R/reg_intervals.R
reg_intervals.Rd
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
#> # 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>> #> 1 I(1/sqrt(disp)) 207. 249. 290. 0.05 student-t [1,001 × 2]# }