FSFWER.arbidept.cv {FixSeqMTP}

Critical Values for Fixed Sequence FWER Controlling Procedures under Arbitrary Dependence

Description

Given a set of pre-ordered p-values and accuracy for the result, return the corresponding critical values using one of three generalized fixed sequence FWER controlling procedures. The function also provides an option to make decisions given a pre-specified significant level \alpha.

Usage

FSFWER.arbidept.cv(p, alpha=0.05, beta=0.5, tol = 1e-6,
  method = c("reject","accept","both"), make.decision = TRUE)

Arguments

p

numeric vector of p-values (possibly with NAs). Any other R is coerced by as.numeric. Same as in p.adjust.

alpha

significant level used to calculate the critical values to make decisions, the default value is 0.05.

beta

pre-specified constant satisfying 0 \le \beta <1, only for method="accept". The default value is 0.5.

tol

desired accuracy. The default value is 1e-6 .

method

critical value calculation method. See details.

make.decision

logical; if TRUE (default), then the output include the decision rules compared adjusted p-values with significant level alpha

Details

The critical value calculation methods for Fixed Sequence multiple testing include Procedure A1 only using numbers of rejections ("reject"), Procedure A2 only using numbers of acceptances ("accept") and Procedure A3 using both numbers of rejections and numbers of acceptances ("both"). The three methods strongly control FWER under arbitrary dependence. The constant beta needs to be specified for the Procedure A2 ("accept"), while one can ignore this argument when using other methods.

Value

A numeric vector of the critical values (of the same length as p) if make.decision = FALSE, or a data frame including original p-values, critical values and decision rules if make.decision = TRUE.

Author(s)

Yalin Zhu

References

Qiu, Z., Guo, W., & Lynch, G. (2015). On generalized fixed sequence procedures for controlling the FWER. Statistics in medicine, 34(30), 3968-3983.

See Also

FSFDR.arbidept.cv and FSFDR.indept.cv for fixed sequence FDR controlling procedures.

Examples

  ## Clinical trial example in Qiu et al. (2015)
Pval <- c(0.0008, 0.0135, 0.0197, 0.7237, 0.0003, 0.2779, 0.0054, 0.8473)
FSFWER.arbidept.cv(p=Pval, alpha=0.05, method = "reject")
FSFWER.arbidept.cv(p=Pval, alpha=0.05, beta=0.1, method = "accept")
FSFWER.arbidept.cv(p=Pval, alpha=0.05, beta=0.5, method = "accept")
FSFWER.arbidept.cv(p=Pval, alpha=0.05, beta=0.9, method = "accept")
FSFWER.arbidept.cv(p=Pval, alpha=0.05, method = "both")