weibull_scale {lgspline} | R Documentation |
Estimate Scale for Weibull Accelerated Failure Time Model
Description
Computes maximum log-likelihood scale estimate of Weibull accelerated failure time (AFT) survival model.
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_scale(log_y, log_mu, status, weights = 1)
Arguments
log_y |
Logarithm of response/survival times |
log_mu |
Logarithm of predicted survival times |
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. |
weights |
Optional observation weights (default = 1) |
Details
Calculates maximum log-likelihood estimate of scale for Weibull AFT model accounting for right-censored observations using Brent's method for optimization.
Value
Scalar representing the estimated scale
Examples
## Simulate exponential data with censoring
set.seed(1234)
mu <- 2 # mean of exponential distribution
n <- 500
y <- rexp(n, rate = 1/mu)
## Introduce censoring (25% of observations)
status <- rbinom(n, 1, 0.75)
y_obs <- ifelse(status, y, NA)
## Compute scale estimate
scale_est <- weibull_scale(
log_y = log(y_obs[!is.na(y_obs)]),
log_mu = log(mu),
status = status[!is.na(y_obs)]
)
print(scale_est)