adjust_p_bonferroni {graphicalMCP} | R Documentation |
Calculate adjusted p-values
Description
For an intersection hypothesis, an adjusted p-value is the smallest
significance level at which the intersection hypothesis can be rejected.
The intersection hypothesis can be rejected if its adjusted p-value is less
than or equal to \alpha
. Currently, there are three test types
supported:
Bonferroni tests for
adjust_p_bonferroni()
,Parametric tests for
adjust_p_parametric()
,Note that one-sided tests are required for parametric tests.
Simes tests for
adjust_p_simes()
.
Usage
adjust_p_bonferroni(p, hypotheses)
adjust_p_parametric(
p,
hypotheses,
test_corr = NULL,
maxpts = 25000,
abseps = 1e-06,
releps = 0
)
adjust_p_simes(p, hypotheses)
Arguments
p |
A numeric vector of p-values (unadjusted, raw), whose values should
be between 0 & 1. The length should match the length of |
hypotheses |
A numeric vector of hypothesis weights. Must be a vector of
values between 0 & 1 (inclusive). The length should match the length of
|
test_corr |
(Optional) A numeric matrix of correlations between test
statistics, which is needed to perform parametric tests using
|
maxpts |
(Optional) An integer scalar for the maximum number of function
values, which is needed to perform parametric tests using the
|
abseps |
(Optional) A numeric scalar for the absolute error tolerance,
which is needed to perform parametric tests using the |
releps |
(Optional) A numeric scalar for the relative error tolerance
as double, which is needed to perform parametric tests using the
|
Value
A single adjusted p-value for the intersection hypothesis.
References
Bretz, F., Maurer, W., Brannath, W., and Posch, M. (2009). A graphical approach to sequentially rejective multiple test procedures. Statistics in Medicine, 28(4), 586-604.
Lu, K. (2016). Graphical approaches using a Bonferroni mixture of weighted Simes tests. Statistics in Medicine, 35(22), 4041-4055.
Xi, D., Glimm, E., Maurer, W., and Bretz, F. (2017). A unified framework for weighted parametric multiple test procedures. Biometrical Journal, 59(5), 918-931.
See Also
adjust_weights_parametric()
for adjusted hypothesis weights using
parametric tests, adjust_weights_simes()
for adjusted hypothesis weights
using Simes tests.
Examples
hypotheses <- c(H1 = 0.5, H2 = 0.25, H3 = 0.25)
p <- c(0.019, 0.025, 0.05)
adjust_p_bonferroni(p, hypotheses)
set.seed(1234)
hypotheses <- c(H1 = 0.5, H2 = 0.25, H3 = 0.25)
p <- c(0.019, 0.025, 0.05)
# Using the `mvtnorm::GenzBretz` algorithm
corr <- matrix(0.5, nrow = 3, ncol = 3)
diag(corr) <- 1
adjust_p_parametric(p, hypotheses, corr)
hypotheses <- c(H1 = 0.5, H2 = 0.25, H3 = 0.25)
p <- c(0.019, 0.025, 0.05)
adjust_p_simes(p, hypotheses)