Nothing
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# ----------------------------------------------------------------------------#
# Test whether empirical and and theoretical rejection probability coincide #
# --------------------------------------------------------------------------- #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
context("Emprical vs theoretical rejection probability for the class selBias")
test_that("the empirical and theoretical frequency coincide for the class selbias", {
# Number of patients
N <- 4
# Number of generated sequences
r <- 10000
# = 1 - confidence level for simulations
alpha <- 0.01
# type-I-error probability of the t.test
toe <- runif(1, min = 0.1, max = 0.15)
# variance of the endpoint
sigma <- runif(1, min = 0.5, max = 5)
# magnitude of selection bias
eta <- runif(1, max = 4)
# type of selection bias
type <- sample(c("CS", "DS"), 1)
endp <- normEndp(mu = c(runif(1, max = 3), runif(1, max = 3)), sigma = c(sigma, sigma))
selBiasEx <- selBias(type = type, eta = eta, method = "exact", alpha = toe)
selBiasSim <- selBias(type = type, eta = eta, method = "sim", alpha = toe)
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# 1. Test for the average rejection probability of selection bias for cr
# true rejection probability
rejProbEx <- summary(assess(getAllSeq(crPar(N)), selBiasEx, endp = endp))[1,1]
# simualted rejection probability
rejProbSim <- summary(assess(genSeq(crPar(N), r), selBiasSim, endp = endp))[1,1]
tol <- qnorm(1-alpha/2)*sqrt(rejProbSim*(1-rejProbSim)/r)
expect_true(abs(rejProbEx - rejProbSim) < tol)
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# 2. Test for the average rejection probability of selection bias for a given sequence
# true rejection probability of a sequence
RS <- getAllSeq(crPar(N))
# sample sequence
RRS <- sample(nrow(RS@M), 1)
# true rejection probability of a sequence
rejProbEx <- assess(RS, selBiasEx, endp = endp)@D[RRS, 3]
# simulated rejection probability of a sequence
simRS <- genSeq(crPar(N), r)
simRS@M <- matrix(rep(RS@M[RRS, ], r), nrow = r, byrow = T)
rejProbSim <- summary(assess(simRS, selBiasSim, endp = endp))[1, 1]
tol <- qnorm(1-alpha/2)*sqrt(rejProbSim*(1-rejProbSim)/r)
expect_true(abs(rejProbEx - rejProbSim) < tol)
}
)
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