direct.discrete.BH: Direct Application of Multiple Testing Procedures to Dataset

In DISOhda/DiscreteFDR: FDR Based Multiple Testing Procedures with Adaptation for Discrete Tests

Direct Application of Multiple Testing Procedures to Dataset

Description

Apply the [HSU], [HSD], [AHSU] or [AHSD] procedure, with or without computing the critical constants, to a data set of 2x2 contingency tables using Fisher's exact tests which may have to be transformed before computing p-values.

Usage

direct.discrete.BH(
  dat,
  test.fun,
  test.args = NULL,
  alpha = 0.05,
  direction = "su",
  adaptive = FALSE,
  ret.crit.consts = FALSE,
  select.threshold = 1,
  preprocess.fun = NULL,
  preprocess.args = NULL
)

Arguments

dat	input data; must be suitable for the first parameter of the provided preprocess.fun function or, if preprocess.fun is NULL, for the first parameter of the test.fun function.
test.fun	function from package DiscreteTests, i.e. one whose name ends with ⁠*_test_pv⁠ and which performs hypothesis tests and provides an object with p-values and their support sets; can be specified by a single character string (which is automatically checked for being a suitable function from that package and may be abbreviated) or a single function object.
test.args	optional named list with arguments for test.fun; the names of the list fields must match the test function's parameter names. The first parameter of the test function (i.e. the data) MUST NOT be included!
alpha	single real number strictly between 0 and 1 indicating the target FDR level.
direction	single character string specifying whether to perform a step-up ("su"; the default) or step-down procedure ("sd").
adaptive	single boolean specifying whether to conduct an adaptive procedure or not.
ret.crit.consts	single boolean specifying 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 threshold = 1 (the default), all raw p-values are selected.
preprocess.fun	optional function for pre-processing the input data; its result must be suitable for the first parameter of the test.fun function.
preprocess.args	optional named list with arguments for preprocess.fun; the names of the list fields must match the pre-processing function's parameter names. The first parameter of the test function (i.e. the data) MUST NOT be included!

Examples

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

DBH.su <- direct.discrete.BH(df, "fisher", direction = "su")
summary(DBH.su)

DBH.sd <- direct.discrete.BH(df, "fisher", direction = "sd")
DBH.sd$Adjusted
summary(DBH.sd)

ADBH.su <- direct.discrete.BH(df, "fisher", direction = "su",
                              adaptive = TRUE)
summary(ADBH.su)

ADBH.sd <- direct.discrete.BH(df, "fisher", direction = "sd",
                              adaptive = TRUE)
ADBH.sd$Adjusted
summary(ADBH.sd)

DISOhda/DiscreteFDR documentation built on April 14, 2025, 8:35 a.m.