View source: R/discreteGR_fun.R
discrete.GR | R Documentation |
Apply the [DGR] procedure, with or without computing the critical values, to a set of p-values and their discrete support. A non-adaptive version is available as well.
discrete.GR( raw.pvalues, pCDFlist, alpha = 0.05, zeta = 0.5, adaptive = TRUE, critical.values = FALSE ) DGR(raw.pvalues, pCDFlist, alpha = 0.05, zeta = 0.5, critical.values = FALSE) NDGR(raw.pvalues, pCDFlist, alpha = 0.05, zeta = 0.5, critical.values = 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 FDP, a number strictly between 0 and 1. For |
zeta |
the target probability of not exceeding the desired FDP, a number strictly between 0 and 1. If |
adaptive |
a boolean specifying whether to conduct an adaptive procedure or not. |
critical.values |
a boolean. If |
DGR
and NDGR
are wrapper functions for discrete.GR
.
The first one simply passes all its parameters to discrete.GR
with
adaptive = TRUE
and NDGR
does the same with
adaptive = FALSE
.
A FDX
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.values |
Critical values (if requested). |
Method |
A character string describing the used algorithm, e.g. 'Discrete Lehmann-Romano procedure (step-up)'. |
FDP.threshold |
FDP threshold |
Exceedance.probability |
Probability |
Adaptive |
A boolean specifying whether an adaptive procedure was conducted or not. |
Data$raw.pvalues |
The values of |
Data$pCDFlist |
The values of |
Data$data.name |
The respective variable names of |
S. Döhler and E. Roquain (2019). Controlling False Discovery Exceedance for Heterogeneous Tests. arXiv:1912.04607v1.
kernel
, FDX-package
, continuous.LR
,
continuous.GR
, discrete.LR
,
discrete.PB
, weighted.LR
,
weighted.GR
, weighted.PB
X1 <- c(4, 2, 2, 14, 6, 9, 4, 0, 1) X2 <- c(0, 0, 1, 3, 2, 1, 2, 2, 2) N1 <- rep(148, 9) N2 <- rep(132, 9) Y1 <- N1 - X1 Y2 <- N2 - X2 df <- data.frame(X1, Y1, X2, Y2) df # Construction of the p-values and their supports (fisher.pvalues.support # is from 'DiscreteFDR' package!) df.formatted <- fisher.pvalues.support(counts = df, input = "noassoc") raw.pvalues <- df.formatted$raw pCDFlist <- df.formatted$support DGR.fast <- DGR(raw.pvalues, pCDFlist) summary(DGR.fast) DGR.crit <- DGR(raw.pvalues, pCDFlist, critical.values = TRUE) summary(DGR.crit) NDGR.fast <- NDGR(raw.pvalues, pCDFlist) summary(NDGR.fast) NDGR.crit <- NDGR(raw.pvalues, pCDFlist, critical.values = TRUE) summary(NDGR.crit)
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