weibull_dispersion_function {lgspline} | R Documentation |
Estimate Weibull Dispersion for Accelerated Failure Time Model
Description
Computes the scale parameter for a Weibull accelerated failure time (AFT) model, supporting right-censored survival data.
This both provides a tool for actually fitting Weibull AFT Models, and boilerplate code for users who wish to incorporate Lagrangian multiplier smoothing splines into their own custom models.
Usage
weibull_dispersion_function(
mu,
y,
order_indices,
family,
observation_weights,
status
)
Arguments
mu |
Predicted survival times |
y |
Observed response/survival times |
order_indices |
Indices to align status with response |
family |
Weibull AFT model family specification |
observation_weights |
Optional observation weights |
status |
Censoring indicator (1 = event, 0 = censored) Indicates whether an event of interest occurred (1) or the observation was right-censored (0). In survival analysis, right-censoring occurs when the full survival time is unknown, typically because the study ended or the subject was lost to follow-up before the event of interest occurred. |
Value
Squared scale estimate for the Weibull AFT model (dispersion)
See Also
weibull_scale
for the underlying scale estimation function
Examples
## Simulate survival data with covariates
set.seed(1234)
n <- 1000
t1 <- rnorm(n)
t2 <- rbinom(n, 1, 0.5)
## Generate survival times with Weibull-like structure
lambda <- exp(0.5 * t1 + 0.3 * t2)
yraw <- rexp(n, rate = 1/lambda)
## Introduce right-censoring
status <- rbinom(n, 1, 0.75)
y <- ifelse(status, yraw, runif(1, 0, yraw))
## Example of using dispersion function
mu <- mean(y)
order_indices <- seq_along(y)
weights <- rep(1, n)
## Estimate dispersion
dispersion_est <- weibull_dispersion_function(
mu = mu,
y = y,
order_indices = order_indices,
family = weibull_family(),
observation_weights = weights,
status = status
)
print(dispersion_est)