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librarylibrary(pkgsearch)
找与ROC相关的包
该包会提供一系列关于感兴趣主题的R包,包括他们的评分,作者,连接等等
ps函数等价于pkg_search
size:定义返回结果数量
format="short"返回格式
Sys.setlocale('LC_ALL','C') rocPkg "ROC",size=200) head(rocPkg) class(rocPkg) [1] "C" - "ROC" ------------------------------------- 74 packages in 0.01 seconds - # package version 1 100 pROC 1.15.0 2 44 caTools 1.17.1.2 3 18 survivalROC 1.0.3 4 18 PRROC 1.3.1 5 15 plotROC 2.2.1 6 14 precrec 0.10.1 by @ Xavier Robin 2M ORPHANED 4M Paramita Saha-Chaudhuri000a> 7y Jan Grau 1y Michael C. Sachs 1y Takaya Saito 3M [1] "pkg_search_result" "tbl_df" "tbl" [4] "data.frame"
ROCR包
performance函数计算tpr,fpr
library(ROCR) data(ROCR.simple) df head(df) ## predictions labels ## 1 0.6125478 1 ## 2 0.3642710 1 ## 3 0.4321361 0 ## 4 0.1402911 0 ## 5 0.3848959 0 ## 6 0.2444155 1 pred perf perf plot(perf,colorize=TRUE)
plotROC包-ggplot绘制ROC曲线
ROC曲线用于评估连续测量的精度,以预测二进制结果。在医学上,ROC曲线用于评价放射学和一般诊断的诊断试验有着悠久的历史。ROC曲线在信号检测理论中也有很长的应用历史。
require(plotROC)
提供网页版操作,为了代码的连贯性,这里不介绍网页版,不可能我们分析到一般导出数据,拿到网页版去操作。
基本用法
set.seed(2529) D.ex M1 M2 test M1 = M1, M2 = M2, stringsAsFactors = FALSE) head(test) ## D D.str M1 M2 ## 1 1 Ill 1.48117155 -2.50636605 ## 2 1 Ill 0.61994478 1.46861033 ## 3 0 Healthy 0.57613345 0.07532573 ## 4 1 Ill 0.85433197 2.41997703 ## 5 0 Healthy 0.05258342 0.01863718 ## 6 1 Ill 0.66703989 0.24732453
geom_roc绘图
d为编码1/0, m为用于预测的值marker
注意需要一个disease code,不一定是1/0,但最后选择编码为1/0
如不1/0,则stat_roc默认按顺序最低值为无病状态
basicplot <- ggplot(test, aes(d = D, m = M1)) + geom_roc()basicplot
- 若diseaase编码非1/0:
提示warning但仍能继续
ggplot(test, aes(d = D.str, m = M1)) + geom_roc()
n.cuts参数:展示几个截断点
labelsize: 展示标签的大小
labelround: label值保留几位小数
ggplot(test, aes(d = D, m = M1)) + geom_roc(n.cuts = 5, labelsize = 5, labelround = 2)
修改style-style_roc函数
styledplot styledplot
修改xlab, 主题
basicplot + style_roc(theme = theme_grey, xlab = "1 - Specificity")
multiROC-多因素诊断
meltroc类似于dplyr中的gather。转换数据为长数据,原数据为两列marker
head(test) ## D M name ## M11 1 1.48117155 M1 ## M12 1 0.61994478 M1 ## M13 0 0.57613345 M1 ## M14 1 0.85433197 M1 ## M15 0 0.05258342 M1 ## M16 1 0.66703989 M1 longtest head(longtest) table(longtest$name) ## ROC曲线比较 ggplot(longtest, aes(d = D, m = M, color = name)) + geom_roc() + style_roc()+ ggsci::scale_color_lancet()
ggplot2分面
ggplot(longtest, aes(d = D, m = M, color = name)) + geom_roc() + style_roc()+ facet_wrap(~name)+ ggsci::scale_color_lancet()
主题与注释
AUC计算并绘制在图中-calc_auc函数
calc_auc(basicplot)$AUC提取
basicplot + style_roc(theme = theme_grey) + ##主题修改 theme(axis.text = element_text(colour = "blue")) + ggtitle("Themes and annotations") + ## 标题 annotate("text", x = .75, y = .25, ## 注释text的位置 label = paste("AUC =", round(calc_auc(basicplot)$AUC, 2))) + scale_x_continuous("1 - Specificity", breaks = seq(0, 1, by = .1)) ## x刻度 ## Scale for 'x' is already present. Adding another scale for 'x', whi
- 对multi_ROC注释,实现多个AUC值的呈现,
实际上仍然是ggplot2语法中的annotate注释
p geom_roc(n.cuts = 0) + style_roc()+ ggsci::scale_color_lancet() auchead(auc) ## PANEL group AUC ## 1 1 1 0.833985 ## 2 1 2 0.679599 p+annotate("text",x = .75, y = .25, ## 注释text的位置 label = paste("AUC of M1 =", round(calc_auc(p)$AUC[1], 2))) + annotate("text",x = .75, y = .15, ## 注释text的位置) label=paste("AUC of M2 =", round(calc_auc(p)$AUC[2], 2)))
其它计算ROC曲线的算法融入
默认的calculate_roc 计算的是 empirical ROC曲线
只要有cutoff, TPF,FPF即可计算,将这些结果以数据框的形式传入到 ggroc 函数
代替默认的统计方法为identity
require(plotROC) require(ggplot2) set.seed(2529) D.ex <- rbinom(200, size = 1, prob = .5)M1 <- rnorm(200, mean = D.ex, sd = .65)M2 <- rnorm(200, mean = D.ex, sd = 1.5)test <- data.frame(D = D.ex, D.str = c("Healthy", "Ill")[D.ex + 1], M1 = M1, M2 = M2, stringsAsFactors = FALSE)head(test) ## D D.str M1 M2 ## 1 1 Ill 1.48117155 -2.50636605 ## 2 1 Ill 0.61994478 1.46861033 ## 3 0 Healthy 0.57613345 0.07532573 ## 4 1 Ill 0.85433197 2.41997703 ## 5 0 Healthy 0.05258342 0.01863718 ## 6 1 Ill 0.66703989 0.24732453D.ex <- test$DM.ex <- test$M1mu1 <- mean(M.ex[D.ex == 1])mu0 <- mean(M.ex[D.ex == 0])s1 <- sd(M.ex[D.ex == 1])s0 <- sd(M.ex[D.ex == 0])c.ex <- seq(min(M.ex), max(M.ex), length.out = 300) ## 构造数据框传入数据binorm.roc <- data.frame(c = c.ex, FPF = pnorm((mu0 - c.ex)/s0), TPF = pnorm((mu1 - c.ex)/s1) )head(binorm.roc)binorm.plot <- ggplot(binorm.roc, aes(x = FPF, y = TPF, label = c)) + geom_roc(stat = "identity") + style_roc(theme = theme_grey)binorm.plot
时间依赖的ROC曲线
配合survival ROC包
配合lapply函数实现批量绘图
- lappy的结果返回为list,刚好输入do.call
require(ggplot2) require(plotROC) library(survivalROC) survT 350, 1/5) cens 350, 1, .1) M -8 * sqrt(survT) + rnorm(350, sd = survT) ### 时间2,5,10 sroc 2, 5, 10), function(t){ stroc status = cens, marker = M, predict.time = t, method = "NNE", ## KM法或NNE法 span = .25 * 350^(-.2)) data.frame(TPF = stroc[["TP"]], FPF = stroc[["FP"]], c = stroc[["cut.values"]], time = rep(stroc[["predict.time"]], length(stroc[["FP"]]))) }) ## 整合到数据框中 sroclong do.call(rbind, sroc) class(sroclong) ## [1] "data.frame" head(sroclong) ## TPF FPF c time ## 1 1 1.0000000 -Inf 2 ## 2 1 0.9970286 -96.21091 2 ## 3 1 0.9940573 -89.13315 2 ## 4 1 0.9910859 -80.53402 2 ## 5 1 0.9881145 -70.53104 2 ## 6 1 0.9851431 -67.81392 2 sroclong$timetime) ## 绘制ROC pROCtime)) + geom_roc(labels = FALSE, stat = "identity") + style_roc()+ ggsci::scale_color_jco() pROC
- 添加注释
pROC+annotate("text",x = .75, y = .25, ## 注释text的位置 label = paste("AUC of 1 years =", round(calc_auc(pROC)$AUC[1], 2))) + annotate("text",x = .75, y = .15, ## 注释text的位置) label=paste("AUC of 3 years =", round(calc_auc(pROC)$AUC[2], 2)))+ annotate("text",x = .75, y = .05, ## 注释text的位置) label=paste("AUC of 5 years =", round(calc_auc(pROC)$AUC[3], 2)))
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