Validate inputs for testing and power simulations

Usage

test_input_val(
  graph,
  p,
  alpha,
  test_groups = list(seq_along(graph$hypotheses)),
  test_types = c("bonferroni"),
  test_corr,
  verbose,
  test_values
)

power_input_val(graph, sim_n, power_marginal, test_corr, success)

Arguments

graph: An initial graph as returned by graph_create().
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 graph.
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.
test_groups: A list of numeric vectors specifying hypotheses to test together. Grouping is needed to correctly perform Simes and parametric tests.
test_types: A character vector of test types to apply to each test group. This is needed to correctly perform Simes and parametric tests. The length should match the number of elements in test_groups.
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 test_corr should be NA. For a test group using parametric tests, its corresponding entry in test_corr should be a numeric correlation matrix specifying the correlation between test statistics for hypotheses in this test group. The length should match the number of elements in test_groups.
verbose: A logical scalar specifying whether the details of the adjusted p-value calculations should be included in results. When verbose = TRUE, adjusted p-values are provided for each intersection hypothesis. The default is verbose = FALSE.
test_values: A logical scalar specifying whether adjusted significance levels should be provided for each hypothesis. When test_values = TRUE, it provides an equivalent way of performing graphical multiple comparison procedures by comparing each p-value with its significance level. If the p-value of a hypothesis is less than or equal to its significance level, the hypothesis is rejected. The default is test_values = FALSE.
sim_n: An integer scalar specifying the number of simulations. The default is 1e5.
power_marginal: A numeric vector of marginal power values to use when simulating p-values. See Details for more on the simulation process.
success: A list of user-defined functions to specify the success criteria. Functions must take one simulation's logical vector of results as an input, and return a length-one logical vector. For instance, if "success" means rejecting hypotheses 1 and 2, use sim_success = list("1 and 2" = function(x) x[1] && x[2]). If the list is not named, the function body will be used as the name. Lambda functions also work starting with R 4.1, e.g. sim_success = list(\(x) x[3] || x[4]).

Value

Returns graph invisibly