Description Usage Arguments Details Value Author(s) References See Also Examples
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.
1 2 | FSFWER.arbidept.p.adjust(p, alpha=0.05, beta=0.5, tol = 1e-6,
method = c("reject","accept","both"), make.decision = TRUE)
|
p |
numeric vector of p-values (possibly with |
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 |
tol |
desired accuracy. The default value is |
method |
adjustment method. See details. |
make.decision |
logical; if |
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.
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
.
Yalin Zhu
Qiu, Z., Guo, W., & Lynch, G. (2015). On generalized fixed sequence procedures for controlling the FWER. Statistics in medicine, 34(30), 3968-3983.
FSFDR.arbidept.p.adjust
and FSFDR.arbidept.p.adjust
for fixed sequence FDR controlling procedures.
1 2 3 4 5 6 7 | ## 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")
|
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
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