[INTERNAL] This function reduces an adjacency matrix of correlations based on p-values.
If computations are done non-parallel corPvalueStudent is used. If computations
are done in parallel, our own parallel implementation (corPvalueStudentParallel)
of this function to calculate Student asymptotic p-values taking the number of samples into account is used.
P-values are adjusted using p.adjust function. The upper triangle without diagonal entries
of the adjacency matrix is passed for faster computation. P-values can be adjusted using one
of several methods. A significance threshold `alpha` can be set. All value entries below this threshold within the
initial adjacency matrix will be set to NA. If a default cluster is registered with the `parallel` package the
computation will happen in parallel automatically.
network_reduction_by_p_value(
adjacency_matrix,
number_of_samples,
p_value_adjustment_method = "BH",
reduction_alpha = 0.05,
parallel_chunk_size = 10^6
)A reduced adjacency matrix with NA's at martix entries with p-values below threshold.
[matrix] Adjacency matrix of correlations computed using cor in
compute_correlation_matrices
[int|matrix] The number of samples used to calculate the correlation matrix. Computed applying
sample_size
["holm"|"hochberg"|"hommel"|"bonferroni"|"BH"|"BY"|"fdr"|"none"] String of the correction method applied to p-values. Passed to p.adjust. (default: "BH")
[float] A number indicating the significance value for correlation p-values during reduction. Not-significant edges are dropped. (default: 0.05)
[int] Number of p-values in smallest work unit when computing in parallel during network reduction with method `p_value`. (default: 10^6)