Anderson-Darling k-Sample Test
This function uses the Anderson-Darling criterion to test the hypothesis that k independent samples with sample sizes n_1,…, n_k arose from a common unspecified distribution function F(x) and testing is done conditionally given the observed tie pattern. Thus this is a permutation test. Both versions of the AD statistic are computed.
ad.test(..., data = NULL, method = c("asymptotic", "simulated", "exact"),
dist = FALSE, Nsim = 10000)... |
Either several sample vectors, say x_1, …, x_k, with x_i containing n_i sample values. n_i > 4 is recommended for reasonable asymptotic P-value calculation. The pooled sample size is denoted by N=n_1+…+n_k, or a list of such sample vectors, or a formula y ~ g, where y contains the pooled sample values and g is a factor (of same length as y) with levels identifying the samples to which the elements of y belong. |
data |
= an optional data frame providing the variables in formula y ~ g. |
method |
=
N!/(n_1!… n_k!) of full enumerations. Otherwise, |
dist |
|
Nsim |
|
If AD is the Anderson-Darling criterion for the k samples, its standardized test statistic is T.AD = (AD - μ)/σ, with μ = k-1 and σ representing mean and standard deviation of AD. This statistic is used to test the hypothesis that the samples all come from the same but unspecified continuous distribution function F(x).
According to the reference article, two versions of the AD test statistic are provided. The above mean and standard deviation are strictly valid only for version 1 in the continuous distribution case.
NA values are removed and the user is alerted with the total NA count. It is up to the user to judge whether the removal of NA's is appropriate.
The continuity assumption can be dispensed with, if we deal with
independent random samples, or if randomization was used in allocating
subjects to samples or treatments, and if we view
the simulated or exact P-values conditionally, given the tie pattern
in the pooled samples. Of course, under such randomization any conclusions
are valid only with respect to the group of subjects that were randomly allocated
to their respective samples.
The asymptotic P-value calculation assumes distribution continuity. No adjustment
for lack thereof is known at this point. For details on the asymptotic
P-value calculation see ad.pval.
A list of class kSamples with components
test.name |
|
k |
number of samples being compared |
ns |
vector of the k sample sizes (n_1,…,n_k) |
N |
size of the pooled sample = n_1+…+n_k |
n.ties |
number of ties in the pooled samples |
sig |
standard deviations σ of version 1 of AD under the continuity assumption |
ad |
2 x 3 (2 x 4) matrix containing AD, T.AD, asymptotic P-value, (simulated or exact P-value), for each version of the standardized test statistic T, version 1 in row 1, version 2 in row 2. |
warning |
logical indicator, warning = TRUE when at least one n.i < 5 |
null.dist1 |
simulated or enumerated null distribution of version 1 of the test statistic, given as vector of all generated AD statistics. |
null.dist2 |
simulated or enumerated null distribution of version 2 of the test statistic, given as vector of all generated AD statistics. |
method |
The |
Nsim |
The number of simulations. |
method = "exact" should only be used with caution.
Computation time is proportional to the number of enumerations. In most cases
dist = TRUE should not be used, i.e.,
when the returned distribution vectors null.dist1 and null.dist2
become too large for the R work space. These vectors are limited in length by 1e8.
For small sample sizes and small k exact null distribution calculations are possible (with or without ties), based on a recursively extended version of Algorithm C (Chase's sequence) in Knuth (2011), Ch. 7.2.1.3, which allows the enumeration of all possible splits of the pooled data into samples of sizes of n_1, …, n_k, as appropriate under treatment randomization. The enumeration and simulation are both done in C.
It has recently come to our attention that the Anderson-Darling test, originally proposed by Pettitt (1976) in the 2-sample case and generalized to k samples by Scholz and Stephens, has a close relative created by Baumgartner et al (1998) in the 2 sample case and populatized by Neuhaeuser (2012) with at least 6 papers among his cited references and generalized by Murakami (2006) to k samples.
Baumgartner, W., Weiss, P. and Schindler, H. (1998), A nonparametric test for the general two-sample problem, Bionetrics, 54, 1129-1135.
Knuth, D.E. (2011), The Art of Computer Programming, Volume 4A Combinatorial Algorithms Part 1, Addison-Wesley
Neuhaeuser, M. (2012), Nonparametric Statistical Tests, A Computational Approach, CRC Press.
Murakami, H. (2006), A k-sample rank test based on modified Baumgartner statistic and it power comparison, Jpn. Soc. Comp. Statist., 19, 1-13.
Murakami, H. (2012), Modified Baumgartner statistic for the two-sample and multisample problems: a numerical comparison. J. of Statistical Comput. and Simul., 82:5, 711-728.
Pettitt, A.N. (1976), A two-sample Anderson_Darling rank statistic, Biometrika, 63, 161-168.
Scholz, F. W. and Stephens, M. A. (1987), K-sample Anderson-Darling Tests, Journal of the American Statistical Association, Vol 82, No. 399, 918–924.
u1 <- c(1.0066, -0.9587, 0.3462, -0.2653, -1.3872) u2 <- c(0.1005, 0.2252, 0.4810, 0.6992, 1.9289) u3 <- c(-0.7019, -0.4083, -0.9936, -0.5439, -0.3921) y <- c(u1, u2, u3) g <- as.factor(c(rep(1, 5), rep(2, 5), rep(3, 5))) set.seed(2627) ad.test(u1, u2, u3, method = "exact", dist = FALSE, Nsim = 1000) # or with same seed # ad.test(list(u1, u2, u3), method = "exact", dist = FALSE, Nsim = 1000) # or with same seed # ad.test(y ~ g, method = "exact", dist = FALSE, Nsim = 1000)
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