Nothing
context("Test integral scores")
test_that("Expected sample size is computed correctly",{
null <- PointMassPrior(.0, 1)
alternative <- PointMassPrior(.4, 1)
dist <- Normal()
# Define design from rpact
design_rp2 <- rpact::getDesignInverseNormal(
kMax = 2, alpha = 0.05, beta = 0.2, futilityBounds = 0, typeOfDesign = "P")
res <- rpact::getSampleSizeMeans(
design_rp2, normalApproximation = TRUE, alternative = .4)
char <- rpact::getDesignCharacteristics(design_rp2)
n1 <- res$numberOfSubjects1[1, 1]
n2 <- res$numberOfSubjects1[2, 1] - n1
c1f <- qnorm(char$futilityProbabilities) +
sqrt(res$numberOfSubjects1[1]) * .4 / sqrt(2)
c1e <- design_rp2$criticalValues[1]
f <- function(z){
w1 <- 1 / sqrt(2)
w2 <- sqrt(1 - w1^2)
out <- (design_rp2$criticalValues[2] - w1 * z) / w2
return(out)
}
x <- adoptr:::GaussLegendreRule(5)$nodes
h <- (c1e - c1f) / 2
x <- h * x + (h + c1f)
design_gs <<- TwoStageDesign(
n1,
c1f,
c1e,
rep(n2, 5),
sapply(x, f))
# Simulation
sim_alt <<- simulate(
design_gs, nsim = 1e4, dist = Normal(), theta = .4, seed = 59)
sim_null <<- simulate(
design_gs, nsim = 1e4, dist = Normal(), theta = .0, seed = 59)
# optimization = TRUE uses non-rounded values of n (as does rpact!)
# Expected Sample sizes under H1
expect_equal(
res$expectedNumberOfSubjectsH1/2, # per group!
evaluate(ExpectedSampleSize(dist, alternative), design_gs, optimization = TRUE),
tolerance = .1, scale = 1)
expect_equal(
res$expectedNumberOfSubjectsH1/2,
evaluate(ExpectedSampleSize(dist, alternative),
design_gs, specific = FALSE, optimization = TRUE),
tolerance = .1, scale = 1)
# Expected Sample sizes under H0
expect_equal(
res$expectedNumberOfSubjectsH1/2,
evaluate(expected(ConditionalSampleSize(), dist, null), design_gs, optimization = TRUE),
tolerance = .1, scale = 1)
expect_equal(
res$expectedNumberOfSubjectsH0/2,
evaluate(expected(ConditionalSampleSize(), dist, null),
design_gs, specific = FALSE, optimization = TRUE),
tolerance = .1, scale = 1)
}) # end 'expected sample size is computed correctly'
test_that("Power is computed correctly for example design", {
pow <- Power(Normal(), PointMassPrior(.4, 1))
expect_equal(
evaluate(pow, design_gs),
.8,
tolerance = 1e-2, scale = 1)
expect_equal(
evaluate(pow, design_gs, specific = FALSE),
.8,
tolerance = 1e-2, scale = 1)
expect_equal(
mean(sim_alt[, "reject"]),
.8,
tolerance = 1e-2, scale = 1)
}) # end 'power is computed correctly for example design'
test_that("Type one error is computed correctly for example design", {
toer <- Power(Normal(), PointMassPrior(.0, 1))
expect_equal(
evaluate(toer, design_gs),
.05,
tolerance = 1e-3, scale = 1)
expect_equal(
evaluate(toer, design_gs, specific = FALSE),
.05,
tolerance = 1e-3, scale = 1)
expect_equal(
mean(sim_null[, "reject"]),
.05,
tolerance = 1e-3, scale = 1)
}) # end 'type one error is computed correctly for example design'
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.