geom_stepconfint.Rd
Produces a step function confidence interval for survival curves. This function is taken from
the utile.visuals
package by Eric Finnesgard. That package is not used because of its
strong dependencies.
geom_stepconfint(
mapping = NULL,
data = NULL,
stat = "identity",
position = "identity",
na.rm = FALSE,
...
)
Aesthetic mappings with aes() function. Like geom_ribbon(), you must provide columns for x, ymin (lower limit), ymax (upper limit).
The data to be displayed in this layer. Can inherit from ggplot parent.
The statistical transformation to use on the data for this layer, as a string. Defaults to 'identity'.
Position adjustment, either as a string, or the result of a call to a position adjustment function.
If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.
Optional. Any other ggplot geom_ribbon() arguments.
Originally adapted from the survminer package <https://github.com/kassambara/survminer>.
require(survival)
require(ggplot2)
#> Loading required package: ggplot2
f <- survfit(Surv(time, status) ~ trt, data = diabetic)
d <- with(f, data.frame(time, surv, lower, upper, trt=rep(names(f$strata), f$strata)))
ggplot(d, aes(x = time, y=surv)) +
geom_step(aes(color = trt)) +
geom_stepconfint(aes(ymin = lower, ymax = upper, fill = trt), alpha = 0.3) +
coord_cartesian(c(0, 50)) +
scale_x_continuous(expand = c(0.02,0)) +
labs(x = 'Time', y = 'Freedom From Event') +
scale_color_manual(
values = c('#d83641', '#1A45A7'),
name = 'Treatment',
labels = c('None', 'Laser'),
aesthetics = c('colour', 'fill'))