Compute confidence interval

Only simulation-based methods are (currently only) supported. get_confidence_interval() and get_ci() are both aliases of conf_int().

conf_int(x, level = 0.95, type = "percentile", point_estimate = NULL)

get_ci(x, level = 0.95, type = "percentile", point_estimate = NULL)

get_confidence_interval(x, level = 0.95, type = "percentile",
  point_estimate = NULL)

Arguments

x	Data frame of calculated statistics or containing attributes of theoretical distribution values. Currently, dependent on statistics being stored in stat column as created in calculate() function.
level	A numerical value between 0 and 1 giving the confidence level. Default value is 0.95.
type	A string giving which method should be used for creating the confidence interval. The default is "percentile" with "se" corresponding to (multiplier * standard error) as the other option.
point_estimate	A numeric value or a 1x1 data frame set to NULL by default. Needed to be provided if type = "se".

Value

A 1 x 2 tibble with values corresponding to lower and upper values in the confidence interval.

Examples

mtcars_df <- mtcars %>%
  dplyr::mutate(am = factor(am))
d_hat <- mtcars_df %>%
  specify(mpg ~ am) %>%
  calculate(stat = "diff in means", order = c("1", "0"))
bootstrap_distn <- mtcars_df %>%
  specify(mpg ~ am) %>%
  generate(reps = 100) %>%
  calculate(stat = "diff in means", order = c("1", "0"))
bootstrap_distn %>% conf_int(level = 0.9)
#> # A tibble: 1 x 2
#>    `5%` `95%`
#>   <dbl> <dbl>
#> 1  4.27  10.1
bootstrap_distn %>% conf_int(type = "se", point_estimate = d_hat)
#> # A tibble: 1 x 2
#>   lower upper
#>   <dbl> <dbl>
#> 1  3.56  10.9