continuous.GR | R Documentation |
Apply the usual continuous [GR] procedure, with or without computing the critical values, to a set of p-values. A non-adaptive version is available as well.
continuous.GR(
test.results,
alpha = 0.05,
zeta = 0.5,
adaptive = TRUE,
critical.values = FALSE,
select.threshold = 1
)
GR(
test.results,
alpha = 0.05,
zeta = 0.5,
critical.values = FALSE,
select.threshold = 1
)
NGR(
test.results,
alpha = 0.05,
zeta = 0.5,
critical.values = FALSE,
select.threshold = 1
)
test.results |
either a numeric vector with p-values or an R6 object of class |
alpha |
single real number strictly between 0 and 1 specifying the target FDP. |
zeta |
single real number strictly between 0 and 1 specifying the target probability of not exceeding the desired FDP. If |
adaptive |
single boolean indicating whether to conduct an adaptive procedure or not. |
critical.values |
single boolean indicating whether critical constants are to be computed. |
select.threshold |
single real number strictly between 0 and 1 indicating the largest raw p-value to be considered, i.e. only p-values below this threshold are considered and the procedures are adjusted in order to take this selection effect into account; if |
GR
and NGR
are wrapper functions for continuous.GR
. The
first one simply passes all its arguments to continuous.GR
with
adaptive = TRUE
and NGR
does the same with
adaptive = FALSE
.
A FDX
S3 class object whose elements are:
Rejected |
rejected raw |
Indices |
indices of rejected |
Num.rejected |
number of rejections. |
Adjusted |
adjusted |
Critical.values |
critical values (only exists if computations where performed with |
Select |
list with data related to |
Select$Threshold |
|
Select$Effective.Thresholds |
results of each |
Select$Pvalues |
selected |
Select$Indices |
indices of |
Select$Scaled |
scaled selected |
Select$Number |
number of selected |
Data |
list with input data. |
Data$Method |
character string describing the used algorithm, e.g. 'Discrete Lehmann-Romano procedure (step-up)'. |
Data$Raw.pvalues |
all observed raw |
Data$FDP.threshold |
FDP threshold |
Data$Exceedance.probability |
probability |
Data$Adaptive |
boolean indicating whether an adaptive procedure was conducted or not. |
Data$Data.name |
the respective variable name(s) of the input data. |
Guo, W. & Romano, J. P. (2007). A generalized Sidak-Holm procedure and control of generalized error rates under independence. Statistical Applications in Genetics and Molecular Biology, 6(1), Art. 3, 35 pp. (electronic). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2202/1544-6115.1247")}
kernel
, FDX-package
, continuous.LR()
,
discrete.LR()
, discrete.GR()
,
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 with Fisher's exact test
library(DiscreteTests) # for Fisher's exact test
test.results <- fisher_test_pv(df)
raw.pvalues <- test.results$get_pvalues()
pCDFlist <- test.results$get_pvalue_supports()
# GR without critical values; using extracted p-values
GR.fast <- GR(raw.pvalues)
summary(GR.fast)
# LR with critical values; using test results object
GR.crit <- GR(test.results, critical.values = TRUE)
summary(GR.crit)
# Non-adaptive GR without critical values; using test results object
NGR.fast <- NGR(test.results)
summary(NGR.fast)
# Non-adaptive GR with critical values; using extracted p-values
NGR.crit <- NGR(raw.pvalues, critical.values = TRUE)
summary(NGR.crit)
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