These functions performs similarly to graph_test_closure() or
graph_test_shortcut() but are optimized for efficiently calculating power.
For example, generating weights and calculating adjusted weights can be done
only once. Vectorization has been applied where possible.
graph_test_closure_fast(p, alpha, adjusted_weights, matrix_intersections)graph_test_shortcut_fast(p, alpha, adjusted_weights)
A logical or integer vector indicating whether each hypothesis can be rejected or not.
A numeric vector of one-sided p-values (unadjusted, raw), whose
values should be between 0 & 1. The length should match the number of
hypotheses in graph.
A numeric value of the one-sided overall significance level, which should be between 0 & 1. The default is 0.025 for one-sided hypothesis testing. Note that only one-sided tests are supported.
The adjusted hypothesis weights, which are the
second half of columns from graph_generate_weights() output, adjusted by
the appropriate test types (Bonferroni, Simes, or parametric).
A matrix of hypothesis indicators in a weighting
strategy, which are the first half the graph_generate_weights() output.
graph_test_closure() for closed graphical multiple comparison
procedures.
graph_test_shortcut() for shortcut graphical multiple comparison
procedures.