Nothing
# LUCID - five 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)
Z4 <- matrix(rnorm(1000), nrow = 100)
Z5 <- matrix(rnorm(1000), nrow = 100)
Z <- list(Z1 = Z1, Z2 = Z2, Z3 = Z3, Z4 = Z4, Z5 = Z5)
Y <- rbinom(n=100, size =1, prob =0.25)
CoY <- matrix(rnorm(200), nrow = 100)
CoG <- matrix(rnorm(200), nrow = 100)
# 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, 2, 2),
lucid_model = "parallel",
family = "binary",
seed = i,
useY = TRUE)))
betas <- mean(unlist(fit1$res_Beta$Beta))
mus <- mean(unlist(fit1$res_Mu))
sigma <- mean(unlist(fit1$res_Sigma))
Gamma <- mean(unlist(fit1$res_Gamma$Gamma))
# check parameters
expect_equal(betas, 0.03, tolerance = 0.05)
expect_equal(mus, 0.02005, tolerance = 0.05)
expect_equal(sigma, 0.09, tolerance = 0.05)
expect_equal(Gamma, 0.297, tolerance = 0.05)
expect_equal(class(fit1), "lucid_parallel")
invisible(capture.output(fit2 <- estimate_lucid(G = G, Z = Z, Y = Y, K = c(2, 2, 2, 2, 2),
CoG = CoG, CoY = CoY,
lucid_model = "parallel",
family = "binary",
seed = i,
useY = TRUE)))
betas <- mean(unlist(fit2$res_Beta$Beta))
mus <- mean(unlist(fit2$res_Mu))
sigma <- mean(unlist(fit2$res_Sigma))
Gamma <- mean(unlist(fit2$res_Gamma$Gamma))
# check parameters
expect_equal(betas, -0.0437, tolerance = 0.05)
expect_equal(mus, 0.02005, tolerance = 0.05)
expect_equal(sigma, 0.09, tolerance = 0.05)
expect_equal(Gamma, 0.2973, tolerance = 0.05)
})
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