Nothing
context("LKSN_test")
set.seed(410)
x=rnorm(100)
expect_error(LKSN_test(x,trend="limear"))
expect_error(LKSN_test(x,tau=0))
expect_error(LKSN_test(x,tau=2))
expect_error(LKSN_test(x,m=1))
expect_warning(LKSN_test(x,simu=0,tau=0.15))
x=stats::ts(x)
expect_error(LKSN_test(x))
x=matrix(1:100,ncol=2)
expect_error(LKSN_test(x))
test_that("",{
skip_on_cran()
#size
trend = c("none", "linear")
statistic = c("mean", "max", "exp")
for(a in 1:2){
tr=trend[a]
q=0
for(i in 1:5){
x=cumsum(rnorm(75))
mod=LKSN_test(x,trend=tr)
expect_lt(mod[1,2],mod[1,1]) #critical values should be in increasing order
expect_equal(ncol(mod),3)
expect_equal(min(mod[1,3],mod[2,3]),mod[3,3]) #max of test statistics should equal test statistic in both directions
q=q+sum(mod[,3]<mod[,2])
}
expect_lt(q,9) #test should not reject H0 (which is true) in more than 8 of 10 cases at the 95 percent level
}
#test has power
for(a in 1:2){
tr=trend[a]
q=0
for(i in 1:5){
x=pb_sim(100, 0.5, "none", d1=0, d2=1, mean=0, var=1)
mod=LKSN_test(x,trend=tr)
q=q+sum(mod[,3]<mod[,1])
}
expect_gt(q,2) #test should reject H0 at least in three of 10 cases at the 90 percent level
}
})
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