A convenience function for confidence intervals with linear-ish parametric models
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, ... )
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 |
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 |
keep_reps |
Should the individual parameter estimates for each bootstrap sample be retained? |
... |
Options to pass to the model function (such as |
A tibble with columns "term", ".lower", ".estimate", ".upper",
".alpha", and ".method". If keep_reps = TRUE, an additional list column
called ".replicates" is also returned.
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
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)
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