pcens_quantile.default: Default method for computing primary event censored quantiles

Description

This method inverts the primary event censored CDF using numerical optimisation via optim. For each probability value, it searches for the delay such that the CDF computed by pcens_cdf() approximates the target probability.

Usage

# S3 method for default
pcens_quantile(
  object,
  p,
  pwindow,
  D = Inf,
  use_numeric = FALSE,
  init = 5,
  tol = 1e-08,
  max_iter = 10000,
  ...
)

Value

A numeric vector containing the computed primary event censored quantiles.

Arguments

object: A primarycensored object as created by new_pcens().
p: A vector of probabilities at which to compute the quantiles.
pwindow: Primary event window
D: Maximum delay (truncation point). If finite, the distribution is truncated at D. If set to Inf, no truncation is applied. Defaults to Inf.
use_numeric: Logical; if TRUE forces the use of numeric inversion even if an analytical solution is available (not yet implemented).
init: Initial guess for the delay. By default, 5.
tol: Numeric tolerance for the convergence criterion in the optimisation routine.
max_iter: Integer specifying the maximum number of iterations allowed during optimisation.
...: Additional arguments passed to underlying functions.

Details

The quantile is computed by minimising the squared difference between the computed CDF and the target probability.

Examples

Run this code

# Create a primarycensored object with gamma delay and uniform primary
pcens_obj <- new_pcens(
  pdist = pgamma,
  dprimary = dunif,
  dprimary_args = list(min = 0, max = 1),
  shape = 3,
  scale = 2
)

# Compute quantile for a single probability
pcens_quantile(pcens_obj, p = 0.8, pwindow = 1)

# Compute quantiles for multiple probabilities
pcens_quantile(pcens_obj, p = c(0.25, 0.5, 0.75), pwindow = 1)

# Compute quantiles for multiple probabilities with truncation
pcens_quantile(pcens_obj, p = c(0.25, 0.5, 0.75), pwindow = 1, D = 10)