Nothing
# LUCID - 1 omics, binary outcome
test_that("check estimations of LUCID with binary outcome (K = 2)", {
G <- sim_data$G[1:500, ]
Z <- sim_data$Z[1:500, ]
Y <- sim_data$Y_binary[1:500, ]
cov <- sim_data$Covariate[1:500, ]
# i <- sample(1:2000, 1)
i <- 1008
# i <- sample(1:2000, 1)
# cat(paste("test1 - seed =", i, "\n"))
invisible(capture.output(fit1 <- estimate_lucid(G = G, Z = Z, Y = Y, K = 2,
lucid_model = "early",
family = "binary",
CoY = cov,
init_omic.data.model = "VVV",
seed = i,
useY = TRUE)))
pars <- fit1
beta_causal <- mean(pars$res_Beta[2, 2:5])
beta_non <- mean(pars$res_Beta[2, 6:10])
mu_causal <- mean(abs(pars$res_Mu[1, 1:5] - pars$res_Mu[2, 1:5]))
mu_non <- mean(abs(pars$res_Mu[1, 6:10] - pars$res_Mu[2, 6:10]))
gamma <- as.numeric(pars$res_Gamma$beta)
# check parameters
expect_equal(beta_causal, log(2), tolerance = 0.2)
expect_equal(beta_non, 0, tolerance = 0.1)
expect_equal(mu_causal, 2, tolerance = 0.1)
expect_equal(mu_non, 0, tolerance = 0.1)
expect_equal(gamma, c(-0.5, 0.9, 0.8, -0.8), tolerance = 0.2)
# check summary_lucid
sum_fit1 <- summary_lucid(fit1)
expect_equal(class(fit1), "early_lucid")
expect_equal(class(sum_fit1), "sumlucid_early")
})
test_that("check variable selection on G", {
# run LUCID model
G <- sim_data$G[1:500, ]
Z <- sim_data$Z[1:500, ]
Y_binary <- sim_data$Y_binary[1:500, ]
cov <- sim_data$Covariate[1:500, ]
# i <- sample(1:2000, 1)
# cat(paste("test2 - seed =", i, "\n"))
i <- 1008
invisible(capture.output(fit1 <- estimate_lucid(G = G,
Z = Z,
Y = Y_binary,
CoY = cov,
family = "binary",
lucid_model = "early",
K = 2,
seed = i,
useY = TRUE
)))
# make prediction
pred1 <- predict_lucid(model = fit1,
lucid_model = "early",
G = G,
Z = Z,
Y = Y_binary,
CoY = cov)
# compare prediction of X
expect_equal(fit1$inclusion.p, pred1$inclusion.p, tolerance = 0.05)
expect_equal(class(pred1$pred.x), "integer")
pred2 <- predict_lucid(model = fit1,
lucid_model = "early",
G = G,
Z = Z,
Y = NULL,
CoY = cov)
expect_equal(class(pred2$pred.x), "integer")
})
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