adjustedPlot2.survreg: Draw predicted survival curve as a ggplot with an object...

In autoReg: Automatic Linear and Logistic Regression and Survival Analysis

Draw predicted survival curve as a ggplot with an object survreg

Description

Draw predicted survival curve as a ggplot with an object survreg

Usage

adjustedPlot2.survreg(
  x,
  xnames = NULL,
  pred.values = list(),
  maxy.lev = 5,
  newdata = NULL,
  addCox = FALSE,
  autovar = TRUE,
  legend.position = NULL,
  facet = NULL
)

Arguments

x	An object of class survreg
xnames	Character Names of explanatory variable to plot
pred.values	A list A list of predictor values
maxy.lev	Integer Maximum unique length of a numeric variable to be treated as categorical variables
newdata	A data.frame or NULL
addCox	logical Whether or not add KM
autovar	logical
legend.position	Character Default value is "topright"
facet	Character name(s) of facet variable(s)

Value

A ggplot

Examples

library(survival)
x=survreg(Surv(time, status) ~ rx, data=anderson,dist="exponential")
adjustedPlot(x,type="plot")
adjustedPlot(x)
adjustedPlot(x,addCox=TRUE)
## Not run: 
x=survreg(Surv(time, status) ~ sex, data=lung,dist="weibull")
adjustedPlot(x,addCox=TRUE)
x=survreg(Surv(time, status) ~ rx, data=anderson,dist="exponential")
adjustedPlot(x,addCox=TRUE)
x=survreg(Surv(time, status) ~ ph.ecog + age + sex, data=lung, dist="weibull")
pred.values=list(ph.ecog=0:3,sex=1:2,age=c(20,40,60,80))
adjustedPlot(x)
adjustedPlot(x,addCox=TRUE)
adjustedPlot(x,addCox=TRUE,xnames=c("ph.ecog","sex"),facet="sex")
adjustedPlot(x,pred.values=pred.values,addCox=TRUE,legend.position="top")+xlim(c(1,1000))
adjustedPlot(x,pred.values=pred.values,xnames=c("ph.ecog","sex","age"),facet=c("ph.ecog","sex"))
adjustedPlot(x,pred.values=pred.values,xnames=c("ph.ecog","sex","age"),facet=c("age","sex"))
adjustedPlot(x,pred.values=pred.values,addCox=TRUE)
adjustedPlot(x,addCox=TRUE)
adjustedPlot(x,pred.values=list(age=c(20,40,60,80),sex=1,ph.ecog=3),addCox=TRUE)

## End(Not run)

autoReg documentation built on Nov. 14, 2023, 9:07 a.m.