graph.cor.test: Test for Association / Correlation Between Paired Samples of...

In statGraph: Statistical Methods for Graphs

Description

graph.cor.test tests for association between paired samples of graphs, using Spearman's rho correlation coefficient.

Usage

graph.cor.test(G1, G2)

Arguments

G1	a list of undirected graphs (igraph type) or their adjacency matrices. The adjacency matrix of an unweighted graph contains only 0s and 1s, while the weighted graph may have nonnegative real values that correspond to the weights of the edges.
G2	a list of undirected graphs (igraph type) or their adjacency matrices. The adjacency matrix of an unweighted graph contains only 0s and 1s, while the weighted graph may have nonnegative real values that correspond to the weights of the edges.

Value

statistic	the value of the test statistic.
p.value	the p-value of the test.
estimate	the estimated measure of association 'rho'.

References

Fujita, A., Takahashi, D. Y., Balardin, J. B., Vidal, M. C. and Sato, J. R. (2017) Correlation between graphs with an application to brain network analysis. _Computational Statistics & Data Analysis_ *109*, 76-92.

Examples

set.seed(1)
G1 <- G2 <- list()

p <- MASS::mvrnorm(50, mu=c(0,0), Sigma=matrix(c(1, 0.5, 0.5, 1), 2, 2))

ma <- max(p)
mi <- min(p)
p[,1] <- (p[,1] - mi)/(ma - mi)
p[,2] <- (p[,2] - mi)/(ma - mi)

for (i in 1:50) {
  G1[[i]] <- igraph::sample_gnp(50, p[i,1])
  G2[[i]] <- igraph::sample_gnp(50, p[i,2])
}
graph.cor.test(G1, G2)

statGraph documentation built on May 19, 2021, 9:11 a.m.