Parametric bootstrap confidence intervals for brs models

Computes bootstrap-based confidence intervals for the parameters of a fitted "brs" model by repeatedly simulating data from the fitted model and re-estimating parameters. Only "brs" (fixed or variable-dispersion) objects are supported; "brsmm" is not supported.

Usage

brs_bootstrap(
  object,
  R = 199L,
  level = 0.95,
  ci_type = c("percentile", "basic", "normal", "bca"),
  max_tries = NULL,
  keep_draws = FALSE
)

# S3 method for class 'brs_bootstrap'
print(x, ...)

Arguments

object: A fitted "brs" object (fixed or variable dispersion).
R: Integer: number of bootstrap replicates (default 199).
level: Numeric: confidence level (default 0.95).
ci_type: Character: type of confidence interval. One of "percentile" (default), "basic", "normal", or "bca".
max_tries: Optional integer: maximum number of bootstrap attempts to obtain converged replicates. If NULL, uses max(3 * R, 50).
keep_draws: Logical: if TRUE, stores successful bootstrap parameter draws in attribute "boot_draws".
x: Object returned by brs_bootstrap.
...: Ignored.

Value

A data frame with columns parameter, estimate (original point estimate), se_boot (bootstrap standard error), ci_lower, ci_upper, mcse_lower, mcse_upper, wald_lower, wald_upper, and level. The attribute "n_success" gives the number of replicates that converged. Additional attributes include "R", "n_attempted", "ci_type", and optionally "boot_draws".

Details

For each replicate, data are simulated via brs_sim using the estimated coefficients (on the link scale) and the original design. The model is then re-fitted with brs. Replicates that fail to converge are discarded; if the number of successful replicates is too low, a warning is issued. Intervals are the empirical quantiles of the bootstrap distribution of each parameter.

Methods (by generic)

print(brs_bootstrap): Print method for bootstrap results

Examples

# \donttest{
dat <- data.frame(
  y = c(
    0, 5, 20, 50, 75, 90, 100, 30, 60, 45,
    10, 40, 55, 70, 85, 25, 35, 65, 80, 15
  ),
  x1 = rep(c(1, 2), 10),
  x2 = rep(c(0, 0, 1, 1), 5)
)
prep <- brs_prep(dat, ncuts = 100)
#> brs_prep: n = 20 | exact = 0, left = 1, right = 1, interval = 18
fit <- brs(y ~ x1, data = prep)
boot <- brs_bootstrap(fit, R = 50, level = 0.95)
print(boot)
#> Bootstrap confidence intervals
#>   Level: 0.95 | CI: percentile | Successful replicates: 50 / 50 | Attempts: 50 
#> 
#>     parameter   estimate   se_boot  ci_lower  ci_upper mcse_lower mcse_upper
#> 1 (Intercept)  0.2550945 0.7907320 -1.133843 1.9053881  0.3600124 0.16466097
#> 2          x1 -0.2202075 0.4952859 -1.166506 0.5223341  0.0896141 0.07822265
#> 3       (phi) -0.3929198 0.3436550 -1.258389 0.1050310  0.1539971 0.13098474
#>   wald_lower wald_upper level
#> 1 -1.4390802  1.9492691  0.95
#> 2 -1.2809288  0.8405139  0.95
#> 3 -0.9343818  0.1485422  0.95
# }