tests/testthat/test-predict-binary-5-layers.R

In LUCIDus: LUCID with Multiple Omics Data

# 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 =0.45)

  #dont use Cog Coy here

  invisible(capture.output(fit1 <- est_lucid(G = G, Z = Z, Y = Y, K = c(2, 2, 2, 2, 2),
                                             lucid_model = "parallel",
                                             family = "binary",

                                             seed = i,
                                             useY = TRUE)))
  set.seed(i+1000)
  n_G <- matrix(rnorm(500), nrow = 100)
  n_Z1 <- matrix(rnorm(1000),nrow = 100)
  n_Z2 <- matrix(rnorm(1000), nrow = 100)
  n_Z3 <- matrix(rnorm(1000), nrow = 100)
  n_Z4 <- matrix(rnorm(1000), nrow = 100)
  n_Z5 <- matrix(rnorm(1000), nrow = 100)
  n_Z <- list(Z1 = n_Z1, Z2 = n_Z2, Z3 = n_Z3, Z4 = n_Z4, Z5 = n_Z5)
  n_Y <- rbinom(n=100, size =1, prob =0.45)

  #use training data
  pred1 <- predict_lucid(model = fit1,
                         lucid_model = "parallel",
                         G = G,
                         Z = Z,
                         Y = Y,
                         response = TRUE)

  expect_equal(fit1$inclusion.p, pred1$inclusion.p, tolerance = 0.05)
  expect_equal(class(pred1$pred.x), "list")
  expect_equal(max(pred1$pred.y), 1)
  expect_equal(mean(pred1$pred.y), 0.28)
  expect_equal(mean(pred1$inclusion.p[[1]]), 0.5)

  #use new data
  pred2 <- predict_lucid(model = fit1,
                         lucid_model = "parallel",
                         G = n_G,
                         Z = n_Z,
                         Y = n_Y,
                         response = TRUE)

  expect_equal(class(pred2$pred.x), "list")
  expect_equal(max(pred2$pred.y), 1)
  expect_equal(mean(pred2$pred.y), 0.28)
  expect_equal(mean(pred2$inclusion.p[[1]]), 0.5)

  #new data not using Y, and response = FALSE
  pred3 <- predict_lucid(model = fit1,
                         lucid_model = "parallel",
                         G = n_G,
                         Z = n_Z,
                         Y = NULL,
                         response = FALSE)

  expect_equal(class(pred3$pred.x), "list")
  expect_equal(max(pred3$pred.y), 0.7, tolerance = 0.05)
  expect_equal(mean(pred3$pred.y), 0.42, tolerance = 0.05)
  expect_equal(mean(pred3$inclusion.p[[1]]), 0.5)

})