Nothing
###############################################################
# ------------------------------------------------------------#
# Tests for the random Sequence Generation #
# Does the Output have correct number of generated sequences? #
# K = 3 #
# ------------------------------------------------------------#
###############################################################
context("Correct number of generated sequences for K = 3")
test_that("Output has correct number of sequences (without seed) for K = 3.", {
# We test that the matrix of randomization sequences has the correct number of rows and
# thus coincides with the number of randomization sequences set earlier.
N <- sample(seq(3, 52, 3), 1) # Sample number of patients
r <- sample(30, 1) # Sample number of randomization sequences
mti <- sample(ceiling(N/2), 1) # Sample maximum tolerated imbalance
p <- sample(seq(0.5001, 1, 0.05), 1) # Sample biased coin parameter
nr <- sample(8, 1) # Sample number of blocks
blocks <- sample(seq(3, 24, 3), nr) # Sample blocks
# 1. Test for complete randomization
output1 <- genSeq(crPar(N = N, K = 3), r = r) # most probably more than one sequence
expect_equal(nrow(getRandList(output1)), r)
# 2. Test for Random Allocation Rule
output1 <- genSeq(rarPar(N = N, K = 3), r = r)
expect_equal(nrow(getRandList(output1)), r)
# 3. Test for Permuted Block Randomization
# note that N is not needed here
output1 <- genSeq(pbrPar(bc = blocks, K = 3), r = r) # no seed
expect_equal(nrow(getRandList(output1)), r)
output2 <- genSeq(rpbrPar(rb = blocks, N = N, K = 3), r = r)
expect_equal(nrow(getRandList(output2)), r)
})
test_that("Output has correct number of sequences (with seed) for K = 3.", {
# We test that the matrix of randomization sequences has the correct number of rows
# and thus coincides with the number randomization sequences set earlier.
N <- sample(seq(3, 52, 3), 1) # Sample number of patients
r <- sample(30, 1) # Sample number of randomization sequences
mti <- sample(ceiling(N/2), 1) # Sample maximum tolerated imbalance
p <- sample(seq(0.5001, 1, 0.05), 1) # Sample biased coin parameter
nr <- sample(8, 1) # Sample number of blocks
blocks <- sample(seq(3, 24, 3), nr) # Sample blocks
seed <- sample(.Machine$integer.max, 1) # Sample seed
# 1. Test for complete randomization
output1 <- genSeq(crPar(N = N, K = 3), r = r, seed = seed)
expect_equal(nrow(getRandList(output1)), r)
# 2. Test for Random Allocation Rule
output1 <- genSeq(rarPar(N = N, K = 3), r = r, seed = seed)
expect_equal(nrow(getRandList(output1)), r)
# 3. Test for Permuted Block Randomization
# note that N is not needed here
output1 <- genSeq(pbrPar(bc = blocks, K = 3), r = r, seed = seed)
expect_equal(nrow(getRandList(output1)), r)
output2 <- genSeq(rpbrPar(rb = blocks, N = N, K = 3), r = r, seed = seed)
expect_equal(nrow(getRandList(output2)), r)
})
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