c_value_function {graphicalMCP} | R Documentation |
An intersection hypothesis can be rejected if its p-values are less than or
equal to their adjusted significance levels, which are their adjusted
hypothesis weights times \alpha
. For Bonferroni tests, their adjusted
hypothesis weights are their hypothesis weights of the intersection
hypothesis. Additional adjustment is needed for parametric tests:
Parametric tests for adjust_weights_parametric()
,
Note that one-sided tests are required for parametric tests.
c_value_function(
x,
hypotheses,
test_corr,
alpha,
maxpts = 25000,
abseps = 1e-06,
releps = 0
)
solve_c_parametric(
hypotheses,
test_corr,
alpha,
maxpts = 25000,
abseps = 1e-06,
releps = 0
)
x |
The root to solve for with |
hypotheses |
A numeric vector of hypothesis weights. Must be a vector of values between 0 & 1 (inclusive). The sum of hypothesis weights should not exceed 1. |
test_corr |
(Optional) A numeric matrix of correlations between test
statistics, which is needed to perform parametric tests using
|
alpha |
(Optional) A numeric value of the overall significance level, which should be between 0 & 1. The default is 0.025 for one-sided hypothesis testing problems; another common choice is 0.05 for two-sided hypothesis testing problems. Note when parametric tests are used, only one-sided tests are supported. |
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
|
c_value_function()
returns the difference between
\alpha
and the Type I error of the parametric test with the c
value of x
, adjusted for the correlation between test statistics using
parametric tests based on equation (6) of Xi et al. (2017).
solve_c_parametric()
returns the c value adjusted for the
correlation between test statistics using parametric tests based on
equation (6) of Xi et al. (2017).
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.
adjust_weights_parametric()
for adjusted hypothesis weights using
parametric tests.