Nothing
context("Alpha")
test_that("alpha = 1 is approximately correct", {
set.seed(15)
mat <- matrix(stats::rpois(n = 10000, lambda = 1000), ncol = 1)
true_esize <- 2
signal_fun <- function(n, true_esize) { rep(true_esize, n) }
pout <- poisthin(mat = mat, nsamp = nrow(mat), ngene = 1,
signal_fun = signal_fun,
signal_params = list(true_esize = true_esize),
prop_null = 0, alpha = 1)
sdhat <- apply(log2(mat + 1), 2, sd) / sqrt(nrow(mat))
ly <- log2(pout$Y + 1)
esize <- mean(ly[pout$X[, 2] == 0], na.rm = TRUE) - mean(ly[pout$X[, 2] == 1], na.rm = TRUE)
expect_equal(esize, true_esize * sdhat, tol = 0.001)
})
test_that("alpha = 0.5 is approximately correct", {
set.seed(15)
mat <- matrix(stats::rpois(n = 10000, lambda = 1000), ncol = 1)
true_esize <- 2
signal_fun <- function(n, true_esize) { rep(true_esize, n) }
pout <- poisthin(mat = mat, nsamp = nrow(mat), ngene = 1,
signal_fun = signal_fun,
signal_params = list(true_esize = true_esize),
prop_null = 0, alpha = 0.5)
sdhat <- apply(log2(mat + 1), 2, sd) / sqrt(nrow(mat)) ## sd of the mean
ly <- log2(pout$Y + 1)
esize <- mean(ly[pout$X[, 2] == 0], na.rm = TRUE) - mean(ly[pout$X[, 2] == 1], na.rm = TRUE)
expect_equal(esize, true_esize * sqrt(sdhat), tol = 0.001)
})
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