statGraph (version 1.0.6)

sp.anogva: Semi-parametric Analysis of Graph Variability (SP-ANOGVA)

Description

sp.anogva statistically tests whether two or more graphs are generated by the same model and set of parameters.

Usage

sp.anogva(Graphs, model, maxBoot = 100, ...)

Value

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

statistic:

the F 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).

estimates:

a vector containing the estimated parameters for each graph.

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.

model

A string that indicates one of the following models: 'ER' (Erdos-Renyi random graph model), 'GRG' (geometric random graph model), 'WS' (Watts-Strogatz random graph model), and 'BA' (Barabási-Albert random graph model).

maxBoot

integer indicating the number of bootstrap resamples (default is 500).

...

Other relevant parameters for graph.param.estimator.

References

Andre Fujita, Eduardo Silva Lira, Suzana de Siqueira Santos, Silvia Yumi Bando, Gabriela Eleuterio Soares, Daniel Yasumasa Takahashi. A semi-parametric statistical test to compare complex networks, Journal of Complex Networks, cnz028, https://doi.org/10.1093/comnet/cnz028

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

# \donttest{
set.seed(1)
model <- 'ER'
G <- list()

# Under H0
G[[1]] <- igraph::sample_gnp(50, 0.5)
G[[2]] <- igraph::sample_gnp(50, 0.5)
G[[3]] <- igraph::sample_gnp(50, 0.5)
result1 <- sp.anogva(G, model, maxBoot = 10,eps=0.1)
result1

# Under H1
G[[1]] <- igraph::sample_gnp(50, 0.5)
G[[2]] <- igraph::sample_gnp(50, 0.75)
G[[3]] <- igraph::sample_gnp(50, 0.5)
result2 <- sp.anogva(G, model, maxBoot = 10,eps=0.1)
result2
# }