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Plot time-varying PTE measures and treatment effects from a "fitted_onlinesurr" object

Source: R/methods.R
plot.fitted_onlinesurr.Rd

Produces a ggplot2 figure showing, over time, either the Local PTE (LPTE), the Cumulative PTE (CPTE), or the marginal and residual treatment effects \(\Delta(t)\) and \(\Delta_R(t)\) (labeled \(\Delta\) and \(\Delta_R\) in the plot). Point estimates are taken from object$Marginal$point and object$Conditional$point, with uncertainty bands computed from the stored bootstrap draws.

Usage

# S3 method for class 'fitted_onlinesurr'
plot(x, type = "LPTE", conf.level = 0.95, one.sided = TRUE, ...)

Arguments

x

A "fitted_onlinesurr" object, typically returned by fit.surr. It must contain $T, $n.fixed, and the components $Marginal and $Conditional, each with point and smp.

type

Character string specifying what to plot. One of "LPTE", "CPTE", or "Delta" (case-insensitive). "Delta" plots both \(\Delta(t)\) and \(\Delta_R(t)\) with separate colors.

conf.level

Numeric in \((0,1)\) giving the confidence level for the plotted intervals. Default is 0.95.

one.sided

Logical; if TRUE (default), uses signif.level = (1-conf.level)/2 when taking quantiles, so each tail excludes 1-conf.level (i.e., a wider interval than the usual two-sided conf.level interval). This is convenient when visually assessing one-sided surrogate validation criteria. If FALSE, uses the standard two-sided construction signif.level = 1-conf.level.

...

Additional arguments (currently unused) included for S3 method compatibility.

Value

A ggplot object.

Details

The function extracts time-indexed treatment-effect estimates \(\Delta(t)\) (marginal) and \(\Delta_R(t)\) (residual/conditional) from the fitted object, along with bootstrap draws for each. It then constructs:

  • LPTE: \(\mathrm{LPTE}(t) = 1 - \Delta_R(t)/\Delta(t)\).

  • CPTE: \(\mathrm{CPTE}(t) = 1 - \sum_{u\le t}\Delta_R(u)/\sum_{u\le t}\Delta(u)\).

  • Delta: plots \(\Delta(t)\) and \(\Delta_R(t)\) directly.

Point estimates are plotted as points; intervals are empirical quantile intervals computed from the bootstrap sample matrices stored in object.

Examples

if (FALSE) { # \dontrun{
fit <- fit.surr(y ~ 1,
  id = id, surrogate = ~ s1 + s2, treat = trt,
  data = dat, time = time, N.boots = 2000
)

plot(fit, type = "LPTE")
plot(fit, type = "CPTE", conf.level = 0.90, one.sided = FALSE)
plot(fit, type = "Delta")
} # }