context("Scale test")

test_that("Output of scale_stat has the correct format", 
{
  x <- 1:6
  y <- scale_stat(x)
  
  expect_true(is.numeric(y))
  expect_equal(length(y), 1)
  
  expect_error(scale_stat(x, version = "a"))
  expect_error(scale_stat(1))
})


test_that("scale_stat is computed correctly", 
{
  x <- c(92, 49, 48, 57, 27)
  b_n <- 2
  
  # empVar:
  y <- 4 * 125.99 / sqrt(5 * lrv(x, control = list(b_n = b_n, kFun = "quadratic", 
                                                version = "empVar")))
  z <- scale_stat(x, version = "empVar", control = list(b_n = b_n))
  attributes(z) <- NULL
  expect_equal(z, y)
  
  # MD:
  y <-  49 / sqrt(5 * lrv(x, control = list(b_n = b_n, kFun = "quadratic", 
                                               version = "MD")))
  z <- scale_stat(x, version = "MD", control = list(b_n = b_n))
  attributes(z) <- NULL
  expect_equal(z, y)
  
  # GMD:
  y <-  30.4 / sqrt(5 * lrv(x, "kernel", control = list(b_n = b_n, kFun = "quadratic",
                                                        version = "GMD")))
  z <- scale_stat(x, version = "GMD", control = list(b_n = b_n))
  attributes(z) <- NULL
  expect_equal(z, y)
  
  # # MAD:
  # y <-  27 * 1.4826 / sqrt(5 * lrv(x, "kernel",
  #                         control = list(b_n = b_n, version = "MAD")))
  # z <- scale_stat(x, version = "MAD", control = list(b_n = b_n))
  # attributes(z) <- NULL
  # expect_equal(z, y)

  # QBeta:
  x <- c(6, 2, 1, 10, 5, 0)
  beta <- 0.9
  y <-  12 / sqrt(6 * lrv(x, "kernel", control = list(version = "Qalpha",
                              kFun = "quadratic", b_n = b_n, alpha_Q = beta)))
  z <- scale_stat(x, version = "Qalpha", control = list(b_n = b_n), alpha = beta)
  attributes(z) <- NULL
  expect_equal(z, y)
})


test_that("Output of scale_cusum has the correct format", 
{
  x <- rnorm(10)
  res <- suppressWarnings(scale_cusum(x))
  
  expect_equal(class(res), "htest")
  expect_equal(res$alternative, "two-sided")
  expect_equal(res$method, "CUSUM test for scale changes")
  
  testStructure(scale_cusum, "kernel")
})


test_that("CUSUM test for changes in the scale is performed correctly", 
{
  # empVar:
  suppressWarnings({p <- replicate(200, 
  {
    x <- rnorm(200)
    x[101:200] <- x[101:200] * 3
    scale_cusum(x, control = list(b_n = NA))$p.value
  })})
  
  expect_equal(mean(p < 0.05), 1, tolerance = 0.01)
  
  # skip_on_cran()
  # suppressWarnings({p <- replicate(200, 
  # {
  #   x <- rnorm(200)
  #   x[101:200] <- x[101:200] * 3
  #   scale_cusum(x, control = list(b_n = NA, l = 10), method = "bootstrap")$p.value
  # })})
  # 
  # expect_equal(mean(p < 0.05), 1, tolerance = 0.01)
  
  
  # MD:
  suppressWarnings({p <- replicate(200, 
  {
    x <- rnorm(200)
    x[101:200] <- x[101:200] * 3
    scale_cusum(x, version = "MD", control = list(b_n = NA))$p.value
  })})
  
  expect_equal(mean(p < 0.05), 1, tolerance = 0.01)
  
  
  # GMD:
  suppressWarnings({p <- replicate(200, 
  {
    x <- rnorm(200)
    x[101:200] <- x[101:200] * 3
    scale_cusum(x, version = "GMD", method = "kernel")$p.value
  })})
  
  expect_equal(mean(p < 0.05), 1, tolerance = 0.01)
  
  # skip_on_cran()
  # suppressWarnings({p <- replicate(200, 
  # {
  #   x <- rnorm(200)
  #   x[101:200] <- x[101:200] * 3
  #   scale_cusum(x, version = "GMD", method = "bootstrap", tol = 1e-3, 
  #               control = list(l = 10))$p.value
  # })})
  
  expect_equal(mean(p < 0.05), 1, tolerance = 0.01)
  
  
  # MAD:
  # suppressWarnings({p <- replicate(200,
  # {
  #   x <- rnorm(200)
  #   x[101:200] <- x[101:200] * 3
  #   scale_cusum(x, version = "MAD", method = "kernel")$p.value
  # })})
  # hist(p)
  # expect_equal(mean(p < 0.05), 1, tolerance = 0.01)

  # Qalpha:
  suppressWarnings({p <- replicate(1000,
  {
    x <- rnorm(200)
    x[101:200] <- x[101:200] * 3
    scale_cusum(x, version = "Qalpha", method = "kernel",
                alpha = 0.8, tol = 1e-3)$p.value
  })})
  
  expect_equal(mean(p < 0.05), 1, tolerance = 0.1)
  
  # correct change point location
  x <- rnorm(100)
  x[50:100] <- x[50:100] * 3
  expect_equal(attr(scale_cusum(x, version = "empVar")$statistic, "cp-location"), 50, tolerance = 1)
  
  ## maybe some more tests
  ## best to be checked graphically:
  ## hist(p)
})


# test_that("Dependent wild bootstrap is performed correctly", 
# {
#   n <- 50
#   x <- rnorm(n)
#   seed <- 1895
#   B <- 100
#   l <- 5
#   k <- floor(n / l)
#   
#   stat1 <- scale_stat(x, "empVar", "bootstrap")
#   stat2 <- scale_stat(x, "GMD", "bootstrap")
#   
#   set.seed(seed)
#   res <- replicate(B,
#   {
#     j <- sample(1:(n-l+1), k, replace = TRUE)
#     x_star <- x[as.vector(index <- sapply(j, function(j) j:(j+l-1)))]
#     c(scale_stat(x_star, "empVar", "bootstrap"), 
#       scale_stat(x_star, "GMD", "bootstrap"))
#   })
#   
#   expect_equal(scale_cusum(x, "empVar", "bootstrap", tol = 1/B, fpc = FALSE,
#                            control = list(l = l, seed = seed))$p.value, 
#                mean(res[1, ] > stat1))
#   expect_equal(scale_cusum(x, "GMD", "bootstrap", tol = 1/B, fpc = FALSE,
#                            control = list(l = l, seed = seed))$p.value, 
#                mean(res[2, ] > stat2))
#   
# })