Nothing
context('data: GE, GV, CPh')
test_that('mhEdge infers the correct graph with clinical phenotypes present',{
# Adjacency matrix for M1 with one clinical phenotype ------------------------
# Fully connected adjacency matrix for M1 with one clinical phenotype
am_m1_cph <- matrix(c(0, 1, 1, 1,
0, 0, 1, 1,
0, 0, 0, 1,
0, 0, 0, 0),
byrow = TRUE,
nrow = 4)
# Simulate data for M1_CPh -------------------------------------------------
set.seed(22)
data_m1_cph <- simdata(b0 = 0,
N = 500,
s = 1,
ss = 1,
q = 0.1,
p = 0.6,
graph = 'm1_cph')
# Run baycn on M1_CPh -- -----------------------------------------------------
baycn_m1_cph <- mhEdge(adjMatrix = am_m1_cph,
burnIn = 0.2,
data = data_m1_cph,
iterations = 1000,
nGV = 1,
nCPh = 1,
pmr = TRUE,
prior = c(0.05,
0.05,
0.9),
progress = FALSE,
thinTo = 500)
# Calculate the MSE for M1_CPh -----------------------------------------------
# Expected probabilities for M1_CPh when using PMR and one clinical phenotype.
ep_m1_cph <- matrix(c(1, 0, 0,
0, 0, 1,
0, 0, 1,
1, 0, 0,
1, 0, 0,
0, 0, 1),
byrow = TRUE,
ncol = 3)
mse_m1_cph <- sum((baycn_m1_cph@posteriorES[, 2:4] - ep_m1_cph)^2)
expect_true(mse_m1_cph < 0.1)
})
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