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The Bioconductor R package multtest implements widely applicable resampling-based single-step and stepwise multiple testing procedures (MTP) for con-trolling a broad class of Type I error rates, in testing problems involving general data generating distributions (with arbitrary dependence structures among variables), null hypotheses, and test st...
The results of a multiple testing procedure are summarized using rejection regions for the test statistics, confidence re-gions for the parameters of interest, and adjusted p-values.
2 Οκτ 2019 · We will now explore multiple hypothesis testing, or what happens when multiple tests are conducted on the same family of data. We will set things up as before, with the false positive rate \(\alpha = 0.05\) and false negative rate \(\beta=0.20\) .
Multiple Test Procedures Description. Given a set of p-values and the level of significance, returns decisions whether the corresponding hypotheses should be rejected or not, including the hybrid Hochberg-Hommel procedure (Gou et al., 2014) and Quick procedure (Gou and Zhang, 2022). Usage mtp(p, alpha = 0.05, method = "gtxr", n = length(p ...
• Correcting for multiple testing in R •Methods for addressing multiple testing (FWER and FDR) •Define the multiple testing problem and related concepts
The mt.teststat and mt.teststat.num.denum functions provide a convenient way to compute test statistics for each row of a data frame, e.g., two-sample Welch t-statistics, Wilcoxon statistics, F-statistics, paired t-statistics, and block F-statistics.
Multiple testing procedure. A multiple testing procedure (MTP) pro-vides rejection regions, C n(m), i.e., sets of values for each test statistic T n(m) that lead to the decision to reject the null hypothesis H0(m). In other words, a MTP produces a random (i.e., data-dependent) subset R n of rejected hypotheses that estimates the set of true ...
