Nothing
# LUCID - three omics, normal outcome
test_that("check estimations of LUCID with normal outcome (K = 2,2,2) with missing data", {
# run LUCID model
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, Z2 = Z3)
CoY <- matrix(rnorm(200), nrow = 100)
CoG <- matrix(rnorm(200), nrow = 100)
Y <- rnorm(100)
invisible(capture.output(fit1 <- estimate_lucid(G = G, Z = Z, Y = Y, K = c(2, 2, 2), CoG = CoG, CoY = CoY,
lucid_model = "parallel",
family = "normal",
init_omic.data.model = "VVV",
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, 0.100, tolerance = 0.01)
expect_equal(beta2, -0.236, tolerance = 0.01)
expect_equal(beta3, -0.0256, tolerance = 0.01)
expect_equal(mu1, -0.042, tolerance = 0.01)
expect_equal(mu2, 0.1119, tolerance = 0.01)
expect_equal(mu3, -0.01587, tolerance = 0.01)
expect_equal(sigma, 0.07487, tolerance = 0.01)
expect_equal(Gamma, 0.6765, tolerance = 0.01)
expect_equal(class(fit1), "lucid_parallel")
# missing data
set.seed(i)
G <- matrix(rnorm(500), nrow = 100)
Z1 <- matrix(rnorm(1000),nrow = 100)
a = sample(1:1000, 30, replace=FALSE)
Z1[a] = NA
Z2 <- matrix(rnorm(1000), nrow = 100)
Z2[62:65, 6:8] = NA
Z3 <- matrix(rnorm(1000), nrow = 100)
Z <- list(Z1 = Z1, Z2 = Z2, Z2 = Z3)
CoY <- matrix(rnorm(200), nrow = 100)
CoG <- matrix(rnorm(200), nrow = 100)
Y <- rnorm(100)
invisible(capture.output(fit1 <- estimate_lucid(G = G, Z = Z, Y = Y, K = c(2, 2, 2), CoG = CoG, CoY = CoY,
lucid_model = "parallel",
family = "normal",
init_omic.data.model = "VVV",
seed = i,
useY = TRUE,
init_impute = "mix")))
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, 0.1232, tolerance = 0.01)
expect_equal(beta2, 0.37066, tolerance = 0.01)
expect_equal(beta3, -0.2164, tolerance = 0.01)
expect_equal(mu1, -0.0394, tolerance = 0.01)
expect_equal(mu2, 0.0989, tolerance = 0.01)
expect_equal(mu3, 0.01258, tolerance = 0.01)
expect_equal(sigma, 0.07635, tolerance = 0.01)
expect_equal(Gamma, 0.7024, tolerance = 0.01)
expect_equal(class(fit1), "lucid_parallel")
})
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.