ad_stat {twosamples}R Documentation

Anderson-Darling Test

Description

Anderson-Darling Test

Usage

ad_stat(a, b, power = 2)

ad_test(a, b, nboots = 2000, p = default.p)

Arguments

a

a vector of numbers

b

a vector of numbers

power

power to raise test stat to

nboots

Number of bootstrap iterations

p

power to raise test stat to

Details

The AD test compares two ECDFs by looking at the weighted sum of the squared differences between them – evaluated at each point in the joint sample. The weights are determined by the variance of the joint ECDF at that point. Formally – if E is the ECDF of sample 1, F is the ECDF of sample 2, and G is the ECDF of the joint sample then CVM = SUM_(x in k) (E(x)-F(x))^2/(G(x)*(1-G(x))) where k is the joint sample. The test p-value is calculated by randomly resampling two samples of the same size using the combined sample. Intuitively the AD test improves on the CVM test by giving lower weight to noisy observations.

Value

Output is a length 2 Vector with test stat and p-value in that order. That vector has 3 attributes – the sample sizes of each sample, and the number of bootstraps performed for the pvalue.

Functions

Examples

vec1 = rnorm(20)
vec2 = rnorm(20,4)
ad_test(vec1,vec2)

[Package twosamples version 1.0.0 Index]