Test Statistics for Network PopulationsSource:
This is a collection of functions that provide statistics for testing equality in distribution between samples of networks.
stat_student_euclidean(d, indices, ...) stat_welch_euclidean(d, indices, ...) stat_original_edge_count(d, indices, edge_count_prep, ...) stat_generalized_edge_count(d, indices, edge_count_prep, ...) stat_weighted_edge_count(d, indices, edge_count_prep, ...)
Either a matrix of dimension \((n1+n2)x(n1+n2)\) containing the distances between all the elements of the two samples put together (for distance-based statistics) or the concatenation of the lists of matrix representations of networks in samples 1 and 2 for Euclidean t-Statistics.
A vector of dimension \(n1\) containing the indices of the elements of the first sample.
Extra parameters specific to some statistics.
A list of preprocessed data information used by edge count statistics and produced by
In details, there are three main categories of statistics:
Euclidean t-Statistics: both Student
stat_student_euclideanversion for equal variances and Welch
stat_welch_euclideanversion for unequal variances,
Statistics based on similarity graphs: 3 types of edge count statistics.
n1 <- 30L n2 <- 10L gnp_params <- list(p = 1/3) k_regular_params <- list(k = 8L) x <- nvd(model = "gnp", n = n1, model_params = gnp_params) y <- nvd(model = "k_regular", n = n2, model_params = k_regular_params) r <- repr_nvd(x, y, representation = "laplacian") stat_student_euclidean(r, 1:n1) #>  0.8055932 stat_welch_euclidean(r, 1:n1) #>  0.9542853 d <- dist_nvd(x, y, representation = "laplacian", distance = "frobenius") ecp <- edge_count_global_variables(d, n1, k = 5L) stat_original_edge_count(d, 1:n1, edge_count_prep = ecp) #>  -5.644431 stat_generalized_edge_count(d, 1:n1, edge_count_prep = ecp) #>  37.37713 stat_weighted_edge_count(d, 1:n1, edge_count_prep = ecp) #>  -0.3640767