plotROC- Generate ROC Curve Charts for Print and Interactive Use pROC- display and analyze ROC curves in R and S+ plotROC plotROC包较为简单与单一,它就是用来绘制ROC曲线的,包中定义的函数基于ggplot2,因此我们可以结合ggplot2使用和修改、美化图形结果。 代码语言:javascript 复制 #从GitHub上安装devtools...
plotROC- Generate ROC Curve Charts for Print and Interactive Use pROC- display and analyze ROC curves in R and S+ plotROC plotROC包较为简单与单一,它就是用来绘制ROC曲线的,包中定义的函数基于ggplot2,因此我们可以结合ggplot2使用和修改、美化图形结果。 #从GitHub上安装devtools::install_github('hadley...
plotROC - Generate ROC Curve Charts for Print and Interactive Use pROC - display and analyze ROC curves in R and S+ plotROC plotROC包较为简单与单一,它就是用来绘制ROC曲线的,包中定义的函数基于ggplot2,因此我们可以结合ggplot2使用和修改、美化图形结果。 #从GitHub上安装 devtools::install_github('...
17sens.ci <- ci.se(pROC_obj) 18plot(sens.ci, type="shape", col="lightblue") 19## Warning in plot.ci.se(sens.ci, type = "shape", col = "lightblue"): Low 20## definition shape. 21plot(sens.ci, type="bars") PRROC - 2014 虽然不像ROCR和pROC那么受欢迎,但PRROC最近似乎又卷土...
- PROC INST MECH ENG B-J ENG MA 被引量: 10发表: 1997年 Process monitoring ROC curve for evaluating dynamic screening methods Monitoring of things like total cholesterol, smoking status and blood pressure are recorded continuously to know in advance the possibility of cardiovascul... P Qiu,L ...
When using PROC LOGISTIC to fit the model, specify the OUTROC= option in the MODEL statement and the OUT= and P= options in the OUTPUT statement before using the ROCPLOT macro to plot the ROC curve for the training data. Before using the ROCPLOT macro to plot the ROC curve for a sco...
使用pROC包的roc()和auc()函数,计算AUC。不难看出,四种模型的AUC都非常高,接近于1,区分度接近于完美。 4.3 校准度 使用rms包的calibration()函数 和ggplot2包绘制LR模型和RF模型的校准曲线。能够看到,与对角线相比,两个模型的校准曲线存在一些偏差,LR模型的校准曲线部分位于对角线上方(低估风险),部分位于下方(高...
A few people have reported this error: > plot(precis(m5.3)) Error in as.double(y) : cannot coerce type 'S4' to vector of type 'double' It appears to be related to precis methods not exporting right. But I have been unable to replicate th...
需要的R包:DESeq2包(转录组差异分析),survival包(cox回归),survminer包(Kaplan-Meier Plot可视化),dplyr包(字符串处理),glmnet包(Lasso回归),ggplot2包(数据可视化),GGally包(绘制相关性矩阵图),rms包(计算VIF方差膨胀因子),survivalROC包(绘制time dependent ROC曲线),plotROC包(绘制ROC曲线)。
常见的TCGA数据挖掘办法之一,是通过差异基因分析获得差异表达基因,然后从中筛选出部分表达水平与患者生存相关联的候选基因,对它们的表达水平进行多因素cox回归构建风险模型,评估风险模型的预测能力(ROC曲线),并用Kaplan-Meier生存分析评估模型的风险评分是否有意义。