tests/testthat/test_bayes_mvr_mix_centered_X.R

In stephenslab/mr.mash.alpha: Multiple Regression with Multivariate Adaptive Shrinkage

context("Test Bayesian multivariate regression with centered X")

test_that("bayes_mvr_mix and bayes_mvr_mix_centered_X return the same results", {
  ###Set seed
  set.seed(123)
  
  ###Simulate X and Y
  n  <- 100
  p <- 10
  
  ###Set residual covariance
  V  <- rbind(c(1.0,0.2),
              c(0.2,0.4))
  
  ###Set true effects
  B  <- matrix(c(-2, -2,
                 5, 5,
                 rep(0, (p-2)*2)), byrow=TRUE, ncol=2)
  
  ###Simulate X
  X <- matrix(rnorm(n*p), nrow=n, ncol=p)
  X <- scale(X, center=TRUE, scale=FALSE)
  
  ###Simulate Y from MN(XB, I_n, V) where I_n is an nxn identity matrix and V is the residual covariance
  Y <- sim_mvr(X, B, V)
  
  ###Specify the mixture weights and covariance matrices for the mixture-of-normals prior
  grid <- seq(1, 5)
  S0mix <- compute_cov_canonical(ncol(Y), singletons=TRUE, hetgrid=c(0, 0.25, 0.5, 0.75, 0.99), grid, zeromat=TRUE)
  S0mix <- lapply(S0mix, makePD, e=1e-8)
  
  w0    <- rep(1/(length(S0mix)), length(S0mix))
  
  ###Set eps
  eps <- .Machine$double.eps
  
  ###Fit BMR with untransformed X
  fit_mix <- bayes_mvr_mix(X[, 1], Y, V, w0, S0mix, eps)
  
  ###Fit BMR with transformed X
  comps1 <- precompute_quants(X, V, S0mix, standardize=FALSE, version="R")
  comps1$xtx <- colSums(X^2)
  fit_mix_centered <- bayes_mvr_mix_centered_X(X[, 1], Y, V, w0, S0mix, comps1$xtx[1], solve(V), comps1$V_chol, comps1$d, comps1$QtimesV_chol, eps)
  
  ###Tests
  expect_equal(fit_mix$mu1, fit_mix_centered$mu1, tolerance = 1e-10, scale = 1)
  expect_equal(fit_mix$S1, fit_mix_centered$S1, tolerance = 1e-10, scale = 1)
  expect_equal(fit_mix$w1, fit_mix_centered$w1, tolerance = 1e-10, scale = 1)
  expect_equal(fit_mix$logbf, fit_mix_centered$logbf, tolerance = 1e-10, scale = 1)
})