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.

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
# }