I would like to have the shading of the confidence intervals for the survival estimates. Now I have black lines.
library(survival)
library(ggplot2)
library(GGally)
data(lung)
sf.sex <- survfit(Surv(time, status) ~ sex, data = lung)
pl.sex <- ggsurv(sf.sex, CI = TRUE)
pl.sex
Here is an easy, almost-right solution:
pl.sex <- ggsurv(sf.sex, CI = FALSE) +
geom_ribbon(aes(ymin=low,ymax=up,fill=group),alpha=0.3)
Set CI = FALSE to get rid of the dashed-line CI bands then add geom_ribbon() to get the confidence bands you want.
This is only mostly right, though, since we want the confidence bands to use the step function as well. To get exactly what we want, we can use some code from this link to create a new stat for geom_ribbon called "stepribbon" as follows:
library(proto)
stairstepn <- function( data, direction="hv", yvars="y" ) {
direction <- match.arg( direction, c( "hv", "vh" ) )
data <- as.data.frame( data )[ order( data$x ), ]
n <- nrow( data )
if ( direction == "vh" ) {
xs <- rep( 1:n, each = 2 )[ -2 * n ]
ys <- c( 1, rep( 2:n, each = 2 ) )
} else {
ys <- rep( 1:n, each = 2 )[ -2 * n ]
xs <- c( 1, rep( 2:n, each = 2))
}
data.frame(
x = data$x[ xs ]
, data[ ys, yvars, drop=FALSE ]
, data[ xs, setdiff( names( data ), c( "x", yvars ) ), drop=FALSE ]
)
}
stat_stepribbon <- function( mapping=NULL, data=NULL, geom="ribbon", position="identity" ) {
StatStepribbon$new( mapping=mapping, data=data, geom=geom, position=position )
}
StatStepribbon <- proto(ggplot2:::Stat, {
objname <- "stepribbon"
desc <- "Stepwise area plot"
desc_outputs <- list(
x = "stepped independent variable",
ymin = "stepped minimum dependent variable",
ymax = "stepped maximum dependent variable"
)
required_aes <- c( "x", "ymin", "ymax" )
default_geom <- function(.) GeomRibbon
default_aes <- function(.) aes( x=..x.., ymin = ..y.., ymax=Inf )
calculate <- function( ., data, scales, direction = "hv", yvars = c( "ymin", "ymax" ), ...) {
stairstepn( data = data, direction = direction, yvars = yvars )
}
examples <- function(.) {
DF <- data.frame( x = 1:3, ymin = runif( 3 ), ymax=rep( Inf, 3 ) )
ggplot( DF, aes( x=x, ymin=ymin, ymax=ymax ) ) + stat_stepribbon()
}
})
With that new stat you can get the solution I think you were really looking for:
pl.sex <- ggsurv(sf.sex, CI = FALSE) +
geom_ribbon(aes(ymin=low,ymax=up,fill=group),stat="stepribbon",alpha=0.3) +
guides(fill=guide_legend("sex"))
I found a better solution. You just use the Rcmdrplugin KMggplot2 which also has the optional features of adding the median line and number at risk table.
