Nothing
###############################################################
# ------------------------------------------------------------#
# Tests for the random Sequence Generation #
# Does the Output have correct number of generated sequences? #
# ------------------------------------------------------------#
###############################################################
context("Correct number of generated sequences")
test_that("Output has correct number of sequences (without seed).", {
# 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(2, 50, 2), 1) # Sample number of patients
r <- sample(30, 1) # Sample number of randomization sequences
mti <- sample(N/2, 1) # Sample maximum tolerated imbalance
p <- sample(seq(0.5001, 1, 0.05), 1) # Sample biased coin parameter
nr <- sample(10,1) # Sample number of blocks
blocks <- sample(seq(2, 20, 2), nr) # Sample blocks
gamma <- sample(50, 1) # Sample parameter for bbcd
a <- sample(50, 1) # Sample parameter for abcd
rho <- sample(50, 1) # Sample parameter for gbcd
# 1. Test for complete randomization
output1 <- genSeq(crPar(N = N), 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), 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), r = r) # no seed
expect_equal(nrow(getRandList(output1)), r)
output2 <- genSeq(rpbrPar(rb = blocks, N = N), r = r)
expect_equal(nrow(getRandList(output2)), r)
# 4. Test for Efron's Biased Coin Desgin
output1 <- genSeq(ebcPar(N = N, p = p), r = r)
expect_equal(nrow(getRandList(output1)), r)
# 5. Test for Big Stick Design
output1 <- genSeq(bsdPar(N = N, mti = mti), r = r)
expect_equal(nrow(getRandList(output1)), r)
# 6. Test for Maximal Procedure
output1 <- genSeq(mpPar(N = N, mti = mti), r = r)
expect_equal(nrow(getRandList(output1)), r)
# 7. Test for Truncated Binomial Design
output1 <- genSeq(tbdPar(bc = blocks), r = r)
expect_equal(nrow(getRandList(output1)), r)
output2 <- genSeq(rtbdPar(N = N, rb = blocks), r = r)
expect_equal(nrow(getRandList(output2)), r)
# 8. Test for Urn Design
ini <- sample(seq(2, 20, 2), 1) # Sample initial urn composition
add <- sample(seq(2, 20, 2), 1) # Sample number of balls that are added to urn each step
output1 <- genSeq(udPar(N = N, ini = ini, add = add), r = r) # no seed
expect_equal(nrow(getRandList(output1)), r)
# 9. Test for Hadamard Randomization
output1 <- genSeq(hadaPar(N = N), r = r)
expect_equal(nrow(getRandList(output1)), r)
# 10. Test for Generalized Biased Coin Design
output1 <- genSeq(gbcdPar(N, rho), r = r)
expect_equal(nrow(getRandList(output1)), r)
# 11. Test for Adjustable Biased Coin Design
output1 <- genSeq(abcdPar(N, a), r = r)
expect_equal(nrow(getRandList(output1)), r)
# 12. Test for Bayesian Biased Coin Design
output1 <- genSeq(bbcdPar(N, gamma), r = r)
expect_equal(nrow(getRandList(output1)), r)
})
test_that("Output has correct number of sequences (with seed)", {
# 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(2, 50, 2), 1) # Sample number of patients
r <- sample(30, 1) # Sample number of randomization sequences
mti <- sample(N/2, 1) # Sample maximum tolerated imbalance
p <- sample(seq(0.5001, 1, 0.05), 1) # biased coin parameter
seed <- sample(.Machine$integer.max, 1) # Sample seed
nr <- sample(10,1) # sample number of blocks
blocks <- sample(seq(2, 20, 2), nr) # sample blocks
gamma <- sample(50, 1) # Sample parameter for bbcd
a <- sample(50, 1) # Sample parameter for abcd
rho <- sample(50, 1) # Sample parameter for gbcd
# 1. Test for complete randomization
output1 <- genSeq(crPar(N = N), r = r, seed = seed)
expect_equal(nrow(getRandList(output1)), r)
# 2. Test for Random Allocation Rule
output1 <- genSeq(rarPar(N = N), 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), r = r, seed = seed)
expect_equal(nrow(getRandList(output1)), r)
output2 <- genSeq(rpbrPar(rb = blocks, N = N), r = r, seed = seed)
expect_equal(nrow(getRandList(output2)), r)
# 4. Test for Efron's Biased Coin Desgin
output1 <- genSeq(ebcPar(N = N, p = p), r = r, seed = seed)
expect_equal(nrow(getRandList(output1)), r)
# 5. Test for Big Stick Design
output1 <- genSeq(bsdPar(N = N, mti = mti), r = r, seed = seed)
expect_equal(nrow(getRandList(output1)), r)
# 6. Test for Maximal Procedure
output1 <- genSeq(mpPar(N = N, mti = mti), r = r, seed = seed)
expect_equal(nrow(getRandList(output1)), r)
# 7. Test for Truncated Binomial Design
output1 <- genSeq(tbdPar(bc = N), r = r, seed = seed)
expect_equal(nrow(getRandList(output1)), r)
output2 <- genSeq(rtbdPar(N = N, rb = blocks), r = r, seed = seed)
expect_equal(nrow(getRandList(output2)), r)
# 8. Test for Urn Design
ini <- sample(seq(2, 20, 2), 1) # Sample initial urn composition
add <- sample(seq(2, 20, 2), 1) # Sample number of balls that are added to urn each step
output1 <- genSeq(udPar(N = N, ini = ini, add = add), r = r, seed = seed)
expect_equal(nrow(getRandList(output1)), r)
# 9. Test for Hadamard Randomization
output1 <- genSeq(hadaPar(N = N), r = r, seed = seed)
expect_equal(nrow(getRandList(output1)), r)
# 10. Test for Generalized Biased Coin Design
output1 <- genSeq(gbcdPar(N, rho), r = r, seed = seed)
expect_equal(nrow(getRandList(output1)), r)
# 11. Test for Adjustable Biased Coin Design
output1 <- genSeq(abcdPar(N, a), r = r, seed = seed)
expect_equal(nrow(getRandList(output1)), r)
# 12. Test for Bayesian Biased Coin Design
output1 <- genSeq(bbcdPar(N, gamma), r = r, seed = seed)
expect_equal(nrow(getRandList(output1)), r)
})
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