mt.rawp2adjp.RdThis function computes adjusted p-values for simple multiple testing procedures from a vector of raw (unadjusted) p-values. The procedures include the Bonferroni, Holm (1979), Hochberg (1988), and Sidak procedures for strong control of the family-wise Type I error rate (FWER), and the Benjamini & Hochberg (1995) and Benjamini & Yekutieli (2001) procedures for (strong) control of the false discovery rate (FDR). The less conservative adaptive Benjamini & Hochberg (2000) and two-stage Benjamini & Hochberg (2006) FDR-controlling procedures are also included. This function is taken from the multtest package. It is the only function used from this package and is added to this package wholesale to reduce user installation burden.
mt.rawp2adjp(rawp, proc=c("Bonferroni", "Holm", "Hochberg", "SidakSS",
"SidakSD", "BH", "BY","ABH","TSBH"), alpha = 0.05, na.rm = FALSE)A vector of raw (unadjusted) p-values for each hypothesis under consideration. These could be nominal p-values, for example, from t-tables, or permutation p-values as given in mt.maxT and mt.minP. If the mt.maxT or mt.minP functions are used, raw p-values should be given in the original data order, ordered by the index of that data.
A vector of character strings containing the names of the multiple testing procedures for which adjusted p-values are to be computed. This vector should include any of the following: "Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD", "BH", "BY", "ABH", "TSBH".
A nominal type I error rate, or a vector of error rates, used for estimating the number of true null hypotheses in the two-stage Benjamini & Hochberg procedure ("TSBH"). Default is 0.05.
An option for handling NA values in a list of raw p-values. If FALSE, the number of hypotheses considered is the length of the vector of raw p-values. Otherwise, if TRUE, the number of hypotheses is the number of raw p-values which were not NAs.
A list with components: adjp, index, h0.ABH, h0.TSBH. See multtest package on Bioconductor for details.