Description Usage Arguments Details Value See Also Examples
View source: R/discreteBH_fun.R
Apply the [HSU], [HSD], [AHSU] and [AHSD] procedures, with or without computing the critical constants, to a set of p-values and their discrete support.
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raw.pvalues |
vector of the raw observed p-values, as provided by the end user and before matching with their nearest neighbor in the CDFs supports. |
pCDFlist |
a list of the supports of the CDFs of the p-values. Each support is represented by a vector that must be in increasing order. |
alpha |
the target FDR level, a number strictly between 0 and 1. For |
direction |
a character string specifying whether to conduct a step-up ( |
adaptive |
a boolean specifying whether to conduct an adaptive procedure or not. |
ret.crit.consts |
a boolean. If |
DBH
and ADBH
are wrapper functions for discrete.BH
.
DBH
simply passes all its parameters to discrete.BH
with adaptive = FALSE
.
ADBH
does the same with adaptive = TRUE
.
This version: 2019-06-18.
A DiscreteFDR
S3 class object whose elements are:
Rejected |
Rejected raw p-values |
Indices |
Indices of rejected hypotheses |
Num.rejected |
Number of rejections |
Adjusted |
Adjusted p-values (only for step-down direction). |
Critical.constants |
Critical constants (if requested) |
Method |
Character string describing the used algorithm, e.g. 'Discrete Benjamini-Hochberg procedure (step-up)' |
Signif.level |
Significance level |
Data$raw.pvalues |
The values of |
Data$pCDFlist |
The values of |
Data$data.name |
The respective variable names of |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | X1 <- c(4, 2, 2, 14, 6, 9, 4, 0, 1)
X2 <- c(0, 0, 1, 3, 2, 1, 2, 2, 2)
N1 <- rep(148, 9)
N2 <- rep(132, 9)
Y1 <- N1 - X1
Y2 <- N2 - X2
df <- data.frame(X1, Y1, X2, Y2)
df
#Construction of the p-values and their support
df.formatted <- fisher.pvalues.support(counts = df, input = "noassoc")
raw.pvalues <- df.formatted$raw
pCDFlist <- df.formatted$support
DBH.su.fast <- DBH(raw.pvalues, pCDFlist)
summary(DBH.su.fast)
DBH.sd.fast <- DBH(raw.pvalues, pCDFlist, direction = "sd")
DBH.sd.fast$Adjusted
summary(DBH.sd.fast)
DBH.su.crit <- DBH(raw.pvalues, pCDFlist, ret.crit.consts = TRUE)
summary(DBH.su.crit)
DBH.sd.crit <- DBH(raw.pvalues, pCDFlist, direction = "sd", ret.crit.consts = TRUE)
DBH.sd.crit$Adjusted
summary(DBH.sd.crit)
ADBH.su.fast <- ADBH(raw.pvalues, pCDFlist)
summary(ADBH.su.fast)
ADBH.sd.fast <- ADBH(raw.pvalues, pCDFlist, direction = "sd")
ADBH.sd.fast$Adjusted
summary(ADBH.sd.fast)
ADBH.su.crit <- ADBH(raw.pvalues, pCDFlist, ret.crit.consts = TRUE)
summary(ADBH.su.crit)
ADBH.sd.crit <- ADBH(raw.pvalues, pCDFlist, direction = "sd", ret.crit.consts = TRUE)
ADBH.sd.crit$Adjusted
summary(ADBH.sd.crit)
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