test_that("TLSW executes", { skip_on_cran() x <- stats::rnorm(128) x.lsw <- TLSW(x) expect_equal(class(x.lsw), "TLSW") }) test_that("TLSW executes without spec est", { skip_on_cran() x <- stats::rnorm(128) x.lsw <- TLSW(x, do.spec.est = FALSE) expect_equal(class(x.lsw), "TLSW") }) test_that("TLSW executes with differenced spec est", { skip_on_cran() x <- stats::rnorm(128) x.lsw <- TLSW(x, S.do.diff = TRUE, do.trend.est = FALSE) expect_equal(class(x.lsw), "TLSW") }) test_that("TLSW executes with nonlinear trend est", { skip_on_cran() x <- stats::rnorm(128) x.lsw <- TLSW(x, T.est.type = "nonlinear") expect_equal(class(x.lsw), "TLSW") }) test_that("TLSW recognises T.thresh.type", { skip_on_cran() x <- stats::rnorm(128) expect_error( TLSW(x, T.thresh.type = "bayes", T.est.type = "nonlinear"), "The parameter T.thresh.type must be either 'hard' or 'soft'." ) }) test_that("TLSW executes with supplied inv mat", { skip_on_cran() x <- stats::rnorm(128) inv.mat <- solve(Cmat.calc(J = log2(128))) x.lsw <- TLSW(x, S.inv.mat = inv.mat) expect_equal(class(x.lsw), "TLSW") }) test_that("TLSW executes with nonlinear wavelet trend estimator using non-differenced spec est", { skip_on_cran() x <- stats::rnorm(128) x.lsw <- TLSW(x, T.CI = TRUE, T.est.type = "nonlinear") expect_equal(class(x.lsw), "TLSW") }) test_that("TLSW gives same output: spec", { skip_on_cran() spec <- matrix(0, nrow = 9, ncol = 512) spec[1, ] <- 1 + sin(seq(from = 0, to = 2 * pi, length = 512))^2 trend <- seq(from = 0, to = 5, length = 512) set.seed(1) x <- TLSWsim(trend = trend, spec = spec) x.TLSW <- TLSW(x) expect_snapshot_value(x.TLSW$spec.est$S$D, style = "deparse") }) test_that("TLSW gives same output: trend", { skip_on_cran() spec <- matrix(0, nrow = 9, ncol = 512) spec[1, ] <- 1 + sin(seq(from = 0, to = 2 * pi, length = 512))^2 trend <- seq(from = 0, to = 5, length = 512) set.seed(1) x <- TLSWsim(trend = trend, spec = spec) x.TLSW <- TLSW(x) expect_snapshot_value(x.TLSW$trend.est, style = "deparse") })