test_values_bonferroni {graphicalMCP} | R Documentation |
An intersection hypothesis can be tested by a mixture of test types including
Bonferroni, parametric and Simes tests. This function organize outputs of
testing and prepare them for graph_report
.
test_values_bonferroni(p, hypotheses, alpha, intersection = NA)
test_values_parametric(p, hypotheses, alpha, intersection = NA, test_corr)
test_values_simes(p, hypotheses, alpha, intersection = NA)
p |
A numeric vector of p-values (unadjusted, raw), whose values should
be between 0 & 1. The length should match the number of hypotheses in
|
hypotheses |
A numeric vector of hypothesis weights in a graphical
multiple comparison procedure. Must be a vector of values between 0 & 1
(inclusive). The length should match the row and column lengths of
|
alpha |
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. |
intersection |
(optional) A numeric scalar used to name the intersection hypothesis in a weighting strategy. |
test_corr |
(Optional) A list of numeric correlation matrices. Each
entry in the list should correspond to each test group. For a test group
using Bonferroni or Simes tests, its corresponding entry in |
A data frame with rows corresponding to individual hypotheses
involved in the intersection hypothesis with hypothesis weights
hypotheses
. There are following columns:
Intersection
- Name of this intersection hypothesis,
Hypothesis
- Name of an individual hypothesis,
Test
- Test type for an individual hypothesis,
p
- (Unadjusted or raw) p-values for a individual hypothesis,
c_value
- C value for parametric tests,
Weight
- Hypothesis weight for an individual hypothesis,
Alpha
- Overall significance level \alpha
,
Inequality_holds
- Indicator to show if the p-value is less than or
equal to its significance level.
For Bonferroni and Simes tests, the significance level is the
hypothesis weight times \alpha
.
For parametric tests, the significance level is the c value times
the hypothesis weight times \alpha
.
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.