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Apply the ABC model with statistical significance testing

Source: R/abc_model.R
abc_model_sig.Rd

This function extends the ABC model with statistical significance testing to evaluate the strength of discovered connections.

Usage

abc_model_sig(
  co_matrix,
  a_term,
  c_term = NULL,
  a_type = NULL,
  c_type = NULL,
  min_score = 0.1,
  n_results = 100,
  n_permutations = 1000,
  scoring_method = c("multiplication", "average", "combined", "jaccard")
)

Arguments

co_matrix

A co-occurrence matrix produced by create_cooccurrence_matrix().

a_term

Character string, the source term (A).

c_term

Character string, the target term (C). If NULL, all potential C terms will be evaluated.

a_type

Character string, the entity type for A terms. If NULL, all types are considered.

c_type

Character string, the entity type for C terms. If NULL, all types are considered.

min_score

Minimum score threshold for results.

n_results

Maximum number of results to return.

n_permutations

Number of permutations for significance testing.

scoring_method

Method to use for scoring ABC connections.

Value

A data frame with ranked discovery results and p-values.

Examples

if (FALSE) { # \dontrun{
abc_results <- abc_model_sig(co_matrix, a_term = "migraine",
                                                scoring_method = "combined")
} # }