AD2gof {Dogoftest} | R Documentation |
Two-Sample Anderson-Darling Test (Bootstrap Version)
Description
Performs a two-sample Anderson-Darling (AD) goodness-of-fit test using bootstrap resampling to compare whether two samples come from the same distribution. This test is sensitive to differences in both location and shape between the two distributions.
Usage
AD2gof(
x,
y,
alternative = c("two.sided", "less", "greater"),
nboots = 2000,
keep.boots = FALSE
)
Arguments
x |
A numeric vector of data values from the first sample. |
y |
A numeric vector of data values from the second sample. |
alternative |
Character string specifying the alternative hypothesis. One of '"two.sided"' (default), '"less"', or '"greater"'. |
nboots |
Integer. Number of bootstrap replicates to compute the null distribution (default: 2000). |
keep.boots |
Logical. If 'TRUE', returns the full vector of bootstrap statistics (default: 'FALSE'). |
Details
The test computes the Anderson-Darling statistic using the pooled empirical distribution functions (ECDFs) of the two samples. A bootstrap procedure resamples the group labels to approximate the null distribution and compute a p-value. If 'p.value = 0', it is adjusted to '1 / (2 * nboots)' for stability.
Value
A list of class '"htest"' containing:
- statistic
The observed Anderson-Darling test statistic.
- p.value
The estimated bootstrap p-value.
- alternative
The alternative hypothesis used.
- method
A character string describing the test.
- bootstraps
(Optional) A numeric vector of bootstrap statistics if 'keep.boots = TRUE'.
Examples
set.seed(123)
x <- rnorm(100, mean = 0, sd = 4)
y <- rnorm(100, mean = 2, sd = 4)
AD2gof(x, y)