Nothing
# Test the pvalue function #
test_that("pvalue produces Pc metric", {
data(Mass, envir = environment())
data(MassClimate, envir = environment())
testdata <- slidingwin(xvar = list(MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date,
baseline = lm(Mass ~ 1, data = Mass), range = c(3, 2),
type = "relative", stat = "max", func = "lin", cmissing = FALSE)
testdatarand <- randwin(repeats = 2, xvar = list(MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date,
baseline = lm(Mass ~ 1, data = Mass), range = c(3, 2),
type = "relative", stat = "max", func = "lin", cmissing = FALSE,
window = "sliding")
testpvalue <- pvalue(datasetrand = testdatarand[[1]], dataset = testdata[[1]]$Dataset,
metric = "C", sample.size = 47)
#Test that pvalue produces a number for Pc
expect_true(is.numeric(testpvalue))
#Test that value is the same as current R version
expect_true(round(testpvalue, 2) == 0.96)
})
test_that("pvalue produces PdAICc metric", {
data(Mass, envir = environment())
data(MassClimate, envir = environment())
testdata <- slidingwin(xvar = list(MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date,
baseline = lm(Mass ~ 1, data = Mass), range = c(3, 2),
type = "relative", stat = "max", func = "lin", cmissing = FALSE)
testdatarand <- randwin(repeats = 2, xvar = list(MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date,
baseline = lm(Mass ~ 1, data = Mass), range = c(3, 2),
type = "relative", stat = "max", func = "lin", cmissing = FALSE,
window = "sliding")
testdata[[1]]$Dataset$deltaAICc[1] = testdatarand[[1]]$deltaAICc[1]
testpvalue <- pvalue(datasetrand = testdatarand[[1]], dataset = testdata[[1]]$Dataset,
metric = "AIC", sample.size = 47)
#Test that pvalue produces a number for PdAICc
expect_true(is.numeric(testpvalue))
testdata[[1]]$Dataset$deltaAICc[1] = -100
testpvalue <- pvalue(datasetrand = testdatarand[[1]], dataset = testdata[[1]]$Dataset,
metric = "AIC", sample.size = 47)
#Test that pvalue returns <0.001 when percentile is 0
expect_true(testpvalue == "<0.001")
})
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