R: Plot for decision curve

ggplot.rFP.p100

R Documentation

Plot for decision curve

Description

Plot for decision curve

Plot Decision Curve

Usage

## S3 method for class 'rFP.p100'
ggplot(
  data,
  mapping,
  color = TRUE,
  linetype = TRUE,
  lwd = 1.05,
  ...,
  environment = parent.frame()
)

## S3 method for class 'dca.cph'
ggplot(
  data,
  mapping,
  color = TRUE,
  linetype = TRUE,
  lwd = 1.05,
  ...,
  environment = parent.frame()
)

## S3 method for class 'dca.lrm'
ggplot(
  data,
  mapping,
  color = TRUE,
  linetype = TRUE,
  lwd = 1.05,
  ...,
  environment = parent.frame()
)

Arguments

`data`	results of dca() function
`mapping`	ignore
`color`	logical, whether models will be classified by color
`linetype`	logical, whether models will be classified by line type
`lwd`	line width
`...`	ignore
`environment`	ignore

Value

a ggplot2 picture

Examples


library(ggDCA)
library(rms)

######## logistic regression

model1 <- lrm(status~ANLN,LIRI)
d <- dca(model1,model.names = 'ANLN')
ggplot(d)



model2 <- lrm(status~ANLN+CENPA,LIRI)
d <- dca(model2,model.names = 'ANLN+CENPA')
ggplot(d)


model3 <- lrm(status~ANLN+CENPA+GPR182,LIRI)
d <- dca(model3,model.names = 'ANLN+CENPA+GPR182')
ggplot(d)

model4 <- lrm(status~ANLN+CENPA+GPR182+BCO2,LIRI)
d <- dca(model4,model.names = 'ANLN+CENPA+GPR182+BCO2')
ggplot(d)


d <- dca(model1,model2,model3,model4,
         model.names = c('ANLN',
                         'ANLN+CENPA',
                         'ANLN+CENPA+GPR182',
                         'ANLN+CENPA+GPR182+BCO2'))
ggplot(d,
       linetype = FALSE,
       color = c('blue','green','black','red','gray','gray'))


##########  cox regression

# evaluate at median time

model1 <- coxph(Surv(time,status)~ANLN,LIRI)
d <- dca(model1,model.names = 'ANLN')
ggplot(d)

model2 <- coxph(Surv(time,status)~ANLN+CENPA,LIRI)
d <- dca(model2,model.names = 'ANLN+CENPA')
ggplot(d)


model3 <- coxph(Surv(time,status)~ANLN+CENPA+GPR182,LIRI)
d <- dca(model3,model.names = 'ANLN+CENPA+GPR182')
ggplot(d)

model4 <- coxph(Surv(time,status)~ANLN+CENPA+GPR182+BCO2,LIRI)
d <- dca(model4,model.names = 'ANLN+CENPA+GPR182+BCO2')
ggplot(d)


d <- dca(model1,model2,model3,model4,
         model.names = c('ANLN',
                         'ANLN+CENPA',
                         'ANLN+CENPA+GPR182',
                         'ANLN+CENPA+GPR182+BCO2'))
ggplot(d,
       linetype = FALSE,
       color = c('blue','green','black','red','gray','gray'))



# evaluate at different times

qt <- quantile(LIRI$time,c(0.25,0.5,0.75))
qt=round(qt,2)
model1 <- coxph(Surv(time,status)~ANLN,LIRI)
d <- dca(model1,
         model.names = 'ANLN',
         times = qt)
ggplot(d)

model2 <- coxph(Surv(time,status)~ANLN+CENPA,LIRI)
d <- dca(model2,
         model.names = 'ANLN+CENPA',
         times = qt)
ggplot(d)


model3 <- coxph(Surv(time,status)~ANLN+CENPA+GPR182,LIRI)
d <- dca(model3,
         model.names = 'ANLN+CENPA+GPR182',
         times = qt)
ggplot(d)

model4 <- coxph(Surv(time,status)~ANLN+CENPA+GPR182+BCO2,LIRI)
d <- dca(model4,
         model.names = 'ANLN+CENPA+GPR182+BCO2',
         times = qt)
ggplot(d)


d <- dca(model1,model2,model3,model4,
         model.names = c('ANLN',
                         'ANLN+CENPA',
                         'ANLN+CENPA+GPR182',
                         'ANLN+CENPA+GPR182+BCO2'),
         times = qt)
ggplot(d)