context("test optimal_ubpop for correctness")
# test optimal_ubpop function
data(nydf, package = "smerc")
cases = nydf$cases
pop = nydf$population
coords = nydf[, c("x", "y")]
set.seed(28)
estats = optimal_ubpop(coords = coords,
cases = cases,
pop = pop,
alpha = 0.05,
nsim = 999,
ubpop_seq = seq(0.01, 0.5, len = 50),
min.cases = 0)
# correct values of neg_lrt and gini_coef
# (created manually at some point)
neg_lrt = c(-6.661,-7.115,-15.019,-15.019,-15.531,
-15.779,-16.627,-17.252,-18.311,-21.03,
-21.03,-21.03,-21.03,-21.03,-21.03,-21.03,
-21.03,-21.03,-21.03,-21.03,-21.03,-21.03,
-21.03,-21.03,-21.03,-21.03,-21.03,-21.03,
-21.03,-21.03,-21.03,-21.03,-21.03,-21.03,
-21.03,-21.03,-21.03,-21.03,-21.03,-21.03,
-21.03,-21.03,-21.03,-21.03,-21.03,-21.03,
-21.03,-21.03,-21.03,-21.03)
gini_coef = c(0,0,0.061,0.061,0.069,0.081,0.086,0.09,
0.097,0.106,0.106,0.106,0.106,0.106,0.106,
0.106,0.106,0.106,0.106,0.106,0.106,0.106,
0.106,0.106,0.106,0.106,0.106,0.106,0.106,
0.106,0.106,0.106,0.106,0.106,0.106,0.106,
0.106,0.106,0.106,0.106,0.106,0.106,0.106,
0.106,0.106,0.106,0.106,0.106,0.106,0.106)
test_that("check accuracy for optimal_ubpop for NY data", {
expect_equal(neg_lrt, round(estats$elbow_method$stats, 3))
expect_equal(gini_coef, round(estats$gini_method$stats, 3))
# check for correct upper bound
expect_equal(0.1, estats$elbow_ubpop)
expect_equal(0.1, estats$gini_ubpop)
})
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