Summarise bootstrap coefficient stability

Summarise the coefficient distributions returned by bootstrap_coefficients() on the GLM link scale or after exponentiation.

Usage

# S3 method for class 'bootstrap_coefficients'
summary(
  object,
  scale = c("link", "exponentiated", "relativity"),
  confidence = 0.95,
  interval = c("percentile", "normal"),
  ...
)

Arguments

object

A bootstrap_coefficients object.

scale

Character string. "link" reports coefficients on their fitted GLM scale. "exponentiated" applies exp() to every original and bootstrap coefficient. "relativity" is an alias for "exponentiated"; this interpretation is most direct for a log-link GLM. For a logit-link model, exponentiated coefficients are odds ratios rather than response probabilities.

confidence

Numeric scalar between 0 and 1 giving the confidence level.

interval

Character string. "percentile" uses empirical bootstrap quantiles. "normal" uses the original estimate plus or minus a normal quantile times the bootstrap standard error.

...

Additional arguments are not used.

Value

A data frame with one row per original coefficient and columns:

term

Coefficient name.

estimate

Estimate from the original GLM.

bootstrap_mean

Mean of the finite bootstrap estimates.

bias

Bootstrap mean minus the original estimate.

bootstrap_se

Standard deviation of the bootstrap estimates.

lower, upper

Requested bootstrap interval.

n_successful

Number of finite bootstrap estimates for the term.

n_requested

Number of requested bootstrap samples.

success_rate

n_successful / n_requested.

See also

bootstrap_coefficients(), as_gt()

Author

Martin Haringa