Plot survivor curves and fitted rates

Base graphics, drawn from the tables the estimators already return, so no plotting dependency is needed. Censored times are marked, a band is drawn where a standard error is available, and groups get a legend.

Usage

# S3 method for class 'tda_ple'
plot(
  x,
  conf.int = TRUE,
  mark.censored = TRUE,
  col = NULL,
  lty = 1,
  lwd = 1.5,
  xlab = "Time",
  ylab = "Survivor function",
  main = NULL,
  legend = TRUE,
  ylim = c(0, 1),
  ...
)

# S3 method for class 'tda_ltb'
plot(x, ...)

# S3 method for class 'tda_rate'
plot(
  x,
  what = c("survivor", "rate", "density"),
  xlab = "Time",
  ylab = NULL,
  col = "black",
  lwd = 1.5,
  main = NULL,
  ...
)

Arguments

x

an estimate from tda_km, tda_ple, tda_ltb, or a fit from tda_rate.

conf.int

draw a band at plus or minus 1.96 standard errors.

mark.censored

mark censoring times with a cross.

col, lty, lwd

colour, line type and width, recycled over groups.

xlab, ylab, main, ylim

as usual.

legend

draw a legend when there is more than one group.

...

passed to plot.

what

for a fitted rate model, which of the survivor, rate or density curves to draw. The fit must have been made with prate.

Value

The object, invisibly.

See also

Other plotting: tda_check_ps(), tda_pl(), tda_pl_arc(), tda_pl_axis(), tda_pl_graph(), tda_pl_hist, tda_pl_panel(), tda_pl_regression(), tda_pl_scatter(), tda_plot_ps(), tda_ps(), tda_ps3(), tda_read_ps()

Examples

set.seed(40)
d <- data.frame(t = round(rexp(60, 0.1), 1) + 0.5, s = rbinom(60, 1, 0.8))
km <- tda_km(Surv(t, s) ~ 1, d)
plot(km)