FDX (version 1.0.2)

fast.Discrete: Fast application of discrete procedures

Description

Applies the [DLR], [DGR] or [DPB] procedures, without computing the critical values, to a data set of 2 x 2 contingency tables using Fisher's exact test.

Usage

fast.Discrete.LR(
  counts,
  alternative = "greater",
  input = "noassoc",
  alpha = 0.05,
  zeta = 0.5,
  direction = "sd",
  adaptive = TRUE
)
fast.Discrete.PB(
  counts,
  alternative = "greater",
  input = "noassoc",
  alpha = 0.05,
  zeta = 0.5,
  adaptive = TRUE,
  exact = FALSE
)
fast.Discrete.GR(
  counts,
  alternative = "greater",
  input = "noassoc",
  alpha = 0.05,
  zeta = 0.5,
  adaptive = TRUE
)

Arguments

counts

a data frame of 2 or 4 columns and any number of lines, each line representing a 2 x 2 contingency table to test. The number of columns and what they must contain depend on the value of the input argument, see Details of fisher.pvalues.support.

alternative

same argument as in fisher.test. The three possible values are "greater" (default), "two.sided" or "less"; may be abbreviated.

input

the format of the input data frame, see Details of fisher.pvalues.support. The three possible values are "noassoc" (default), "marginal" or "HG2011"; may be abbreviated.

alpha

the target FDP, a number strictly between 0 and 1. For *.fast kernels, it is only necessary, if stepUp = TRUE.

zeta

the target probability of not exceeding the desired FDP, a number strictly between 0 and 1. If zeta=NULL (the default), then zeta is chosen equal to alpha.

direction

a character string specifying whether to conduct a step-up (direction="su", the default) or step-down procedure (direction="sd").

adaptive

a boolean specifying whether to conduct an adaptive procedure or not.

exact

a boolean specifying whether to compute the Poisson-Binomial distribution exactly or by a normal approximation.

Value

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).

Method

A character string describing the used algorithm, e.g. 'Discrete Lehmann-Romano procedure (step-up)'.

FDP.threshold

FDP threshold alpha.

Exceedance.probability

Probability zeta of FDP exceeding alpha; thus, FDP is being controlled at level alpha with confidence 1 - zeta.

Adaptive

A boolean specifying whether an adaptive procedure was conducted or not.

Data$raw.pvalues

The values of raw.pvalues.

Data$data.name

The respective variable names of raw.pvalues and pCDFlist.

Examples

# NOT RUN {
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

DLR.sd <- fast.Discrete.LR(counts = df, input = "noassoc")
DLR.sd$Adjusted
summary(DLR.sd)
DLR.su <- fast.Discrete.LR(counts = df, input = "noassoc", direction = "su")
summary(DLR.su)

NDLR.sd <- fast.Discrete.LR(counts = df, input = "noassoc", adaptive = FALSE)
NDLR.sd$Adjusted
summary(NDLR.sd)
NDLR.su <- fast.Discrete.LR(counts = df, input = "noassoc", direction = "su", adaptive = FALSE)
summary(NDLR.su)

DGR <- fast.Discrete.GR(counts = df, input = "noassoc")
DGR$Adjusted
summary(DGR)

NDGR <- fast.Discrete.GR(counts = df, input = "noassoc", adaptive = FALSE)
NDGR$Adjusted
summary(NDGR)

DPB <- fast.Discrete.PB(counts = df, input = "noassoc")
DPB$Adjusted
summary(DPB)

NDPB <- fast.Discrete.PB(counts = df, input = "noassoc", adaptive = FALSE)
NDPB$Adjusted
summary(NDPB)

# }