--- a
+++ b/Nomogram.R
@@ -0,0 +1,120 @@
+## Here are the simple examples for plotting nomogram, ROC curves, Calibration curves, and Decision curves 
+## in training and test dataset by using R language.
+
+# Library and data
+library(rms)
+library(pROC)
+library(rmda)
+train <-read.csv("E:/Experiments/YinjunDong/nomogram/EGFR-nomogram.csv")
+test <-read.csv("E:/Experiments/YinjunDong/nomogram/EGFR-nomogram-test.csv")
+
+# Nomogram
+dd=datadist(train)
+options(datadist="dd")
+f1 <- lrm(EGFR~ Rad
+          +Smoking
+          +Type
+          ,data = train,x = TRUE,y = TRUE)
+
+nom <- nomogram(f1, fun=plogis,fun.at=c(.001, .01, seq(.1,.9, by=.4)), lp=F, funlabel="Risk")
+plot(nom)
+
+# ROC train
+f2 <- glm(EGFR~ Rad
+          +Smoking
+          +Type
+          ,data = train,family = "binomial")
+
+pre <- predict(f2, type='response')
+plot.roc(train$EGFR, pre,
+         main="ROC Curve", percent=TRUE,
+         print.auc=TRUE,
+         ci=TRUE, ci.type="bars", 
+         of="thresholds",
+         thresholds="best",
+         print.thres="best",
+         col="blue"
+         #,identity=TRUE
+         ,legacy.axes=TRUE,
+         print.auc.x=ifelse(50,50),
+         print.auc.y=ifelse(50,50)
+         )
+
+# ROC test
+pre1 <- predict(f2,newdata = test)
+plot.roc(test$EGFR, pre1,
+         main="ROC Curve", percent=TRUE,
+         print.auc=TRUE,
+         ci=TRUE, ci.type="bars",
+         of="thresholds",
+         thresholds="best",
+         print.thres="best",
+         col="blue",legacy.axes=TRUE,
+         print.auc.x=ifelse(50,50),
+         print.auc.y=ifelse(50,50)
+         )
+
+# Calibration Curve train
+rocplot1 <- roc(train$EGFR, pre)
+ci.auc(rocplot1)
+cal <- calibrate(f1,  method = "boot", B = 1000)
+plot(cal, xlab = "Nomogram Predicted Survival", ylab = "Actual Survival",main = "Calibration Curve")
+
+# Calibration Curve test
+rocplot2 <- roc(test$EGFR,pre1)
+ci.auc(rocplot2)
+f3 <- lrm(test$EGFR ~ pre1,x = TRUE,y = TRUE)
+cal2 <- calibrate(f3,  method = "boot", B = 1000)
+plot(cal2, xlab = "Nomogram Predicted Survival", ylab = "Actual Survival",main = "Calibration Curve")
+
+# Decision Curve train
+Rad<- decision_curve(EGFR~ 
+                          Rad, data = train, family = binomial(link ='logit'),
+                          thresholds= seq(0,1, by = 0.01),
+                          confidence.intervals =0.95,study.design = 'case-control',
+                          population.prevalence = 0.3)
+
+Clinical<- decision_curve(EGFR~ 
+                         Smoking+Type, data = train, family = binomial(link ='logit'),
+                         thresholds= seq(0,1, by = 0.01),
+                         confidence.intervals =0.95,study.design = 'case-control',
+                         population.prevalence = 0.3)
+
+clinical_Rad<- decision_curve(EGFR~ Rad
+                         +Smoking+Type, data = train,
+                         family = binomial(link ='logit'), thresholds = seq(0,1, by = 0.01),
+                         confidence.intervals= 0.95,study.design = 'case-control',
+                         population.prevalence= 0.3)
+
+List<- list(Clinical,Rad,clinical_Rad)
+plot_decision_curve(List,curve.names= c('Clinical','Rad-Score','Nomogram'),
+                    cost.benefit.axis =FALSE,col = c('green','red','blue'),
+                    confidence.intervals =FALSE,standardize = FALSE,
+                    #legend.position = "none"
+                    legend.position = "bottomleft"
+                    )
+
+# Decision Curve test
+Rad1<- decision_curve(EGFR~ 
+                     Rad, data = test, family = binomial(link ='logit'),
+                     thresholds= seq(0,1, by = 0.01),
+                     confidence.intervals =0.95,study.design = 'case-control',
+                     population.prevalence = 0.3)
+
+Clinical1<- decision_curve(EGFR~ 
+                          Smoking+Type, data = test, family = binomial(link ='logit'),
+                          thresholds= seq(0,1, by = 0.01),
+                          confidence.intervals =0.95,study.design = 'case-control',
+                          population.prevalence = 0.3)
+
+clinical_Rad1<- decision_curve(EGFR~ Rad
+                              +Smoking+Type, data = test,
+                              family = binomial(link ='logit'), thresholds = seq(0,1, by = 0.01),
+                              confidence.intervals= 0.95,study.design = 'case-control',
+                              population.prevalence= 0.3)
+
+List1<- list(Clinical1, Rad1, clinical_Rad1)
+plot_decision_curve(List1,curve.names= c('Clinical','Rad-Score','Nomogram'),
+                    cost.benefit.axis =FALSE,col = c('green','red','blue'),
+                    confidence.intervals =FALSE,standardize = FALSE,
+                    legend.position = "bottomleft")