A convenience function for confidence intervals with linear-ish parametric models
Source:R/reg_intervals.R
reg_intervals.Rd
A convenience function for confidence intervals with linear-ish parametric models
Usage
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
forstats::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
Examples
# \donttest{
set.seed(1)
reg_intervals(mpg ~ I(1 / sqrt(disp)), data = mtcars)
#> # A tibble: 1 × 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
set.seed(1)
reg_intervals(mpg ~ I(1 / sqrt(disp)), data = mtcars, keep_reps = TRUE)
#> # A tibble: 1 × 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]
# }