Confidence intervals for a TMB discounting model

Wald (Hessian-based) confidence intervals: estimate +/- z * se on the internal (log-k) scale, then back-transformed to report_space.

Usage

# S3 method for class 'beezdiscounting_tmb'
confint(
  object,
  parm = NULL,
  level = 0.95,
  report_space = c("internal", "natural"),
  ...
)

Arguments

object

A beezdiscounting_tmb object.

parm

Optional character vector for filtering. Accepts display names ("k:(Intercept)", and "s" for the GM/Rachlin shape parameter) or raw optimizer names ("beta_k", "log_sigma_u", "log_phi", "log_sigma_e", "log_s", and for a 2-RE fit "log_sd_re" / "cor_re"). NULL returns all coefficients.

level

Confidence level (default 0.95).

report_space

"internal" (default; all coefficients on their estimation/log scale) or "natural" (exponentiate beta_k rows so the intercept is k at the reference level and non-intercept terms are multiplicative fold-changes; variance/aux params stay on their internal scale).

...

Unused.

Value

A tibble with columns term, estimate, conf.low, conf.high, level.

Note

For a 2-RE fit the cor_re and log_sd_re rows are reported on their internal (atanh / log) scales and are NOT back-transformed by report_space = "natural" (only beta_k/log_s rows are); use VarCorr() to obtain the correlation and natural-log SDs.

Examples

# \donttest{
dd <- simulate_dd_ip(n_subjects = 20, seed = 1)
fit <- fit_dd_tmb(dd, equation = "mazur", family = "sltb",
                  random_effects = k ~ 1, verbose = 0)
confint(fit)
#> # A tibble: 3 × 5
#>   term          estimate conf.low conf.high level
#>   <chr>            <dbl>    <dbl>     <dbl> <dbl>
#> 1 k:(Intercept)   -4.53     -4.80    -4.27   0.95
#> 2 log_sigma_u     -0.632    -1.03    -0.235  0.95
#> 3 log_phi          2.52      2.29     2.76   0.95
confint(fit, parm = "k:(Intercept)", report_space = "natural")
#> # A tibble: 1 × 5
#>   term          estimate conf.low conf.high level
#>   <chr>            <dbl>    <dbl>     <dbl> <dbl>
#> 1 k:(Intercept)   0.0108  0.00826    0.0140  0.95
# }