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 = "kaplan-meier", 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 display the data |
position |
Position adjustment, either as a string, or the result of a call to a position adjustment function. |
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 "kaplan-meier" which is equivalent to stype=1, ctype=1, "fleming-harrington" 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 Kaplan-Meier survival estimate for
right-censored 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 |
Kaplan-Meier 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 = "fleming-harrington") p1 + stat_km(start.time = 5)
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