R語言pROC包繪制ROC曲線

作者：白介素2
相關(guān)閱讀：
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生存曲線

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pROC package

以下是本包中常用的一些縮寫

  • ROC: receiver operating characteristic,ROC曲線

  • AUC: area under the ROC curve,曲線下面積

  • pAUC: partial area under the ROC curve 部分曲線下面積

  • CI: confidence interval 可信區(qū)間

  • SP: specificity 特異度

  • SE: sensitivity 靈敏度

require(pROC)
data(aSAH)
if(!require(DT)) install.packages(DT)
DT::datatable(aSAH)
aSAH[1:5,1:5]
image.png

roc函數(shù)建立roc曲線

  • 支持在管道中運(yùn)行
  • 參數(shù)分別為data, event, predict marker
library(dplyr)
aSAH %>% 
    filter(gender == "Female") %>%
    roc(outcome, s100b)
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函數(shù)中篩選有效的的坐標(biāo)

  • transpose參數(shù)指返回值的格式,FALSE 為row
  • 這樣篩選出了敏感度和特異度>0.6的坐標(biāo)
library(dplyr)
aSAH %>% 
    filter(gender == "Female") %>%
    roc(outcome, s100b) %>%
    coords(transpose=FALSE) %>%
    filter(sensitivity > 0.6, 
           specificity > 0.6)
 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 對象的方法

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

建立光滑曲線

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

可信區(qū)間與繪圖

# 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)
## 在原有圖形上繼續(xù)繪制
roc2 <- roc(aSAH$outcome, aSAH$wfns,
            plot=TRUE, add=TRUE, percent=roc1$percent)
image.png

找出感興趣的坐標(biāo)

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

計(jì)算AUC可信區(qū)間

# CI of the AUC
ci(roc2)

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

plot在原有圖形上增加

  • add=TRUE參數(shù)
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)
plot(roc2, add=TRUE)
image.png

比較AUC

  • 看是否有統(tǒng)計(jì)學(xué)意義
# 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

繪制ROC曲線-基于ggplot2

  1. 創(chuàng)建roc對象
  2. ggroc繪圖
# Create a basic roc object
data(aSAH)
rocobj <- roc(aSAH$outcome, aSAH$s100b)
rocobj2 <- roc(aSAH$outcome, aSAH$wfns)

繪圖

  1. 基礎(chǔ)繪圖
library(ggplot2)
g <- ggroc(rocobj)
g
image.png
  1. 美化參數(shù)設(shè)置
ggroc(rocobj, alpha = 0.5, colour = "red", linetype = 2, size = 2)
image.png

支持gglot2語法的美化

# 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")
image.png

修改橫縱坐標(biāo)

# 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")
image.png

image.png

繪制多條曲線

    1. ggroc以list格式包裹roc對象
# Multiple curves:
g2 <- ggroc(list(s100b=rocobj, wfns=rocobj2, ndka=roc(aSAH$outcome, aSAH$ndka)))
g2
image.png
    1. 也可先構(gòu)建好公式，再繪制
# This is equivalent to using roc.formula:
roc.list <- roc(outcome ~ s100b + ndka + wfns, data = aSAH)
g.list <- ggroc(roc.list)
g.list
image.png

美化修改

  • size設(shè)置線條粗細(xì)
  • alpha設(shè)置透明度
# with additional aesthetics:
g3 <- ggroc(roc.list, size = 1.2,alpha=.6)
g3+ggsci::scale_color_lancet()
image.png

改變參數(shù)

  • aes即按什么屬性進(jìn)行區(qū)分
g4 <- ggroc(roc.list, aes="linetype", color="red")
g4
image.png

按多種屬性區(qū)分ROC曲線

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

分面繪制ROC曲線

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

所有曲線有相同顏色

  • group參數(shù)
# 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)
image.png

我是白介素2，本期內(nèi)容就到這里，下期再見。

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