context("Methods")
test_that("OWL executes the do.call instruction", {
n_samples <- 200
p <- 20
X <- matrix((runif(n_samples * p, min = 0, max = 1) < runif(n_samples *
p, min = 0, max = 1)) + (runif(n_samples * p, min = 0, max = 1) <
runif(n_samples * p, min = 0, max = 1)), ncol = p, nrow = n_samples)
A <- (runif(n_samples, min = 0, max = 1) < 0.5)
propensity <- runif(n_samples, min = 0.4, max = 0.8)
Y <- (runif(n_samples, min = 0, max = 1) < 0.5)
expect_length(OWL(A, X, Y, propensity), p)
expect_error(OWL(A, X, Y, propensity, family = "gaussian"))
})
test_that("do.call is instructed for stabilityGLM", {
stability_mode <- FALSE
n_samples <- 200
p <- 20
X <- matrix((runif(n_samples * p, min = 0, max = 1) < runif(n_samples *
p, min = 0, max = 1)) + (runif(n_samples * p, min = 0, max = 1) <
runif(n_samples * p, min = 0, max = 1)), ncol = p, nrow = n_samples)
A <- (runif(n_samples, min = 0, max = 1) < 0.5)
propensity <- runif(n_samples, min = 0.4, max = 0.8)
Y <- (runif(n_samples, min = 0, max = 1) < 0.5)
expect_length(modified_outcome(A, X, Y, propensity,
parallel = stability_mode,
n_subsample = 20, n_lambda = 100
), p)
expect_length(shifted_outcome(A, X, Y, propensity,
shift = 0.2, parallel = stability_mode,
n_subsample = 20, n_lambda = 100
), p)
expect_length(normalized_outcome(A, X, Y, propensity,
parallel = stability_mode,
n_subsample = 20, n_lambda = 100
), p)
expect_length(robust_outcome(A, X, Y, propensity,
parallel = stability_mode,
n_subsample = 20, n_lambda = 100
), p)
})
test_that("do.call is instructed for stabilityBIG", {
stability_mode <- TRUE
n_samples <- 200
p <- 20
X <- matrix((runif(n_samples * p, min = 0, max = 1) < runif(n_samples *
p, min = 0, max = 1)) + (runif(n_samples * p, min = 0, max = 1) <
runif(n_samples * p, min = 0, max = 1)), ncol = p, nrow = n_samples)
A <- (runif(n_samples, min = 0, max = 1) < 0.5)
propensity <- runif(n_samples, min = 0.4, max = 0.8)
Y <- (runif(n_samples, min = 0, max = 1) < 0.5)
expect_length(modified_outcome(A, X, Y, propensity,
parallel = stability_mode,
ncores = 2, n_subsample = 20, n_lambda = 100
), p)
expect_length(normalized_outcome(A, X, Y, propensity,
parallel = stability_mode,
ncores = 2, n_subsample = 20, n_lambda = 100
), p)
expect_length(shifted_outcome(A, X, Y, propensity,
shift = 0.2, parallel = stability_mode,
ncores = 2, n_subsample = 20, n_lambda = 100
), p)
expect_length(robust_outcome(A, X, Y, propensity,
parallel = stability_mode,
ncores = 2, n_subsample = 20, n_lambda = 100
), p)
})
test_that("jointly calling all methods is possible with epiGWAS", {
n_samples <- 200
p <- 20
X <- matrix(
(runif(n_samples * p, min = 0, max = 1) <
runif(n_samples * p, min = 0, max = 1)) +
(runif(n_samples * p, min = 0, max = 1) <
runif(n_samples * p, min = 0, max = 1)),
ncol = p, nrow = n_samples
)
A <- (runif(n_samples, min = 0, max = 1) < 0.5)
propensity <- runif(n_samples, min = 0.4, max = 0.8)
Y <- (runif(n_samples, min = 0, max = 1) < 0.5)
epiGWAS_aucs <- epiGWAS(A, X, Y, propensity,
methods = "all",
parallel = TRUE, shift = 0.1,
ncores = 2, n_subsample = 20
)
expect_equal(names(epiGWAS_aucs), c(
"OWL", "modified_outcome", "normalized_outcome",
"shifted_outcome", "robust_outcome"
))
})
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