Nothing
# LUCID - three omics, binary outcome
test_that("check estimations of LUCID with binary outcome (K = 2,2,2)", {
i <- 1008
set.seed(i)
G <- matrix(rnorm(500), nrow = 100)
Z1 <- matrix(rnorm(1000),nrow = 100)
Z2 <- matrix(rnorm(1000), nrow = 100)
Z3 <- matrix(rnorm(1000), nrow = 100)
Z <- list(Z1 = Z1, Z2 = Z2, Z3 = Z3)
Y <- rbinom(n=100, size =1, prob =0.25)
# i <- sample(1:2000, 1)
# cat(paste("test1 - seed =", i, "\n"))
invisible(capture.output(fit1 <- estimate_lucid(G = G, Z = Z, Y = Y, K = c(2, 2, 2),
lucid_model = "parallel",
family = "binary",
seed = i,
useY = TRUE)))
betas <- fit1$res_Beta$Beta
beta1 <- mean(unlist(betas[1]))
beta2 <- mean(unlist(betas[2]))
beta3 <- mean(unlist(betas[3]))
mus <- fit1$res_Mu
mu1 <- mean(unlist(mus[1]))
mu2 <- mean(unlist(mus[2]))
mu3 <- mean(unlist(mus[3]))
sigma <- mean(unlist(fit1$res_Sigma))
Gamma <- mean(unlist(fit1$res_Gamma$Gamma))
# check parameters
expect_equal(beta1, 0.00, tolerance = 0.01)
expect_equal(beta2, 0.0719, tolerance = 0.01)
expect_equal(beta3, 0.0278, tolerance = 0.01)
expect_equal(mu1, -0.04, tolerance = 0.1)
expect_equal(mu2, -0.013, tolerance = 0.1)
expect_equal(mu3, -0.011, tolerance = 0.1)
expect_equal(sigma, 0.087, tolerance = 0.01)
expect_equal(Gamma, 0.63636, tolerance = 0.01)
expect_equal(class(fit1), "lucid_parallel")
})
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