FSFWER.arbidept.p.adjust: Adjusted P-values for Fixed Sequence FWER Controlling...

In FixSeqMTP: Fixed Sequence Multiple Testing Procedures

Description

Given a set of pre-ordered p-values and accuracy for the result, returns adjusted p-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 α.

Usage

FSFWER.arbidept.p.adjust(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 compare with adjusted p-values to make decisions, the default value is 0.05.
beta	pre-specified constant satisfying 0 ≤ β <1, only for method="accept". The default value is 0.5.
tol	desired accuracy. The default value is 1e-6 .
method	adjustment 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 adjustment 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 adjusted p-values (of the same length as p) if make.decision = FALSE, or a data frame including original p-values, adjusted p-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.p.adjust and FSFDR.arbidept.p.adjust 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.p.adjust(p=Pval, alpha=0.05, method = "reject")
FSFWER.arbidept.p.adjust(p=Pval, alpha=0.05, beta=0.1, method = "accept")
FSFWER.arbidept.p.adjust(p=Pval, alpha=0.05, beta=0.5, method = "accept")
FSFWER.arbidept.p.adjust(p=Pval, alpha=0.05, beta=0.9, method = "accept")
FSFWER.arbidept.p.adjust(p=Pval, alpha=0.05, method = "both")

Example output

   raw.p    adjust.p decision
1 0.0008 0.006400108   reject
2 0.0135 0.094500542   accept
3 0.0197 0.118199348   accept
4 0.7237 0.999999046   accept
5 0.0003 0.002400398   reject
6 0.2779 0.999999046   accept
7 0.0054 0.032399178   reject
8 0.8473 0.999999046   accept
   raw.p     adjust.p decision
1 0.0008 0.0008897781   reject
2 0.0135 0.0150003433   reject
3 0.0197 0.0218896866   reject
4 0.7237 0.8041105270   accept
5 0.0003 0.0218896866   reject
6 0.2779 0.8041105270   accept
7 0.0054 0.5999994278   accept
8 0.8473 0.9414453506   accept
   raw.p    adjust.p decision
1 0.0008 0.001593590   reject
2 0.0135 0.026894569   reject
3 0.0197 0.039246559   reject
4 0.7237 0.999999046   accept
5 0.0003 0.004780769   reject
6 0.2779 0.999999046   accept
7 0.0054 0.043030739   reject
8 0.8473 0.999999046   accept
   raw.p    adjust.p decision
1 0.0008 0.004555702   reject
2 0.0135 0.076886177   accept
3 0.0197 0.112198830   accept
4 0.7237 0.999999046   accept
5 0.0003 0.002604485   reject
6 0.2779 0.999999046   accept
7 0.0054 0.046875954   reject
8 0.8473 0.999999046   accept
   raw.p    adjust.p decision
1 0.0008 0.003413200   reject
2 0.0135 0.057057381   accept
3 0.0197 0.080475807   accept
4 0.7237 0.999999046   accept
5 0.0003 0.002743721   reject
6 0.2779 0.999999046   accept
7 0.0054 0.045077324   reject
8 0.8473 0.999999046   accept

FixSeqMTP documentation built on May 1, 2019, 10:53 p.m.