tests/testthat/test_1_risk_com.R
In carloscinelli/NetworkRiskMeasures: Risk Measures for (Financial) Networks
library(testthat)
context("Risk and Communicability Matrices")
test_that("vulnerability and communicability",
{
liabilities <- structure(c(0, 0, 0, 0, 0, 0,
5, 0, 0, 0, 0, 2,
0, 3, 0, 0, 2, 2,
0, 0, 4, 0, 5, 0,
10, 2, 0, 0, 5, 0,
2, 0, 0, 0, 0, 0),
.Dim = c(6L, 6L),
.Dimnames = list(c("A", "B", "C", "E", "F", "D"),
c("A", "B", "C", "E", "F", "D")))
assets <- t(liabilities)
capital_buffer <- c(A = 10, B = 2, C = 2, E = 1, F = 10, D = 4)
res <- structure(c(0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 1,
0, 1, 0, 0, 1, 1,
0, 0, 1, 0, 1, 0,
1, 0.2, 0, 0, 0.5, 0,
0.5, 0, 0, 0, 0, 0),
.Dim = c(6L, 6L),
.Dimnames = list(c("A", "B", "C", "E", "F", "D"),
c("A","B", "C", "E", "F", "D")))
v1 <- impact_matrix(exposures = liabilities,
buffer = capital_buffer,
binary = FALSE, exposure_type = "liabilities")
v1[v1 > 1] <- 1
expect_equal(v1, res)
# zero buffer, error
capital_buffer2 <- c(A = 0, B = 2, C = 0, E = 1, F = 10, D = 4)
expect_error(v1zero <- impact_matrix(exposures = liabilities,
buffer = capital_buffer2,
binary = FALSE, exposure_type = "liabilities"))
# na buffer, error
capital_buffer2 <- c(A = NA, B = 2, C = NA, E = 1, F = 10, D = 4)
expect_error(v1zero <- impact_matrix(exposures = liabilities,
buffer = capital_buffer2,
binary = FALSE, exposure_type = "liabilities"))
# wrong size buffer, error
capital_buffer2 <- c(A = 3, B = 2, C = 3, E = 1)
expect_error(v1zero <- impact_matrix(exposures = liabilities,
buffer = capital_buffer2,
binary = FALSE, exposure_type = "liabilities"))
v1 <- impact_matrix(exposures = liabilities,
buffer = capital_buffer,
binary = FALSE, exposure_type = "liabilities")
v1v <- vulnerability_matrix(exposures = liabilities,
buffer = capital_buffer,
binary = FALSE, exposure_type = "liabilities")
expect_equal(v1v, t(v1))
expect_warning(vulnerability_matrix(unname(liabilities), capital_buffer))
v2 <- impact_matrix(exposures = assets,
buffer = capital_buffer,
binary = FALSE,
exposure_type = "assets")
v2[v2 > 1] <- 1
expect_equal(v2, res)
v3 <- impact_matrix(exposures = liabilities,
buffer = capital_buffer,
binary = TRUE,
exposure_type = "liabilities")
expect_equal(v3, (res >= 1)*1)
v4 <- impact_matrix(exposures = assets,
buffer = capital_buffer,
binary = TRUE,
exposure_type = "assets")
expect_equal(v4, (res >= 1)*1)
res2 <- new("dgeMatrix",
x = c(1, 0, 0, 0, 0, 0, 1, 1,
0, 0, 0, 1, 1, 1, 1, 0, 1, 1.5, 0.833333333333333,
0.5, 1, 1, 1.5, 0.666666666666667, 1, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 1),
Dim = c(6L, 6L),
Dimnames = list(c("A", "B", "C", "E", "F", "D"),
c("A", "B", "C", "E", "F","D")),
factors = list())
## Testing igraph method only if package is installed
if (require(igraph, quietly = TRUE) && installed.packages()["igraph","Version"] > "1.0.0") {
g <- graph_from_adjacency_matrix(assets)
vi <- impact_matrix(exposures = g,
buffer = capital_buffer,
binary = FALSE,
exposure_type = "assets")
vi[vi > 1] <- 1
expect_equal(as.matrix(vi), res)
vib <- impact_matrix(exposures = g,
buffer = capital_buffer,
binary = TRUE,
exposure_type = "assets")
expect_equal(as.matrix(vib), (res >= 1)*1)
}
c1 <- communicability_matrix(v4)
expect_equal(c1, res2)
c2 <- communicability_matrix(Matrix(v4))
expect_equal(c1, res2)
c3 <- communicability_matrix(v4, terms = 10, sparse = F)
expect_equal(c3, as.matrix(res2))
## Testing igraph method only if package is installed
if (require(igraph, quietly = TRUE) && installed.packages()["igraph","Version"] > "1.0.0") {
g <- graph_from_adjacency_matrix(assets)
vib <- impact_matrix(exposures = g,
buffer = capital_buffer,
binary = TRUE,
exposure_type = "assets")
ci <- communicability_matrix(vib)
expect_equal(ci, res2)
}
binary <- vulnerability_matrix(exposures = liabilities,
buffer = capital_buffer,
binary = TRUE, exposure_type = "liabilities")
v1v <- (v1v >=1)*1
expect_equal(binary, v1v)
expect_error(vulnerability_matrix(exposures = liabilities,
buffer = "a"))
expect_error(vulnerability_matrix(exposures = liabilities,
buffer = 1, binary = 1))
}
)
# test_that("DebtRank - Random Data",
# {
# res <-
# structure(
# list(
# DebtRank = structure(
# list(
# stressed_vertex = c("1",
# "2", "3", "4", "5", "6", "7", "8", "9", "10"),
# vertex_weight = c(0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0),
# additional_stress = c(0,0.125596829283833,0,0,0.0464057085078027,0.0652369423410151,0,0.0188312338332124,0,0),
# additional_defaults = c(0L, 0L,0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L)),
#
# .Names = c(
# "stressed_vertex",
# "vertex_weight",
# "additional_stress",
# "additional_defaults"
# ),
#
# row.names = c(NA,-10L),
# class = "data.frame"
# ),
#
# StressLevel = structure(
# list(
# stressed_vertex = c("1","1","1","1","1","1","1","1","1","1","2","2","2","2","2","2","2","2","2","2","3","3","3","3","3","3","3","3","3","3","4","4","4","4","4","4","4","4","4","4","5","5","5","5","5","5","5","5","5","5","6","6","6","6","6","6","6","6","6","6","7","7","7","7","7","7","7","7","7","7","8","8","8","8","8","8","8","8","8","8","9","9","9","9","9","9","9","9","9","9","10","10","10","10","10","10","10","10","10","10"),
# vertex_name = c("1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10"),
# vertex_weight = c(0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0),
# initial_stress = c(1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1),
# final_stress = c(1,0,0,0,0,0,0,0,0,0,0.209346243433055,1,0,0,0.152617341163366,0.68961163156093,0,0.822020598469199,0,0,0,0,1,0,0,0,0,0,0,0,0.379358765808802,0,0.789706497495893,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0.303571218715092,0,0,0,0.221309116869039,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.221309116869039,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1),
# diff_stress = c(0,0,0,0,0,0,0,0,0,0,0.209346243433055,0,0,0,0.152617341163366,0.68961163156093,0,0.822020598469199,0,0,0,0,0,0,0,0,0,0,0,0,0.379358765808802,0,0.789706497495893,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0.303571218715092,0,0,0,0.221309116869039,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.221309116869039,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)),
#
# .Names = c(
# "stressed_vertex",
# "vertex_name",
# "vertex_weight",
# "initial_stress",
# "final_stress",
# "diff_stress"
# ),
#
# row.names = c(NA,-100L),
# class = "data.frame"
# )
# ),
# .Names = c("DebtRank",
# "StressLevel"),
# class = "DebtRank"
# )
#
# set.seed(1)
# n <- 10
# exposures <- abs(rcauchy(n*n))
# exposures[exposures < 10] <- 0
# dim(exposures) <- c(n,n)
#
# buffer <- rowSums(exposures) + rnorm(n, mean(exposures), sd(exposures)*2)
# size <- colSums(exposures)*10
#
# expect_warning(test <- debt_rank(exposures, buffer, size))
# expect_equal(test, res)
# expect_warning(test2 <- debt_rank(Matrix(exposures), buffer, size))
# expect_equal(test2, res)
# test
# plot(test)
#
# res2 <- structure(list(
# DebtRank = structure(list(
# additional_stress = 0.100681040489344,
# additional_defaults = 2L),
# .Names = c("additional_stress", "additional_defaults"),
# row.names = c(NA, -1L), class = "data.frame"),
# StressLevel = structure(list(
# vertex_name = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10"),
# vertex_weight = c(0, 0.47786067378296, 0, 0.231495669428445, 0.0850901856174138, 0.10797982474177, 0.0511679379216082, 0.0464057085078027, 0, 0),
# initial_stress = c(0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
# final_stress = c(0.841464992261947, 0.5, 0.894853248747946, 0.5, 0.672313006609779, 0.844805815780465, 1, 1, 0.5, 0.5),
# diff_stress = c(0.341464992261947, 0, 0.394853248747946, 0, 0.172313006609779, 0.344805815780465, 0.5, 0.5, 0, 0)),
# .Names = c("vertex_name", "vertex_weight", "initial_stress", "final_stress", "diff_stress"),
# row.names = c(NA, -10L), class = "data.frame")), .Names = c("DebtRank", "StressLevel"),
# class = "DebtRankShock")
#
# shock <- rep(0.5, n)
# expect_warning(test3 <- debt_rank_shock(exposures, buffer, size, shock))
# expect_equal(res2, test3)
#
# })
carloscinelli/NetworkRiskMeasures documentation built on March 7, 2020, 4:39 p.m.
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library(testthat)
context("Risk and Communicability Matrices")
test_that("vulnerability and communicability",
{
liabilities <- structure(c(0, 0, 0, 0, 0, 0,
5, 0, 0, 0, 0, 2,
0, 3, 0, 0, 2, 2,
0, 0, 4, 0, 5, 0,
10, 2, 0, 0, 5, 0,
2, 0, 0, 0, 0, 0),
.Dim = c(6L, 6L),
.Dimnames = list(c("A", "B", "C", "E", "F", "D"),
c("A", "B", "C", "E", "F", "D")))
assets <- t(liabilities)
capital_buffer <- c(A = 10, B = 2, C = 2, E = 1, F = 10, D = 4)
res <- structure(c(0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 1,
0, 1, 0, 0, 1, 1,
0, 0, 1, 0, 1, 0,
1, 0.2, 0, 0, 0.5, 0,
0.5, 0, 0, 0, 0, 0),
.Dim = c(6L, 6L),
.Dimnames = list(c("A", "B", "C", "E", "F", "D"),
c("A","B", "C", "E", "F", "D")))
v1 <- impact_matrix(exposures = liabilities,
buffer = capital_buffer,
binary = FALSE, exposure_type = "liabilities")
v1[v1 > 1] <- 1
expect_equal(v1, res)
# zero buffer, error
capital_buffer2 <- c(A = 0, B = 2, C = 0, E = 1, F = 10, D = 4)
expect_error(v1zero <- impact_matrix(exposures = liabilities,
buffer = capital_buffer2,
binary = FALSE, exposure_type = "liabilities"))
# na buffer, error
capital_buffer2 <- c(A = NA, B = 2, C = NA, E = 1, F = 10, D = 4)
expect_error(v1zero <- impact_matrix(exposures = liabilities,
buffer = capital_buffer2,
binary = FALSE, exposure_type = "liabilities"))
# wrong size buffer, error
capital_buffer2 <- c(A = 3, B = 2, C = 3, E = 1)
expect_error(v1zero <- impact_matrix(exposures = liabilities,
buffer = capital_buffer2,
binary = FALSE, exposure_type = "liabilities"))
v1 <- impact_matrix(exposures = liabilities,
buffer = capital_buffer,
binary = FALSE, exposure_type = "liabilities")
v1v <- vulnerability_matrix(exposures = liabilities,
buffer = capital_buffer,
binary = FALSE, exposure_type = "liabilities")
expect_equal(v1v, t(v1))
expect_warning(vulnerability_matrix(unname(liabilities), capital_buffer))
v2 <- impact_matrix(exposures = assets,
buffer = capital_buffer,
binary = FALSE,
exposure_type = "assets")
v2[v2 > 1] <- 1
expect_equal(v2, res)
v3 <- impact_matrix(exposures = liabilities,
buffer = capital_buffer,
binary = TRUE,
exposure_type = "liabilities")
expect_equal(v3, (res >= 1)*1)
v4 <- impact_matrix(exposures = assets,
buffer = capital_buffer,
binary = TRUE,
exposure_type = "assets")
expect_equal(v4, (res >= 1)*1)
res2 <- new("dgeMatrix",
x = c(1, 0, 0, 0, 0, 0, 1, 1,
0, 0, 0, 1, 1, 1, 1, 0, 1, 1.5, 0.833333333333333,
0.5, 1, 1, 1.5, 0.666666666666667, 1, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 1),
Dim = c(6L, 6L),
Dimnames = list(c("A", "B", "C", "E", "F", "D"),
c("A", "B", "C", "E", "F","D")),
factors = list())
## Testing igraph method only if package is installed
if (require(igraph, quietly = TRUE) && installed.packages()["igraph","Version"] > "1.0.0") {
g <- graph_from_adjacency_matrix(assets)
vi <- impact_matrix(exposures = g,
buffer = capital_buffer,
binary = FALSE,
exposure_type = "assets")
vi[vi > 1] <- 1
expect_equal(as.matrix(vi), res)
vib <- impact_matrix(exposures = g,
buffer = capital_buffer,
binary = TRUE,
exposure_type = "assets")
expect_equal(as.matrix(vib), (res >= 1)*1)
}
c1 <- communicability_matrix(v4)
expect_equal(c1, res2)
c2 <- communicability_matrix(Matrix(v4))
expect_equal(c1, res2)
c3 <- communicability_matrix(v4, terms = 10, sparse = F)
expect_equal(c3, as.matrix(res2))
## Testing igraph method only if package is installed
if (require(igraph, quietly = TRUE) && installed.packages()["igraph","Version"] > "1.0.0") {
g <- graph_from_adjacency_matrix(assets)
vib <- impact_matrix(exposures = g,
buffer = capital_buffer,
binary = TRUE,
exposure_type = "assets")
ci <- communicability_matrix(vib)
expect_equal(ci, res2)
}
binary <- vulnerability_matrix(exposures = liabilities,
buffer = capital_buffer,
binary = TRUE, exposure_type = "liabilities")
v1v <- (v1v >=1)*1
expect_equal(binary, v1v)
expect_error(vulnerability_matrix(exposures = liabilities,
buffer = "a"))
expect_error(vulnerability_matrix(exposures = liabilities,
buffer = 1, binary = 1))
}
)
# test_that("DebtRank - Random Data",
# {
# res <-
# structure(
# list(
# DebtRank = structure(
# list(
# stressed_vertex = c("1",
# "2", "3", "4", "5", "6", "7", "8", "9", "10"),
# vertex_weight = c(0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0),
# additional_stress = c(0,0.125596829283833,0,0,0.0464057085078027,0.0652369423410151,0,0.0188312338332124,0,0),
# additional_defaults = c(0L, 0L,0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L)),
#
# .Names = c(
# "stressed_vertex",
# "vertex_weight",
# "additional_stress",
# "additional_defaults"
# ),
#
# row.names = c(NA,-10L),
# class = "data.frame"
# ),
#
# StressLevel = structure(
# list(
# stressed_vertex = c("1","1","1","1","1","1","1","1","1","1","2","2","2","2","2","2","2","2","2","2","3","3","3","3","3","3","3","3","3","3","4","4","4","4","4","4","4","4","4","4","5","5","5","5","5","5","5","5","5","5","6","6","6","6","6","6","6","6","6","6","7","7","7","7","7","7","7","7","7","7","8","8","8","8","8","8","8","8","8","8","9","9","9","9","9","9","9","9","9","9","10","10","10","10","10","10","10","10","10","10"),
# vertex_name = c("1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10","1","2","3","4","5","6","7","8","9","10"),
# vertex_weight = c(0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0,0,0.47786067378296,0,0.231495669428445,0.0850901856174138,0.10797982474177,0.0511679379216082,0.0464057085078027,0,0),
# initial_stress = c(1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1),
# final_stress = c(1,0,0,0,0,0,0,0,0,0,0.209346243433055,1,0,0,0.152617341163366,0.68961163156093,0,0.822020598469199,0,0,0,0,1,0,0,0,0,0,0,0,0.379358765808802,0,0.789706497495893,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0.303571218715092,0,0,0,0.221309116869039,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.221309116869039,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1),
# diff_stress = c(0,0,0,0,0,0,0,0,0,0,0.209346243433055,0,0,0,0.152617341163366,0.68961163156093,0,0.822020598469199,0,0,0,0,0,0,0,0,0,0,0,0,0.379358765808802,0,0.789706497495893,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0.303571218715092,0,0,0,0.221309116869039,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.221309116869039,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)),
#
# .Names = c(
# "stressed_vertex",
# "vertex_name",
# "vertex_weight",
# "initial_stress",
# "final_stress",
# "diff_stress"
# ),
#
# row.names = c(NA,-100L),
# class = "data.frame"
# )
# ),
# .Names = c("DebtRank",
# "StressLevel"),
# class = "DebtRank"
# )
#
# set.seed(1)
# n <- 10
# exposures <- abs(rcauchy(n*n))
# exposures[exposures < 10] <- 0
# dim(exposures) <- c(n,n)
#
# buffer <- rowSums(exposures) + rnorm(n, mean(exposures), sd(exposures)*2)
# size <- colSums(exposures)*10
#
# expect_warning(test <- debt_rank(exposures, buffer, size))
# expect_equal(test, res)
# expect_warning(test2 <- debt_rank(Matrix(exposures), buffer, size))
# expect_equal(test2, res)
# test
# plot(test)
#
# res2 <- structure(list(
# DebtRank = structure(list(
# additional_stress = 0.100681040489344,
# additional_defaults = 2L),
# .Names = c("additional_stress", "additional_defaults"),
# row.names = c(NA, -1L), class = "data.frame"),
# StressLevel = structure(list(
# vertex_name = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10"),
# vertex_weight = c(0, 0.47786067378296, 0, 0.231495669428445, 0.0850901856174138, 0.10797982474177, 0.0511679379216082, 0.0464057085078027, 0, 0),
# initial_stress = c(0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
# final_stress = c(0.841464992261947, 0.5, 0.894853248747946, 0.5, 0.672313006609779, 0.844805815780465, 1, 1, 0.5, 0.5),
# diff_stress = c(0.341464992261947, 0, 0.394853248747946, 0, 0.172313006609779, 0.344805815780465, 0.5, 0.5, 0, 0)),
# .Names = c("vertex_name", "vertex_weight", "initial_stress", "final_stress", "diff_stress"),
# row.names = c(NA, -10L), class = "data.frame")), .Names = c("DebtRank", "StressLevel"),
# class = "DebtRankShock")
#
# shock <- rep(0.5, n)
# expect_warning(test3 <- debt_rank_shock(exposures, buffer, size, shock))
# expect_equal(res2, test3)
#
# })
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