DBR {DiscreteFDR} | R Documentation |
\lambda
] procedureApply the [HBR-\lambda
] procedure,
with or without computing the critical constants,
to a set of p-values and their discrete support.
DBR(
raw.pvalues,
pCDFlist,
alpha = 0.05,
lambda = NULL,
ret.crit.consts = FALSE
)
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 |
lambda |
a number strictly between 0 and 1. If |
ret.crit.consts |
a boolean. If |
[DBR-lambda] is the discrete version of the [Blanchard-Roquain-lambda] procedure (see References),
the authors of the latter suggest to take lambda = alpha
(see their Proposition 17),
which explains the choice of the default value here.
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 |
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 |
Lambda |
Value of |
Data$raw.pvalues |
The values of |
Data$pCDFlist |
The values of |
Data$data.name |
The respective variable names of |
G. Blanchard and E. Roquain (2009). Adaptive false discovery rate control under independence and dependence. Journal of Machine Learning Research, 10, 2837-2871.
X1 <- c(4, 2, 2, 14, 6, 9, 4, 0, 1)
X2 <- c(0, 0, 1, 3, 2, 1, 2, 2, 2)
N1 <- rep(148, 9)
N2 <- rep(132, 9)
Y1 <- N1 - X1
Y2 <- N2 - X2
df <- data.frame(X1, Y1, X2, Y2)
df
#Construction of the p-values and their support
df.formatted <- fisher.pvalues.support(counts = df, input = "noassoc")
raw.pvalues <- df.formatted$raw
pCDFlist <- df.formatted$support
DBR.fast <- DBR(raw.pvalues, pCDFlist)
summary(DBR.fast)
DBR.crit <- DBR(raw.pvalues, pCDFlist, ret.crit.consts = TRUE)
summary(DBR.crit)