ggplot: Plot for decision curve

In ggDCA: Calculate and Plot Decision Curve

Description

Plot for decision curve

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)

ggDCA documentation built on Sept. 6, 2020, 5:06 p.m.