Bootstrap resampling for hazard model coefficients

Resample data with replacement, refit the hazard model on each replicate, and accumulate coefficient distributions. Returns a tidy data frame of per-replicate estimates with summary statistics. This is the R equivalent of the SAS bootstrap.hazard.sas macro.

Usage

hzr_bootstrap(
  object,
  n_boot = 200L,
  fraction = 1,
  seed = NULL,
  verbose = FALSE
)

# S3 method for class 'hzr_bootstrap'
print(x, digits = 4, ...)

Arguments

object

A fitted hazard object (with fit = TRUE).

n_boot

Integer: number of bootstrap replicates (default 200).

fraction

Numeric in (0, 1]: fraction of data to sample per replicate (default 1.0 for full bootstrap; < 1 for bagging).

seed

Optional integer random seed for reproducibility. When supplied, set.seed(seed) is called at function entry, jumping the global RNG to the seeded state; it is not restored on exit. Pass NULL (the default) to skip the set.seed() call and start from the caller's current RNG state. Note that the bootstrap consumes random numbers either way, so the global RNG state will advance during the call – seed = NULL avoids the reset at entry, not the advance during resampling.

verbose

Logical; if TRUE, print progress every 50 replicates.

x

An hzr_bootstrap object.

digits

Number of decimal places for formatting.

...

Additional arguments (ignored).

Value

A list with class "hzr_bootstrap" containing:

replicates

Data frame with columns replicate, parameter, and estimate – one row per parameter per successful replicate.

summary

Data frame with columns parameter, n, pct, mean, sd, min, max, ci_lower, ci_upper – one row per parameter.

n_success

Number of successfully converged replicates.

n_failed

Number of replicates that failed to converge.

See also

hazard() for model fitting, vcov.hazard() for Hessian-based standard errors.

Examples

# \donttest{
data(avc)
avc <- na.omit(avc)
fit <- hazard(
  survival::Surv(int_dead, dead) ~ age + mal,
  data  = avc,
  dist  = "weibull",
  theta = c(mu = 0.01, nu = 0.5, 0, 0),
  fit   = TRUE
)
bs <- hzr_bootstrap(fit, n_boot = 50, seed = 123)
print(bs)
#> Bootstrap inference for hazard model
#> Replicates: 50 successful, 0 failed
#> 
#>  parameter  n pct    mean     sd     min     max ci_lower ci_upper
#>         mu 50 100  0.0004 0.0004  0.0000  0.0023   0.0000   0.0013
#>         nu 50 100  0.2219 0.0144  0.1963  0.2618   0.1986   0.2462
#>        age 50 100 -0.0062 0.0028 -0.0129 -0.0022  -0.0125  -0.0029
#>        mal 50 100  0.8464 0.2701  0.2673  1.3909   0.4337   1.3471
# }