Nothing
# Test randwin function #
test_that("Check randwin output with slidingwin and max", {
set.seed(666)
data(Mass, envir = environment())
data(MassClimate, envir = environment())
furthest = 2
closest = 1
stat = "max"
repeats = 2
rand <- randwin(repeats = 2, xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date,
bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass),
range = c(2, 1),
type = "relative", stat = "max", func = "lin", cmissing = FALSE,
window = "sliding")
duration <- (furthest - closest) + 1
maxmodno <- (duration * (duration + 1)) / 2
# Test that randwin produces an output
expect_true(is.list(rand))
# Test that there are no NA values in output
expect_equal(length(which(is.na(rand[[1]][, 4]))), 0)
# Test that the randomised output has the right number of columns
expect_true(ncol(rand[[1]]) == 19)
# Test that the right number of models has been fitted
expect_equal(repeats, nrow(rand[[1]]))
# Test that data has been stored as randomised
expect_true((rand[[1]]["Randomised"])[1,] == "yes")
#Test that values match previous R version
expect_true(round(rand[[1]]$deltaAICc[1], 1) == 1.7)
expect_true(rand[[1]]$WindowOpen[1] == 1 & rand[[1]]$WindowClose[1] == 1)
expect_true(round(rand[[1]]$ModelBeta[1], 1) == -0.3)
})
############################################################
# Test randwin function with mean climate#
test_that("Check randwin output with slidingwin and mean", {
set.seed(666)
data(Mass, envir = environment())
data(MassClimate, envir = environment())
furthest = 2
closest = 1
stat = "max"
repeats = 2
rand <- randwin(repeats = 2, xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date,
bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass),
range = c(2, 1),
type = "relative", stat = "mean", func = "lin", cmissing = FALSE,
window = "sliding")
duration <- (furthest - closest) + 1
maxmodno <- (duration * (duration + 1)) / 2
# Test that randwin produces an output
expect_true(is.list(rand))
# Test that there are no NA values in output
expect_equal(length(which(is.na(rand[[1]][, 4]))), 0)
# Test that the randomised output has the right number of columns
expect_true(ncol(rand[[1]]) == 19)
# Test that the right number of models has been fitted
expect_equal(repeats, nrow(rand[[1]]))
# Test that data has been stored as randomised
expect_true((rand[[1]]["Randomised"])[1,] == "yes")
#Test that values match previous R version
expect_true(round(rand[[1]]$deltaAICc[1], 1) == 1.7)
expect_true(rand[[1]]$WindowOpen[1] == 1 & rand[[1]]$WindowClose[1] == 1)
expect_true(round(rand[[1]]$ModelBeta[1], 1) == -0.3)
})
############################################################
test_that("Check randwin output works with spatial replication", {
set.seed(666)
data(Mass, envir = environment())
Mass$Plot <- c(rep(c("A", "B"), 23), "A")
data(MassClimate, envir = environment())
MassClimate$Plot <- "A"
MassClimate2 <- MassClimate
MassClimate2$Plot <- "B"
Clim <- rbind(MassClimate, MassClimate2)
furthest = 2
closest = 1
stat = "max"
repeats = 2
rand <- randwin(repeats = 2, xvar = list(Temp = Clim$Temp), cdate = Clim$Date,
bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass),
range = c(2, 1), spatial = list(Mass$Plot, Clim$Plot),
type = "relative", stat = "max", func = "lin", cmissing = FALSE,
window = "sliding")
duration <- (furthest - closest) + 1
maxmodno <- (duration * (duration + 1)) / 2
# Test that randwin produces an output
expect_true(is.list(rand))
# Test that there are no NA values in output
expect_equal(length(which(is.na(rand[[1]][, 4]))), 0)
# Test that the randomised output has the right number of columns
expect_true(ncol(rand[[1]]) == 19)
# Test that the right number of models has been fitted
expect_equal(repeats, nrow(rand[[1]]))
# Test that data has been stored as randomised
expect_true((rand[[1]]["Randomised"])[1,] == "yes")
#Test that values match previous R version
expect_true(round(rand[[1]]$deltaAICc[1], 1) == 1.7)
expect_true(rand[[1]]$WindowOpen[1] == 1 & rand[[1]]$WindowClose[1] == 1)
expect_true(round(rand[[1]]$ModelBeta[1], 1) == -0.3)
})
#############################################################
test_that("Check randwin output with weightwin", {
set.seed(666)
data(Mass, envir = environment())
data(MassClimate, envir = environment())
furthest = 2
closest = 1
repeats = 2
rand <- randwin(repeats = 2, xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date,
bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass),
range = c(2, 1),
type = "relative", func = "lin", cmissing = FALSE,
window = "weighted", weightfunc = "W", cinterval = "day",
par = c(3, 0.2, 0), control = list(ndeps = c(0.01, 0.01, 0.01)),
method = "L-BFGS-B")
duration <- (furthest - closest) + 1
maxmodno <- (duration * (duration + 1)) / 2
# Test that randwin produces an output
expect_true(is.list(rand))
# Test that there are no NA values in output
expect_equal(length(which(is.na(rand[[1]][, 9]))), 0)
# Test that the randomised output has the right number of columns
expect_true(ncol(rand[[1]]) == 15)
# Test that the right number of models has been fitted
expect_equal(repeats, nrow(rand[[1]]))
# Test that data has been stored as randomised
expect_true((rand[[1]]["Randomised"])[1,] == "yes")
#Test that values match previous R version
expect_true(round(rand[[1]]$deltaAICc[1], 1) == 1.7)
expect_true(round(rand[[1]]$ModelBeta[1], 1) == -0.3)
expect_true(round(rand[[1]]$ModelInt[1], 0) == 132)
})
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