reg_intervals function

A convenience function for confidence intervals with linear-ish parametric models

reg_intervals(
  formula,
  data,
  model_fn = "lm",
  type = "student-t",
  times = NULL,
  alpha = 0.05,
  filter = term != "(Intercept)",
  keep_reps = FALSE,
  ...
)

Arguments

• formula: An R model formula with one outcome and at least one predictor.
• data: A data frame.
• model_fn: The model to fit. Allowable values are "lm", "glm", "survreg", and "coxph". The latter two require that the survival package be installed.
• type: The type of bootstrap confidence interval. Values of "student-t" and "percentile" are allowed.
• times: A single integer for the number of bootstrap samples. If left NULL, 1,001 are used for t-intervals and 2,001 for percentile intervals.
• alpha: Level of significance.
• filter: A logical expression used to remove rows from the final result, or NULL to keep all rows.
• keep_reps: Should the individual parameter estimates for each bootstrap sample be retained?
• ...: Options to pass to the model function (such as family for glm()).

Returns

A tibble with columns "term", ".lower", ".estimate", ".upper", ".alpha", and ".method". If keep_reps = TRUE, an additional list column called ".replicates" is also returned.

Examples

set.seed(1)
reg_intervals(mpg ~ I(1 / sqrt(disp)), data = mtcars)

set.seed(1)
reg_intervals(mpg ~ I(1 / sqrt(disp)), data = mtcars, keep_reps = TRUE)

References

Davison, A., & Hinkley, D. (1997). Bootstrap Methods and their Application. Cambridge: Cambridge University Press. doi:10.1017/CBO9780511802843

Bootstrap Confidence Intervals, https://rsample.tidymodels.org/articles/Applications/Intervals.html

See Also

int_pctl(), int_t()