multtest: Resampling-based multiple hypothesis testing. Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Aoshima, Makoto; Yata, Kazuyoshi: Asymptotic normality for inference on multisample, high-dimensional mean vectors under mild conditions (2015)
- Yata, Kazuyoshi; Aoshima, Makoto: Inference on high-dimensional mean vectors with fewer observations than the dimension (2012)