context("test_susie.R")
# test_that("susie agrees with version 0.3", with(simulate(sparse=T), {
# original.res = readRDS('susiefit_original_res.rds')
# original.res2 = readRDS('susiefit_original_res2.rds')
# original.res3 = readRDS('susiefit_original_res3.rds')
# original.res4 = readRDS('susiefit_original_res4.rds')
#
# original.res$Xr = as.vector(original.res$Xr)
# original.res2$Xr = as.vector(original.res2$Xr)
# original.res3$Xr = as.vector(original.res3$Xr)
# original.res4$Xr = as.vector(original.res4$Xr)
# dense.res = susie(X, y, tol=1E-2, estimate_prior_variance = FALSE)
# sparse.res = susie(X.sparse, y, tol=1E-2, estimate_prior_variance = FALSE)
#
# dense.res2 = susie(X, y, standardize=TRUE, intercept = FALSE, tol=1E-2, estimate_prior_variance = FALSE)
# sparse.res2 = susie(X.sparse, y, standardize=TRUE, intercept = FALSE,
# tol=1E-2, estimate_prior_variance = FALSE)
#
# dense.res3 = susie(X, y, standardize=FALSE, intercept = TRUE, tol=1E-2, estimate_prior_variance = FALSE)
# sparse.res3 = susie(X.sparse, y, standardize=FALSE, intercept = TRUE,
# tol=1E-2, estimate_prior_variance = FALSE)
#
# dense.res4 = susie(X, y, standardize=FALSE, intercept = FALSE, tol=1E-2, estimate_prior_variance = FALSE)
# sparse.res4 = susie(X.sparse, y, standardize=FALSE, intercept = FALSE,
# tol=1E-2, estimate_prior_variance = FALSE)
# expect_equal_susie(sparse.res, original.res)
# expect_equal_susie(dense.res, original.res)
# expect_equal_susie(sparse.res2, original.res2)
# expect_equal_susie(dense.res2, original.res2)
# expect_equal_susie(sparse.res3, original.res3)
# expect_equal_susie(dense.res3, original.res3)
# expect_equal_susie(sparse.res4, original.res4)
# expect_equal_susie(dense.res4, original.res4)
# }))
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