This function combines several independent Anderson-Darling k-sample tests into one overall test of the hypothesis that the independent samples within each block come from a common unspecified distribution, while the common distributions may vary from block to block. Both versions of the Anderson-Darling test statistic are provided.
This function uses the Kruskal-Wallis criterion to test the hypothesis of no association between the counts for two responses "A" and "B" across t categories and across M blocks.
This function uses pairwise Wilcoxon tests, comparing a common control sample with each of several treatment samples, in a multiple comparison fashion. The experiment wise significance probabity is calculated, estimated, or approximated, when testing the hypothesis that all independent samples arise from a common unspecified distribution, or that treatments have no effect when assigned randomly to the given subjects.
This function uses the QN criterion (Kruskal-Wallis, van der Waerden scores, normal scores) to test the hypothesis that k independent samples arise from a common unspecified distribution.
This function plots upper tail probabilities of the limiting distribution against the corresponding exact or simulated probabilities, both on a log-scale.
This function uses the Kruskal-Wallis criterion to test the hypothesis of no association between the counts for two responses "A" and "B" across t categories.
This function inverts pairwise Wilcoxon tests, comparing a common control sample with each of several treatment samples to provide simultaneous confidence bounds for the respective shift parameters by which the sampled treatment populations may differ from the control population. It is assumed that all samples are independent and that the sampled distributions are continuous to avoid ties. The joint coverage probability for all bounds/intervals is calculated, estimated, or approximated, see Details. For treatment of ties also see Details.
The k-sample Anderson-Darling, Kruskal-Wallis, normal score and van der Waerden score tests are used to test the hypothesis that k samples of sizes n_1, …, n_k come from a common continuous distribution F that is otherwise unspecified. They are rank tests. Average rank scores are used in case of ties. While ad.test is consistent against all alternatives, qn.test tends to be sensitive mainly to shifts between samples. The combined versions of these tests, ad.test.combined and qn.test.combined, are used to simultaneously test such hypotheses across several blocks of samples. The hypothesized common distributions and the number k of samples for each block of samples may vary from block to block.
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