denstrip (version 1.5.4)

densregion.survfit: Density regions for survival curves

Description

Adds a density region to a survival plot. The shading of the region has darkness proportional to the fiducial density of the point. This distribution is assumed to be normal with standard deviation calculated using the lower confidence limit stored in the survival curve object.

Usage

# S3 method for survfit
densregion(x, ny=20, …)

Arguments

x

Survival curve object, returned by survfit. Confidence intervals must have been calculated, using conf.type.

ny

Minimum number of points to calculate the density at for each event time. The default of 20 should be sufficient to obtain smooth-looking plots.

…

Further arguments passed to densregion.default.

Details

The density is calculated at a grid of points, and interpolated using the method referred to in densregion.

References

Jackson, C. H. (2008) Displaying uncertainty with shading. The American Statistician, 62(4):340-347.

See Also

densregion, densregion.normal, denstrip

Examples

# NOT RUN {
if (requireNamespace("survival", quietly=TRUE)){

library(survival)
fit <- survfit(Surv(time, status) ~ 1, data=aml, conf.type="log-log")
plot(fit, col=0)
densregion(fit)
lines(fit, lwd=3, conf.int=FALSE, lty=1)
lines(fit, lwd=3, conf.int=TRUE, lty=2)
# }
# NOT RUN {
## Wider CIs based on log survival
fit <- survfit(Surv(time, status) ~ 1, data=aml, conf.type="log")
plot(fit, col=0) 
densregion(fit) # Big variation in maximum density 
plot(fit, col=0) 
densregion(fit, pointwise=TRUE, colmax="maroon4")
par(new=TRUE)
plot(fit)

## Narrower CIs based on untransformed survival.
## Normal assumption probably unrealistic
fit <- survfit(Surv(time, status) ~ 1, data=aml, conf.type="plain")
plot(fit, col=0) 
densregion(fit, pointwise=TRUE, colmax="darkmagenta")
par(new=TRUE)
plot(fit)

## Multiple survival curves on same axes
## Should overlap smoothly on devices that allow transparency
fit2 <- survfit(Surv(time, status) ~ x, data=aml, conf.type="log-log")
fit2x1 <- survfit(Surv(time, status) ~ 1, data=aml,
                  conf.type="log-log", subset=(x=="Maintained"))
fit2x0 <- survfit(Surv(time, status) ~ 1, data=aml,
                  conf.type="log-log", subset=(x=="Nonmaintained"))
plot(fit2, lwd=3, xlab="Weeks", ylab="Survival", xlim=c(0, 60),
     lty=1:2, col=c("red", "blue"), conf.int=TRUE, mark.time=TRUE)
densregion(fit2x1, colmax="red", gamma=2)
densregion(fit2x0, colmax="blue", gamma=2)

}

# }