stat_km  R Documentation 
Adds a Kaplan Meier Estimate of Survival
stat_km(
mapping = NULL,
data = NULL,
geom = "km",
position = "identity",
show.legend = NA,
inherit.aes = TRUE,
trans = scales::identity_trans(),
firstx = 0,
firsty = 1,
type = "kaplanmeier",
start.time = 0,
...
)
mapping 
Set of aesthetic mappings created by 
data 
The data to be displayed in this layer. There are three options: If A A 
geom 
The geometric object to use to display the data, either as a

position 
Position adjustment, either as a string naming the adjustment
(e.g. 
show.legend 
logical. Should this layer be included in the legends?

inherit.aes 
If 
trans 
Transformation to apply to the survival probabilities. Defaults to "identity". Other options include "event", "cumhaz", "cloglog", or define your own using trans_new. 
firstx, firsty 
the starting point for the survival curves. By default, the plot program obeys tradition by having the plot start at (0,1). 
type 
an older argument that combined stype and ctype, now deprecated. Legal values were "kaplanmeier" which is equivalent to stype=1, ctype=1, "flemingharrington" which is equivalent to stype=2, ctype=1, and "fh2" which is equivalent to stype=2, ctype=2. 
start.time 
numeric value specifying a time to start calculating survival information. The resulting curve is the survival conditional on surviving to start.time. 
... 
Other arguments passed to survfit.formula 
This stat is for computing the confidence intervals for the KaplanMeier survival estimate for
rightcensored data. It requires the aesthetic mapping x
for the
observation times and status
which indicates the event status,
0=alive, 1=dead or 1/2 (2=death). Logical status is not supported.
a data.frame with additional columns:
x 
x in data 
y 
KaplanMeier Survival Estimate at x 
stat_km
understands the following aesthetics (required aesthetics
are in bold):
time
The survival times
status
The censoring indicator, see Surv for more information.
alpha
color
linetype
size
library(ggplot2)
sex < rbinom(250, 1, .5)
df < data.frame(time = exp(rnorm(250, mean = sex)), status = rbinom(250, 1, .75), sex = sex)
ggplot(df, aes(time = time, status = status, color = factor(sex))) +
stat_km()
## Examples illustrating the options passed to survfit.formula
p1 < ggplot(df, aes(time = time, status = status))
p1 + stat_km()
p1 + stat_km(trans = "cumhaz")
# for cloglog plots also log transform the time axis
p1 + stat_km(trans = "cloglog") + scale_x_log10()
p1 + stat_km(type = "flemingharrington")
p1 + stat_km(start.time = 5)
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