Summarise the coefficient distributions returned by
bootstrap_coefficients() on the GLM link scale or after exponentiation.
# S3 method for bootstrap_coefficients
summary(
object,
scale = c("link", "exponentiated", "relativity"),
confidence = 0.95,
interval = c("percentile", "normal"),
...
)A data frame with one row per original coefficient and columns:
Coefficient name.
Estimate from the original GLM.
Mean of the finite bootstrap estimates.
Bootstrap mean minus the original estimate.
Standard deviation of the bootstrap estimates.
Requested bootstrap interval.
Number of finite bootstrap estimates for the term.
Number of requested bootstrap samples.
n_successful / n_requested.
A bootstrap_coefficients object.
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.
Numeric scalar between 0 and 1 giving the confidence level.
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.
Martin Haringa
bootstrap_coefficients(), as_gt()