anogva {statGraph}

Analysis Of Graph Variability (ANOGVA)

Description

anogva statistically tests whether two or more sets of graphs are generated by the same random graph model. It is a generalization of the takahashi.test function.

Usage

anogva(Graphs, labels, maxBoot = 1000, dist = "KL", ...)

Arguments

Graphs

a list of undirected graphs. If each graph has the attribute eigenvalues containing its eigenvalues , such values will be used to compute their spectral density.

labels

an array of integers indicating the labels of each graph.

maxBoot

integer indicating the number of bootstrap resamplings (default 1000).

dist

string indicating if you want to use the 'KL' (default), 'JS' , 'L1' or 'L2' distances. 'KL' means Kullback-Leibler divergence. 'JS' means Jensen-Shannon divergence.

...

Other relevant parameters for graph.spectral.density.

Value

A list with class 'htest' containing the following components:

statistic:

the statistic of the test.

p.value:

the p-value of the test.

method:

a string indicating the used method.

data.name:

a string with the data's name(s).

References

Fujita, A., Vidal, M. C. and Takahashi, D. Y. (2017) A Statistical Method to Distinguish Functional Brain Networks. _Front. Neurosci._, *11*, 66. doi:10.3389/fnins.2017.00066.

Takahashi, D. Y., Sato, J. R., Ferreira, C. E. and Fujita A. (2012) Discriminating Different Classes of Biological Networks by Analyzing the Graph Spectra Distribution. _PLoS ONE_, *7*, e49949. doi:10.1371/journal.pone.0049949.

Silverman, B. W. (1986) _Density Estimation_. London: Chapman and Hall.

Sturges, H. A. The Choice of a Class Interval. _J. Am. Statist. Assoc._, *21*, 65-66.

Sheather, S. J. and Jones, M. C. (1991). A reliable data-based bandwidth selection method for kernel density estimation. _Journal of the Royal Statistical Society series B_, 53, 683-690. http://www.jstor.org/stable/2345597.

Examples

set.seed(1)
g1 <- g2 <- g3 <- list()
for (i in 1:20) {
  g1[[i]] <- igraph::sample_gnp(50, 0.50)
  g2[[i]] <- igraph::sample_gnp(50, 0.50)
  g3[[i]] <- igraph::sample_gnp(50, 0.52)
}
G <- c(g1, g2, g3)
label <- c(rep(1,20),rep(2,20),rep(3,20))
result <- anogva(G, label, maxBoot=50)
result