context("Correlated E-R random graphs")
## Not very meaningful tests. They good for testing that the
## functions run, but not much more
test_that("sample_correlated_gnp works", {
library(igraph)
set.seed(42)
g <- erdos.renyi.game(10, .1)
g2 <- sample_correlated_gnp(g, corr=1, p=g$p, permutation=NULL)
expect_that(g[], equals(g2[]))
g3 <- sample_correlated_gnp(g, corr=0, p=g$p, permutation=NULL)
c3 <- cor(as.vector(g[]), as.vector(g3[]))
expect_true(abs(c3) < .3)
})
test_that("sample_correlated_gnp works when p is not given", {
library(igraph)
set.seed(42)
g <- erdos.renyi.game(10, .1)
g2 <- sample_correlated_gnp(g, corr=1)
expect_that(g[], equals(g2[]))
g3 <- sample_correlated_gnp(g, corr=0)
c3 <- cor(as.vector(g[]), as.vector(g3[]))
expect_true(abs(c3) < .3)
})
test_that("sample_correlated_gnp works even for non-ER graphs", {
library(igraph)
set.seed(42)
g <- grg.game(100, 0.2)
g2 <- sample_correlated_gnp(g, corr=1)
expect_that(g[], equals(g2[]))
g3 <- sample_correlated_gnp(g, corr=0)
c3 <- cor(as.vector(g[]), as.vector(g3[]))
expect_true(abs(c3) < .3)
})
test_that("sample_correlated_gnp_pair works", {
library(igraph)
set.seed(42)
gp <- sample_correlated_gnp_pair(10, corr=.95, p=.1, permutation=NULL)
expect_true(abs(ecount(gp[[1]]) - ecount(gp[[2]])) < 3)
})
## Some corner cases
test_that("sample_correlated_gnp corner cases work", {
library(igraph)
set.seed(42)
is.full <- function(g) {
g2 <- graph.full(vcount(g), directed=is.directed(g))
graph.isomorphic(g, g2)
}
g <- erdos.renyi.game(10, .3)
g2 <- sample_correlated_gnp(g, corr=0.000001, p=.99999999)
expect_true(is.full(g2))
g3 <- sample_correlated_gnp(g, corr=0.000001, p=0.0000001)
expect_that(ecount(g3), equals(0))
expect_that(vcount(g3), equals(10))
gg <- erdos.renyi.game(10, .3, directed=TRUE)
gg2 <- sample_correlated_gnp(gg, corr=0.000001, p=.99999999)
expect_true(is.full(gg2))
gg3 <- sample_correlated_gnp(gg, corr=0.000001, p=0.0000001)
expect_that(ecount(gg3), equals(0))
expect_that(vcount(gg3), equals(10))
})
test_that("permutation works for sample_correlated_gnp", {
library(igraph)
set.seed(42)
g <- erdos.renyi.game(10, .3)
perm <- sample(vcount(g))
g2 <- sample_correlated_gnp(g, corr=.99999, p=.3, permutation=perm)
g <- permute.vertices(g, perm)
expect_that(g[], equals(g2[]))
g <- erdos.renyi.game(10, .3)
perm <- sample(vcount(g))
g2 <- sample_correlated_gnp(g, corr=1, p=.3, permutation=perm)
g <- permute.vertices(g, perm)
expect_that(g[], equals(g2[]))
})
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