可信区间与绘图

找出感兴趣的坐标

计算AUC可信区间

plot在原有图形上增加

比较AUC

绘制ROC曲线-基于ggplot2

绘图

支持gglot2语法的美化

修改横纵坐标

绘制多条曲线

美化修改

改变参数

按多种属性区分ROC曲线

分面绘制ROC曲线

所有曲线有相同颜色