Kuiper2gof {Dogoftest} | R Documentation |
Two-Sample Kuiper Test with Bootstrap
Description
Performs a two-sample Kuiper test using bootstrap resampling to test whether two independent samples come from the same distribution.
Usage
Kuiper2gof(
x,
y,
alternative = c("two.sided", "less", "greater"),
nboots = 2000,
keep.boots = FALSE
)
Arguments
x , y |
Numeric vectors of data values for the two samples. |
alternative |
Character string indicating the alternative hypothesis. Must be one of |
nboots |
Integer. Number of bootstrap resamples to compute the empirical null distribution (default: 2000). |
keep.boots |
Logical. If |
Details
The Kuiper test is a nonparametric test similar to the Kolmogorov–Smirnov test, but sensitive to discrepancies in both location and shape between two distributions. This implementation uses bootstrap resampling to estimate the p-value.
The two.sided
test uses the sum of maximum positive and negative ECDF differences.
The greater
and less
options use one-sided variations.
If the observed test statistic exceeds all bootstrap values, the p-value is set to 1 / (2 * nboots)
to avoid zero.
Value
An object of class "htest"
containing:
- statistic
The observed Kuiper statistic.
- p.value
The p-value computed from the bootstrap distribution.
- alternative
The specified alternative hypothesis.
- method
A character string describing the test.
- bootstraps
(If requested) A numeric vector of bootstrap statistics.
Examples
set.seed(123)
x <- rnorm(100, 0, 4)
y <- rnorm(100, 2, 4)
Kuiper2gof(x, y)