Nothing
d <- 2; n <- 16
design.fact <- expand.grid(x1=seq(0,1,length=4), x2=seq(0,1,length=4))
y <- apply(design.fact, 1, branin)
# kriging model 1 : matern5_2 covariance structure, no trend
m1 <- km(design=design.fact, response=y,
coef.trend=130, coef.cov=c(0.3, 0.8), coef.var=10000)
# with nugget: should still interpolate (a difference between noisy observations)
m1Nugget <- km(design=design.fact, response=y, nugget = 1000,
coef.trend=130, coef.cov=c(0.3, 0.8), coef.var=10000)
p <- predict(m1, newdata=design.fact, type="UK")
pNugget <- predict(m1Nugget, newdata=design.fact, type="UK")
precision <- 1e-10 # the following tests should work with it, since the computations are analytical
test_that(desc="Kriging mean (no nugget), on the design points",
expect_true(max(abs(p$mean - y)) < precision))
test_that(desc="Kriging mean with nugget, on the design points",
expect_true(max(abs(pNugget$mean - y)) < precision))
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