tests/testthat/test-lucid-normal.R

In Yinqi93/LUCIDus: Latent Unknown Clustering Integrating Multi-view Data

# LUCID - single omics, normal outcome


test_that("check estimations of LUCID with normal outcome (K = 2)", {
  # run LUCID model
  G <- sim_data$G[1:200, ]
  Z <- sim_data$Z[1:200, ]
  Y_normal <- sim_data$Y_normal[1:200, ]
  cov <- sim_data$Covariate[1:200, ]
  X <- sim_data$X[1:200]
  # i <- sample(1:2000, 1)
  i <- 1008
  # cat(paste("test1 - seed =", i, "\n"))
  invisible(capture.output(fit1 <- lucid(G = G,
                                             Z = Z,
                                             Y = Y_normal,
                                             CoY = cov,
                                             family = "normal",
                                             K = 2,
                                             seed = i,
                                             modelName = "VVV")))
  pars <- fit1$pars
  beta_causal <- mean(pars$beta[2, 2:5])
  beta_non <- mean(pars$beta[2, 6:10])
  mu_causal <- mean(abs(pars$mu[1, 1:5] - pars$mu[2, 1:5]))
  mu_non <- mean(abs(pars$mu[1, 6:10] - pars$mu[2, 6:10]))
  gamma_causal <- as.numeric(abs(pars$gamma$beta[1] - pars$gamma$beta[2]))
  gamma_non <- as.numeric(mean(pars$gamma$beta[3:4]))
  sigma <- mean(pars$gamma$sigma)
  
  # 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_causal, 1, tolerance = 0.05)
  expect_equal(gamma_non, 0, tolerance = 0.05)
  expect_equal(sigma, 1, tolerance = 0.05)
  
  # check summary_lucid
  sum_fit1 <- summary_lucid(fit1)
  expect_equal(class(fit1), "lucid")
  expect_equal(class(sum_fit1), "sumlucid")
})


test_that("check variable selection on G", {
  # run LUCID model
  G <- sim_data$G[1:200, ]
  Z <- sim_data$Z[1:200, ]
  Y_normal <- sim_data$Y_normal[1:200, ]
  cov <- sim_data$Covariate[1:200, ]
  X <- sim_data$X[1:200]
  # i <- sample(1:2000, 1)
  i <- 1008
  # cat(paste("test2 - seed =", i, "\n"))
  invisible(capture.output(fit1 <- lucid(G = G,
                                             Z = Z,
                                             Y = Y_normal,
                                             CoY = cov,
                                             family = "normal",
                                             K = 2,
                                             seed = i,
                                             modelName = "VVV",
                                             Rho_G = 0.05)))
  
  # check parameters
  expect_equal(class(fit1$select$selectG), "logical")
  expect_equal(as.vector(fit1$select$selectG), 
               rep(TRUE, 4))
})


test_that("check variable selection on Z", {
  # run LUCID model
  G <- sim_data$G[1:200, ]
  Z <- sim_data$Z[1:200, ]
  Y_normal <- sim_data$Y_normal[1:200, ]
  cov <- sim_data$Covariate[1:200, ]
  X <- sim_data$X[1:200]
  # i <- sample(1:2000, 1)
  i <- 1008
  # cat(paste("test3 - seed =", i, "\n"))
  invisible(capture.output(fit1 <- lucid(G = G,
                                             Z = Z,
                                             Y = Y_normal,
                                             CoY = cov,
                                             family = "normal",
                                             K = 2,
                                             seed = i,
                                             modelName = "VVV",
                                             init_par = "random",
                                             Rho_Z_Mu = 13,
                                             Rho_Z_Cov = 0.05)))
  
  # check parameters
  expect_equal(class(fit1$select$selectG), "logical")
})