survfit.spbp: BP-based model survival curves

Description

Compute survival curves for a fitted spbp model.

Usage

# S3 method for spbp
survfit(
  formula,
  newdata = NULL,
  times = NULL,
  se.fit = TRUE,
  interval = 0.95,
  type = c("log", "log-log", "plain"),
  interval.type = c("hpd", "quantile"),
  monotone = NULL,
  tidy = FALSE,
  baseline = FALSE,
  ...
)
# S3 method for survfitbp
as.data.frame(x, row.names = NULL, optional = FALSE, ...)
# S3 method for survfitbp
plot(
  x,
  conf.int,
  col = 1,
  lty = 1,
  lwd = 1.5,
  xlab = "Time",
  ylab = "Survival probability",
  ylim = c(0, 1),
  mark.time = FALSE,
  ...
)

Value

An object of class "survfit" (with classes survfitbp, survfitcox, survfit).

A data.frame with columns id (curve index), time, surv,

lower, upper, cumhaz, std.err.

Arguments

formula: An object of class "spbp" returned by spbp.
newdata: Optional data frame used to obtain survival curves for specific covariate values.
times: Optional evaluation times: a Surv object (legacy), or a non-negative numeric vector (unique values are used; suitable for smooth ggplot2::geom_line plots via predict.spbp or as.data.frame.survfitbp).
se.fit: Logical; if TRUE, compute standard errors.
interval: Confidence level for intervals (e.g. 0.95).
type: Character; confidence interval transformation. One of "log", "log-log", or "plain".
interval.type: For Bayesian fits only: "hpd" (default) or "quantile" (equal-tailed).
monotone: Logical; for Bayesian fits, enforce non-increasing credible-band limits over time so ribbons plot smoothly with ggplot2::geom_line. Defaults to TRUE when approach = "bayes".
tidy: Logical; if TRUE, return a data.frame for ggplot2. instead of a "survfit" object.
baseline: Logical; if TRUE, return the baseline survival curve \(S_0(t)\) at observed event times (or times) with no covariate effect. When tidy = TRUE, returns a simple data.frame with columns time and surv.
...: Further arguments passed to survfit.coxph for the reference Cox object (e.g. conf.type is ignored; use type instead).
x: Object from survfit.spbp.
row.names, optional: Unused; included for S3 consistency.
conf.int: Logical; draw pointwise interval limits. Defaults to TRUE for a single curve when limits are present.
col, lty, lwd, xlab, ylab, ylim: Graphical parameters.
mark.time: Ignored (Bernstein curves are continuous).

Functions

as.data.frame(survfitbp): Tidy survival curves for ggplot2::geom_line / geom_ribbon.
plot(survfitbp): Plot Bernstein-polynomial survival as a smooth line (type = "l"), never a Kaplan--Meier step function. Pass conf.int = TRUE for pointwise bands (also drawn with lines).

Examples

Run this code

library(spsurv)
data(veteran, package = "survival")
fit <- bpph(Surv(time, status) ~ karno + factor(celltype), data = veteran)
survfit(fit)
plot(survfit(fit, times = seq(0, max(veteran$time), length.out = 80)))

data(veteran, package = "survival")
fit <- bpph(Surv(time, status) ~ karno, data = veteran, approach = "mle", init = 0)
sf <- survfit(fit, times = seq(0, max(veteran$time), length.out = 80))
head(as.data.frame(sf))