ControlFDR.Rd
This is a modification of the mt.rawp2adjp
function from
the Bioconductor package multtest
. We fixed an error wherein
selecting the "TSBH"
option overwrote the results of any previous
adjustment methods, and another error created when the "BY"
and
"TSBH"
methods were called simultaneously. We did not write the
original function. For more information, see
https://www.bioconductor.org/packages/3.7/bioc/manuals/multtest/man/multtest.pdf.
ControlFDR( rawp, proc = c("BH", "BY", "ABH", "TSBH", "Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD"), alpha = 0.05, na.rm = FALSE, as.multtest.out = FALSE )
rawp  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. 

proc  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 options listed in the
"Details" Section. Adjusted \(p\)values are computed for simple FWER
and FDR controlling procedures based on a vector of raw (unadjusted)
\(p\)values. Defaults to 
alpha  A nominal TypeI error rate, or a vector of error rates, used
for estimating the number of true null hypotheses in the twostage
Benjamini & Hochberg procedure ( 
na.rm  An option for handling 
as.multtest.out  Should the output match the output from the

A vector of the same length and order as rawp
, unless the user
specifies that the output should match the output from the multtest
package. In that case, the use should specify as.multtest.out = TRUE
and this function will return output identical to that of the
mt.rawp2adjp
function from package multtest
. That output is
as follows:
adjp
: A matrix of adjusted \(p\)values, with rows
corresponding to hypotheses and columns to multiple testing
procedures. Hypotheses are sorted in increasing order of their raw
(unadjusted) \(p\)values.
index
: A vector of row indices, between 1 and
length(rawp)
, where rows are sorted according to their raw
(unadjusted) \(p\)values. To obtain the adjusted \(p\)values
in the original data order, use adjp\[order(index),\]
.
h0.ABH
: The estimate of the number of true null
hypotheses (as proposed by Benjamini & Hochberg (2000)) used when
computing adjusted \(p\)values for the "ABH"
procedure (see
Dudoit et al., 2007).
h0.TSBH
: The estimate (or vector of estimates) of the
number of true null hypotheses (as proposed by Benjamini et al.
(2006)) when computing adjusted \(p\)values for the "TSBH"
procedure (see Dudoit et al., 2007).
This 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 familywise TypeI 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 twostage Benjamini & Hochberg (2006) FDRcontrolling procedures are also included.
The proc
options are
"BH"
: Adjusted \(p\)values for the Benjamini &
Hochberg (1995) stepup FDRcontrolling procedure (independent and
positive regression dependent test statistics).
"BY"
: Adjusted \(p\)values for the Benjamini &
Yekutieli (2001) stepup FDRcontrolling procedure (general
dependency structures).
"ABH"
: Adjusted \(p\)values for the adaptive
Benjamini & Hochberg (2000) stepup FDRcontrolling procedure. This
method amends the original stepup procedure using an estimate of the
number of true null hypotheses obtained from \(p\)values. This
method is not guaranteed to return finite values.
"TSBH"
: Adjusted \(p\)values for the twostage
Benjamini & Hochberg (2006) stepup FDRcontrolling procedure. This
method amends the original stepup procedure using an estimate of the
number of true null hypotheses obtained from a firstpass application
of "BH"
. The adjusted \(p\)values are \(\alpha\)
dependent, therefore \(\alpha\) must be set in the function
arguments when using this procedure.
"Bonferroni"
: Bonferroni singlestep adjusted \(p\)
values for strong control of the FWER.
"Holm"
: Holm (1979) stepdown adjusted \(p\)values
for strong control of the FWER.
"Hochberg"
: Hochberg (1988) stepup adjusted \(p\)
values for strong control of the FWER (for raw (unadjusted) \(p\)
values satisfying the Simes inequality).
"SidakSS"
: Sidak singlestep adjusted \(p\)values for
strong control of the FWER (for positive orthant dependent test
statistics).
"SidakSD"
: Sidak stepdown adjusted \(p\)values for
strong control of the FWER (for positive orthant dependent test
statistics).
# DO NOT CALL THIS FUNCTION DIRECTLY. # Call this function through AESPCA_pVals() or SuperPCA_pVals() instead. if (FALSE) { ### Load the Example Data ### data("colonSurv_df") data("colon_pathwayCollection") ### Create an OmicsSurv Object ### colon_Omics < CreateOmics( assayData_df = colonSurv_df[, (2:3)], pathwayCollection_ls = colon_pathwayCollection, response = colonSurv_df[, 1:3], respType = "surv" ) ### Extract Pathway PCs and Loadings ### colonPCs_ls < ExtractAESPCs( object = colon_Omics, parallel = TRUE, numCores = 2 ) ### Pathway pValues ### pVals < PermTestSurv( OmicsSurv = colon_Omics, pathwayPCs_ls = colonPCs_ls$PCs, parallel = TRUE, numCores = 2 ) ### Adjust pValues ### ControlFDR(rawp = pVals) }