survMisc (version 0.4.2)

autoplot: Generate a ggplot for survfit object

Description

Uses ggplot2 to plot survival curves (Kaplan-Meier plot)

Usage

## S3 method for class 'survfit':
autoplot (object, ..., alpha = 0.5,
    shape = 3, xlab = "Time", ylab = "Survival",
    title = "Marks show times with censoring",
    legendLabs = NULL, CI = FALSE, bands = FALSE,
    pval = FALSE, plotTable = FALSE, divideTime = 1,
    returnTable = FALSE)

Arguments

object

An object of class survfit

...

Additional arguments

alpha

Transparency to use for confidence intervals or bands

shape

Shape of marks to indicate censored onservations. Default is 3 which gives vertical ticks. Use 10 for circular marks.

xlab

Label for x axis

ylab

Label for y axis

title

Title for graph

legendLabs

Legend labels. These can be used to replace the names of the strata from the fit. Should be given in the same order as those strata.

CI

Include confidence intervals (plotted as lines). These are taken from the survfit object.

bands

Include confidence bands (plotted as ribbons i.e. filled and joined by diagonal lines rather than steps)

pval

Add $p$ value from log-rank test to lower left of plot

plotTable

Add table below plot. This contains one row for each time increment for each strata, giving the number at risk at that time.

divideTime

Divide time by this number to give time increments. A larger number may be preferable with large data sets to avoid crowding.

returnTable

Return data.frame used for table.

Value

• A ggplot and optionally a data.frame as above.

Examples

data(kidney, package="KMsurv")
s1 <- survfit(Surv(time=time, event=delta) ~ type, data=kidney)
autoplot(s1)
autoplot(s1, CI=TRUE, pval=TRUE, plotTable=TRUE, divideTime=5,
 legendLabs=c("surgical", "percutaneous"),
 title="Time to infection following catheter placement \n
   by type of catheter, for dialysis patients")
s1 <- survfit(Surv(time=time, event=delta) ~ 1, data=kidney)
autoplot(s1)
data(rectum.dat, package="km.ci")
s1 <- survfit(Surv(time, status) ~ 1, data=rectum.dat)
### change confidence intervals to log Equal-Precision confidence bands
km.ci::km.ci(s1, method="logep")
autoplot(s1, bands=TRUE)