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()` ). |

## Value

A tibble with columns "term", ".lower", ".estimate", ".upper",
".alpha", and ".method". If `keep_reps = TRUE`

, an additional list column
called ".replicates" is also returned.

## 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

## Examples

#> # A tibble: 1 x 6
#> term .lower .estimate .upper .alpha .method
#> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 I(1/sqrt(disp)) 207. 249. 290. 0.05 student-t

#> # A tibble: 1 x 7
#> term .lower .estimate .upper .alpha .method .replicates
#> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <list<tibble[,2]>>
#> 1 I(1/sqrt(disp)) 207. 249. 290. 0.05 student-t [1,001 × 2]

# }