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This article mainly introduces "how to use pROC package to draw ROC curve" related knowledge, editor through the actual case to show you the operation process, the method of operation is simple and fast, practical, I hope that this "how to use pROC package to draw ROC curve" article can help you solve the problem.

R package pROC can be used for ROC curve analysis, and the main functions involved are roc function and plot.roc function.

Take your own data as an example:

> library (pROC) > data ("aSAH") > head (aSAH) 10) gos6 outcome gender age wfns s100b ndka29 5 Good Female 42 1 0.13 3.0130 5 Good Female 37 1 0.14 8.5431 5 Good Female 42 1 0.10 8.0932 5 Good Female 27 10.04 10.4233 1 Poor Female 423 0.13 17.4034 1 Poor Male 48 2 0.10 12.7535 4 Good Male 57 5 0.47 6.0036 1 Poor Male 41 40 . 16 13.2037 5 Good Female 49 1 0.18 15.5438 4 Good Female 75 2 0.10 6.01

According to the data display, select s100b to analyze the outcome

> outcome=roc (response=aSAH$outcome,predictor = aSAH$s100b) > outcomeCall:roc.default (response=aSAH$outcome,predictor = aSAH$s100b) Data: aSAH$s100b in 72 controls (aSAH$outcome Good)

Plot.roc (outcome,col= "red", print.auc = TRUE,print.auc.col = "darkgreen", auc.polygon = TRUE,auc.polygon.col = "pink") on "how to use the pROC package to draw ROC curve" content here, thank you for reading. If you want to know more about the industry, you can follow the industry information channel. The editor will update different knowledge points for you every day.

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