## 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")