lgspline_weibull: Fit Weibull Accelerated Failure Time Model via lgspline

In lgspline: Lagrangian Multiplier Smoothing Splines for Smooth Function Estimation

Fit Weibull Accelerated Failure Time Model via lgspline

Description

Convenience wrapper that calls lgspline with the correct family, weight, dispersion, score, and unconstrained-fit functions for Weibull accelerated failure time regression. All standard lgspline arguments (knots, penalties, constraints, parallel, etc.) are passed through.

Usage

lgspline_weibull(formula, data, status, ...)

Arguments

formula	Formula specifying the model. The response should be survival time; status is passed separately.
data	Data frame.
status	Integer vector of event indicators (1 = event, 0 = censored), same length as the number of rows in data.
...	Additional arguments passed to lgspline.

Details

Internally sets:

• family = weibull_family()

• unconstrained_fit_fxn = unconstrained_fit_weibull

• glm_weight_function = weibull_glm_weight_function

• qp_score_function = weibull_qp_score_function

• dispersion_function = weibull_dispersion_function

• schur_correction_function = weibull_schur_correction

• need_dispersion_for_estimation = TRUE

• estimate_dispersion = TRUE

• standardize_response = FALSE

A formula interface is needed, e.g. lgspline_weibull(t, y, ...) won't work, unlike for ordinary lgspline.

Value

An object of class "lgspline".

See Also

lgspline_cox for Cox proportional hazards, weibull_family, unconstrained_fit_weibull

Examples

## Weibull AFT with a nonlinear age effect on lung cancer survival
if(requireNamespace("survival", quietly = TRUE)) {
  library(survival)
  set.seed(1234)
  lung <- na.omit(lung[, c("time", "status", "age")])
  lung$age_std <- std(lung$age)

  ## survival codes status as 1 = censored, 2 = dead
  event <- as.integer(lung$status == 2)

  ## Spline on age
  fit <- lgspline_weibull(
    time ~ spl(age_std),
    data = lung,
    status = event,
    K = 1,
    opt = FALSE,
    wiggle_penalty = 1e-4,
    flat_ridge_penalty = 1
  )
  print(summary(fit))
  plot(fit,
       show_formulas = TRUE,
       custom_response_lab = 'Survival Time',
       custom_predictor_lab = 'Standardized Age')
}

lgspline documentation built on Aug. 5, 2026, 1:10 a.m.