tests/testthat/test_plot.R

In ankargren/mfbvar: Mixed-Frequency Bayesian VAR Models

library(mfbvar)
context("Plots")
test_that("Forecasts (minn)", {
  set.seed(10237)
  Y <- mfbvar::mf_sweden
  prior_obj <- set_prior(Y = Y, freq = c(rep("m", 4), "q"),
                                        n_lags = 4, n_burnin = 10, n_reps = 10)

  prior_intervals <- matrix(c( 6,   7,
                               0.1, 0.2,
                               0,   0.5,
                               -0.5, 0.5,
                               0.4, 0.6), ncol = 2, byrow = TRUE)
  psi_moments <- interval_to_moments(prior_intervals)
  prior_psi_mean <- psi_moments$prior_psi_mean
  prior_psi_Omega <- psi_moments$prior_psi_Omega
  prior_obj <- update_prior(prior_obj, d = "intercept", prior_psi_mean = prior_psi_mean,
                             prior_psi_Omega = prior_psi_Omega, n_fcst = 4)

  testthat::skip_on_cran()
  set.seed(10)
  mod_minn <- estimate_mfbvar(mfbvar_prior = prior_obj, prior = "minn", n_fcst = 12)

  expect_error(plot(mod_minn), NA)
  expect_error(plot(mod_minn, plot_start = "2013-07-31"), NA)

  rownames(Y) <- as.character(floor_date(as_date(rownames(Y)), unit = "month"))
  set.seed(10)
  mod_minn <- estimate_mfbvar(mfbvar_prior = prior_obj, prior = "minn", n_fcst = 12,
                              Y = Y)
  expect_error(plot(mod_minn), NA)
  expect_error(plot(mod_minn, plot_start = "2013-07-01"), NA)

  rownames(Y) <- NULL

  set.seed(10)
  mod_minn <- estimate_mfbvar(mfbvar_prior = prior_obj, prior = "minn", n_fcst = 12,
                              Y = Y)
  expect_error(plot(mod_minn))
})

test_that("Forecasts (ss)", {
  set.seed(10237)
  Y <- mfbvar::mf_sweden
  prior_obj <- set_prior(Y = Y, freq = c(rep("m", 4), "q"),
                         n_lags = 4, n_burnin = 10, n_reps = 10)

  prior_intervals <- matrix(c( 6,   7,
                               0.1, 0.2,
                               0,   0.5,
                               -0.5, 0.5,
                               0.4, 0.6), ncol = 2, byrow = TRUE)
  psi_moments <- interval_to_moments(prior_intervals)
  prior_psi_mean <- psi_moments$prior_psi_mean
  prior_psi_Omega <- psi_moments$prior_psi_Omega
  prior_obj <- update_prior(prior_obj, d = "intercept", prior_psi_mean = prior_psi_mean,
                            prior_psi_Omega = prior_psi_Omega, n_fcst = 12)

  testthat::skip_on_cran()
  set.seed(10)
  mod_ss <- estimate_mfbvar(mfbvar_prior = prior_obj, prior = "ss")

  expect_error(plot(mod_ss), NA)
  expect_error(plot(mod_ss, plot_start = "2013-07-31"), NA)

  rownames(Y) <- as.character(floor_date(as_date(rownames(Y)), unit = "month"))
  set.seed(10)
  mod_ss <- estimate_mfbvar(mfbvar_prior = prior_obj, prior = "ss", Y = Y)
  expect_error(plot(mod_ss), NA)
  expect_error(plot(mod_ss, plot_start = "2013-07-01"), NA)

  rownames(Y) <- NULL

  set.seed(10)
  mod_ss <- estimate_mfbvar(mfbvar_prior = prior_obj, prior = "ss", Y = Y)
  expect_error(plot(mod_ss))
})

test_that("Prior", {
  set.seed(10237)
  Y <- mfbvar::mf_sweden
  prior_obj <- set_prior(Y = Y, freq = c(rep("m", 4), "q"),
                         n_lags = 4, n_burnin = 10, n_reps = 10)
  expect_error(plot(prior_obj), NA)

  rownames(Y) <- NULL
  prior_obj <- set_prior(Y = Y, freq = c(rep("m", 4), "q"),
                         n_lags = 4, n_burnin = 10, n_reps = 10)
  plot(prior_obj)
})

test_that("varplot", {
  set.seed(10237)
  Y <- mfbvar::mf_sweden
  prior_obj <- set_prior(Y = Y, freq = c(rep("m", 4), "q"),
                         n_lags = 4, n_burnin = 10, n_reps = 10, n_fac = 1)

  prior_intervals <- matrix(c( 6,   7,
                               0.1, 0.2,
                               0,   0.5,
                               -0.5, 0.5,
                               0.4, 0.6), ncol = 2, byrow = TRUE)
  psi_moments <- interval_to_moments(prior_intervals)
  prior_psi_mean <- psi_moments$prior_psi_mean
  prior_psi_Omega <- psi_moments$prior_psi_Omega
  prior_obj <- update_prior(prior_obj, d = "intercept", prior_psi_mean = prior_psi_mean,
                            prior_psi_Omega = prior_psi_Omega, n_fcst = 12)

  testthat::skip_on_cran()
  set.seed(10)
  mod_ss <- estimate_mfbvar(mfbvar_prior = prior_obj, prior = "ss", variance = "fsv")
  expect_error(varplot(mod_ss, variables = "gdp"), NA)

  rownames(Y) <- NULL
  set.seed(10)
  mod_ss <- estimate_mfbvar(mfbvar_prior = prior_obj, prior = "ss", Y = Y, variance = "fsv")
  expect_error(varplot(mod_ss, variables = "gdp"), NA)

  colnames(Y) <- NULL
  set.seed(10)
  mod_ss <- estimate_mfbvar(mfbvar_prior = prior_obj, prior = "ss", Y = Y, variance = "fsv")
  expect_error(varplot(mod_ss, variables = 1), NA)
})

test_that("Weekly-Monthly plots", {
  set.seed(10237)
  Y <- matrix(rnorm(400), 100, 4)
  Y[setdiff(1:100,seq(4, 100, by = 4)), 4] <- NA

  prior_obj <- set_prior(Y = Y, freq = c(rep("w", 3), "m"),
                         n_lags = 4, n_reps = 10)

  prior_intervals <- matrix(c(
    -0.5, 0.5,
    -0.5, 0.5,
    -0.5, 0.5,
    -0.5, 0.5), ncol = 2, byrow = TRUE)
  psi_moments <- interval_to_moments(prior_intervals)
  prior_psi_mean <- psi_moments$prior_psi_mean
  prior_psi_Omega <- psi_moments$prior_psi_Omega
  prior_obj <- update_prior(prior_obj, d = "intercept", prior_psi_mean = prior_psi_mean,
                            prior_psi_Omega = prior_psi_Omega, n_fcst = 4)

  testthat::skip_on_cran()
  set.seed(10)
  mod_ss <- estimate_mfbvar(mfbvar_prior = prior_obj, prior = "ss", variance = "csv")
  expect_error(varplot(mod_ss, variables = 1), NA)
  expect_error(plot(mod_ss))
})