Bootstrap standard errors and confidence intervals

Performs bootstrap resampling to estimate the sampling distribution of parameter estimates.

Usage

bootstrap_fit(loglik_maker, data, params, n_boot = 500, progress = TRUE, ...)

Arguments

loglik_maker: A function that takes data and returns a log-likelihood function suitable for fit(). See examples.
data: The data (vector, matrix, or data frame).
params: Initial parameter values for fit().
n_boot: Number of bootstrap replications (default 500).
progress: Logical; show progress messages (default TRUE).
...: Additional arguments passed to fit().

Value

A bootstrap_result object containing:

estimates: Matrix of bootstrap estimates (n_boot x n_params)
original: Original parameter estimates
se: Bootstrap standard errors
bias: Estimated bias

Details

The loglik_maker function should accept data and return a log-likelihood function that can be passed to fit(). This design allows bootstrap to work with any model specification.

Examples

if (FALSE) { # \dontrun{
set.seed(42)
x <- rnorm(50, mean = 5, sd = 2)

# Define a function that creates the loglik function from data
make_loglik <- function(data) {
  function(mu, log_sigma) {
    loglik_normal(mu, exp(log_sigma), data)
  }
}

# Bootstrap
boot <- bootstrap_fit(
  make_loglik,
  data = x,
  params = c(mu = 0, log_sigma = 0),
  n_boot = 200
)

# Results
boot$se          # Bootstrap SEs
confint(boot)    # Bootstrap CIs

# Compare with Wald SEs
result <- fit(make_loglik(x), params = c(mu = 0, log_sigma = 0))
se(result)       # Wald SEs
} # }