context("test-AD.pvalue.R")
test_that("AD P-value for uniform sample", {
x = c(0.3103205, -0.1386720, -0.5988813, 0.9483934, -0.5213117,
0.7555062, -0.6821612, 0.7957394, -0.7387161, 0.1910647)
asq = AD.uniform(x)
p_value = AD.uniform.pvalue(a=asq)$P
expect_equal(p_value,0.68906061)
})
test_that("AD P-value for normal sample", {
x = c(0.25024690, -0.33712454, -0.11335370, -0.09888291, 0.26408682,
0.13898369, -0.24226950, 0.05903138, -0.17727187, 0.79468027)
asq = AD.normal(x)
p_value = AD.normal.pvalue(a=asq)$P
expect_equal(p_value,0.39529586)
})
test_that("AD for gamma sample", {
x = c(0.5047757, 0.1538300, 0.5704100, 0.3013008, 1.2775724,
1.0468233, 0.6525627, 0.4376768, 2.4700737, 1.0944885)
asq = AD.gamma(x)
shape = estimate.gamma(x)[2]
p_value = AD.gamma.pvalue(a=asq,shape=shape)$P
expect_equal(p_value,0.88814846)
})
test_that("AD for logistic sample", {
x = c(-1.1263960, 0.9562103, -3.3860294, 0.1980448, 0.7667096,
-0.8461510, -0.4524666, 1.0070690, 3.2450939, 1.1559508)
asq = AD.logistic(x)
p_value = AD.logistic.pvalue(a=asq)$P
expect_equal(p_value,0.61998034)
})
test_that("AD for laplace sample", {
x = c(-0.23539279, 0.16009027, 2.84634962, 0.35710312, -0.40466195,
-0.41113889, 2.16169132, -0.27151351, 0.13770907, 0.02330074)
asq = AD.laplace(x)
p_value = AD.laplace.pvalue(a=asq)$P
expect_equal(p_value,0.067560437)
})
test_that("AD for weibull sample", {
x = c(0.36218715, 0.16506700, 0.16757965, 0.93681048, 1.87396510,
0.44718470, 1.24767735, 0.07435952, 1.86023456, 0.03682825)
asq = AD.weibull(x)
p_value = AD.weibull.pvalue(a=asq)$P
expect_equal(p_value,0.57984488)
})
test_that("AD for exponential sample", {
x = c(13.0581121, 0.8301048, 0.5207504, 1.0923122, 0.7086793,
0.1271974, 3.9326089, 0.0510448, 4.3839846, 3.4396530)
asq = AD.exp(x)
p_value = AD.exp.pvalue(a=asq)$P
expect_equal(p_value,0.14574441)
})
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