graph.takahashi.test {statGraph} | R Documentation |
Test for the Jensen-Shannon Divergence Between Graphs
Description
graph.takahashi.test
tests whether two sets of graphs were generated by the same
random graph model.
This bootstrap test is based on the Jensen-Shannon (JS) divergence between
graphs.
Usage
graph.takahashi.test(Graphs1, Graphs2, maxBoot = 1000, dist = "JS", ...)
Arguments
Graphs1 |
a list of undirected Graphs.
If each graph has the attribute |
Graphs2 |
a list of undirected Graphs.
If each graph has the attribute |
maxBoot |
integer indicating the number of bootstrap resamplings (default |
dist |
string indicating if you want to use the 'JS' (default) , 'L1' or 'L2' distances. 'JS' means Jensen-Shannon divergence. |
... |
Other relevant parameters for |
Details
Given two lists of graphs, Graphs1
and Graphs2
, graph.takahashi.test
tests H0: 'JS
divergence between Graphs1
and Graphs2
is 0
' against H1: 'JS divergence between
Graphs1
and Graphs2
is larger than 0
'.
Value
A list with class 'htest' containing the following components:
statistic: |
the value of the Jensen-Shannon divergence (default), L1 or L2 between 'Graphs1' and 'Graphs2'. |
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
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 <- list()
for (i in 1:20) {
G1[[i]] <- igraph::sample_gnp(n=50, p=0.500)
}
for (i in 1:20) {
G2[[i]] <- igraph::sample_gnp(n=50, p=0.512)
}
result <- graph.takahashi.test(G1, G2, maxBoot=500)
result