Nothing
# LUCID - LUCID in Serial, binary outcome
test_that("check estimations of LUCID in Serial with binary outcome (K = 2,2,2,2,2)", {
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)
##missing data
a = sample(1:1000, 30, replace=FALSE)
Z1[a] = NA
Z2[62:65, 6:8] = NA
a = sample(1:1000, 30, replace=FALSE)
Z4[a] = NA
Z <- list(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"))
##needs work!!!!!#####
##needs work!!!!!#####
##needs work!!!!!#####
invisible(capture.output(fit1 <- estimate_lucid(G = G, Z = Z, Y = Y, K = list(list(2,2,2),2,2),
lucid_model = "serial",
family = "binary",
init_omic.data.model = "VVV",
CoG = CoG, CoY = CoY,
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$res_Gamma))
# check parameters
expect_equal(betas, -0.039, tolerance = 0.01)
expect_equal(mus, 0.0115, tolerance = 0.01)
expect_equal(sigma, 0.08, tolerance = 0.01)
expect_equal(Gamma, -0.1857, tolerance = 0.01)
expect_equal(class(fit1), "lucid_serial")
Z <- list(Z1 = Z1, list(Z2 = Z2, Z3 = Z3), Z4 = Z4, Z5 = Z5)
invisible(capture.output(fit2 <- estimate_lucid(G = G, Z = Z, Y = Y, K = list(2,list(2,2),2,2),
CoG = CoG, CoY = CoY,
lucid_model = "serial",
family = "binary",
init_omic.data.model = "VVV",
seed = i,
useY = TRUE)))
betas <- mean(unlist(fit2$res_Beta))
mus <- mean(unlist(fit2$res_Mu))
sigma <- mean(unlist(fit2$res_Sigma))
Gamma <- mean(unlist(fit2$res_Gamma))
# check parameters
expect_equal(betas, 0.0865, tolerance = 0.01)
expect_equal(mus, 0.01195, tolerance = 0.01)
expect_equal(sigma, 0.0803, tolerance = 0.01)
expect_equal(Gamma, -0.1856, tolerance = 0.01)
expect_equal(class(fit2), "lucid_serial")
})
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