Nothing
test.r.vs.c <- function () {
s <- 3
p <- 4
v <- 3
for (model in 1:8) {
v.rep <- rep((s*p) %/% v, v) + c(rep(1, (s*p) %% v), rep(0, v-((s*p) %% v)))
design <- matrix(sample(rep(1:v, v.rep)), p, s)
rcDesign <- rcd(design, v, model=model)
rcDesign_R <- rcd_R(design, v, model=model)
expect_true(max(abs(rcDesign-rcDesign_R))<0.00001)
expect_warning(rcdMatrix(design, v, model=model))
expect_warning(rcdMatrix_R(design, v, model=model))
rcDesignMat <- rcdMatrix(rcDesign, v, model=model)
rcDesignMat_R <- rcdMatrix_R(rcDesign, v, model=model)
expect_true(max(abs(rcDesignMat-rcDesignMat_R))<0.00001)
# TODO Implement infMatrix for model 3 and 7:
if (!model %in% c(3,7)) {
Ar <- infMatrix(rcDesign, v, model=model)
} else {
Ar <- infMatrix_R(rcDesign, v, model=model, method=1)
}
Ar_R1 <- infMatrix_R(rcDesign, v, model=model, method=1)
Ar_R2 <- infMatrix_R(rcDesign, v, model=model, method=2)
expect_true(max(abs(Ar-Ar_R1))<0.00001)
expect_true(max(abs(Ar_R2-Ar_R1))<0.00001)
d <- structure(c(2, 2, 1, 1, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 1, 1, 2,
2, 1, 1, 2, 1, 1, 2), .Dim = c(4L, 6L))
expect_true(estimable_R(d, v=2, model=2)==estimable(d, v=2, model=2))
}
}
test_that("R and C calculations give the same result", {
test.r.vs.c()
})
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