ci (version 0.0.1)

ci_boot: Confidence Intervals via Bootstrap

Description

Calculates confidence intervals (CI) using bootstrap methods. This enhanced version of DescTools::BootCI() returns a data frame.

Usage

ci_boot(.data, x, y = NULL, conf.level = 0.95, ...)

Value

A data frame with confidence intervals. Columns depend on arguments and grouping:

• (if grouped) grouping variable names;

• Column matching the statistic name (from FUN) containing the estimate;

• lwr.ci, upr.ci -- lower and upper CI bounds.

Arguments

.data

Data frame.

x, y

Column names (unquoted).

conf.level

Confidence level. Default: 0.95.

...

Additional parameters for DescTools::BootCI(), including:

1. FUN -- function for which CI is calculated;

2. bci.method -- interval method:

   • "perc" -- percentile method,

   • "bca" -- bias-corrected and accelerated (BCa) method (see note below),

   • others;

3. R -- number of replications, typically 1,000 to 10,000.

Details

Similar to DescTools::BootCI(), but:

• First argument is a data frame;

• Arguments x and y are unquoted column names;

• Responds to dplyr::group_by() for subgroup calculations;

• Returns a data frame for convenient plotting with ggplot2.

Examples

# Bootstrap is useful when:
# - Data is skewed (not normal)
# - You want CI for statistics other than the mean (e.g., median, SD)
# - You don't want to assume a specific distribution

data(iris, package = "datasets")
head(iris)

set.seed(123) # For reproducible results

# Example 1: CI for the median (resistant to outliers)
iris |>
  ci_boot(Petal.Length, FUN = median, R = 1000, bci.method = "perc")
# Compare to mean CI - median is often more robust

# Example 2: CI for the median by group
iris |>
  dplyr::group_by(Species) |>
  ci_boot(Petal.Length, FUN = median, R = 1000, bci.method = "perc")
# Useful when groups have different distributions

# Example 3: CI for standard deviation
# How variable is petal length?
set.seed(456)
iris |>
  ci_boot(Petal.Length, FUN = sd, R = 1000, bci.method = "perc")

# Example 4: CI for interquartile range (IQR)
# IQR = 75th percentile - 25th percentile
set.seed(789)
iris |>
  ci_boot(Petal.Length, FUN = IQR, R = 1000, bci.method = "perc")

# Example 5: CI for correlation coefficient (Pearson's r)
# How related are petal length and width?
set.seed(101)
iris |>
  dplyr::group_by(Species) |>
  ci_boot(
    Petal.Length, Petal.Width,
    FUN = cor, method = "pearson",
    R = 1000, bci.method = "perc"
  )
# Look for CIs that don't include 0 (suggests real correlation)

# Example 6: Comparing BCa and percentile methods
set.seed(111)
# BCa method (often more accurate but requires more assumptions)
iris |> ci_boot(Petal.Length, FUN = median, R = 1000, bci.method = "bca")

# Percentile method (simpler, more robust)
iris |> ci_boot(Petal.Length, FUN = median, R = 1000, bci.method = "perc")

# Example 7: Effect of number of bootstrap replications
set.seed(222)
# Fewer replications (faster but less stable)
iris |> ci_boot(Petal.Length, FUN = median, R = 500, bci.method = "perc")

# More replications (slower but more stable)
iris |> ci_boot(Petal.Length, FUN = median, R = 5000, bci.method = "perc")
# For teaching: 1000 is usually enough; for research: 5000-10000

# Example 8: Handling missing values
set.seed(333)
iris |>
  ci_boot(
    Petal.Length,
    FUN = median, na.rm = TRUE,
    R = 1000, bci.method = "bca"
  )

# Example 9: With mtcars dataset
set.seed(444)
data(mtcars, package = "datasets")
mtcars |>
  dplyr::group_by(cyl) |>
  ci_boot(mpg, FUN = median, R = 1000, bci.method = "perc")
# Compare median MPG for different cylinder counts

# Example 10: Spearman correlation (rank-based, robust to outliers)
set.seed(555)
iris |>
  dplyr::group_by(Species) |>
  ci_boot(
    Petal.Length, Petal.Width,
    FUN = cor, method = "spearman",
    R = 1000, bci.method = "perc"
  )