plot.survcorr: Plot Correlated Bivariate Survival Times

In SurvCorr: Correlation of Bivariate Survival Times

Plot Correlated Bivariate Survival Times

Description

Produces a scatterplot of bivariate survival times, either on the original times scale or as copula (uniform marginal distributions). Censored observations are inserted either by their imputed values (copula plot) or marked by arrows (survival times plot). The first time variable will be plotted on the y-axis, the second on the x-axis.

Usage

## S3 method for class 'survcorr'
plot(x, what = "uniform", imputation = 1, 
  xlab = switch(what, copula= expression(hat(F)(t[2])),
    uniform = expression(hat(F)(t[2])), 
  times = expression(t[2])), 
  ylab = switch(what, copula = expression(hat(F)(t[1])), 
    uniform = expression(hat(F)(t[1])), 
  times = expression(t[1])), xlim, ylim, 
  main = switch(what, copula = "Bivariate Copula",uniform = "Bivariate Copula", 
    times = "Bivariate Survival Times"), 
  legend = TRUE, cex.legend = switch(what, copula = 0.8, uniform = 0.8, times = 0.7), 
  pch = "*", colEvent = "black", colImput = "gray", ...)

Arguments

x	an object of class survcorr
what	what should be plotted: "uniform" or "copula" to plot the bivariate copula, "times" to plot the survival times. The default is to plot the copula.
imputation	If the copula is plotted, then the index of the imputated data set to be used to replace censored observation can be given (e.g., imputation=1:5. Default: imputation=1)
xlab	An optional x-axis label.
ylab	An optional y-axis label.
xlim	Optional limits for x-axis.
ylim	Optional limits for y-axis.
main	Optional title.
legend	Optional legend.
cex.legend	Optional font size of legend.
pch	Optional plot character.
colEvent	Color of symbols representing uncensored times (default="black").
colImput	Color of symbols representing imputations for censored times (default="gray").
...	Further options to be passed to the plot function.

Value

no return value; function is called for its side effects

Author(s)

Meinhard Ploner, Alexandra Kaider, Georg Heinze

References

Schemper,M., Kaider,A., Wakounig,S. & Heinze,G. (2013): "Estimating the correlation of bivariate failure times under censoring", Statistics in Medicine, 32, 4781-4790 doi: 10.1002/sim.5874.

Examples

## Example 2
data(diabetes)
obj <- survcorr(formula1=Surv(TIME1, STATUS1) ~ 1, formula2=Surv(TIME2, STATUS2) ~ 1, 
  data=diabetes, M=100, MCMCSteps=10, alpha=0.05, epsilon=0.001)
plot(obj, "times")
plot(obj, "copula", imputation=1)
plot(obj, "copula", imputation=7)

SurvCorr documentation built on Nov. 10, 2022, 5:43 p.m.