tests/testthat/test-plm_methods.R

In bdrc: Bayesian Discharge Rating Curves

context('plm general functions')
test_that("trace plots have known output", {
    skip_on_cran()
    expect_doppelganger('plm0_trace_plot_hyperparameters',autoplot(plm0.fit,type='trace','hyperparameters'))
    expect_doppelganger('plm0_trace_plot_hyperparameters_transformed',autoplot(plm0.fit,type='trace','hyperparameters',transformed=TRUE))
    expect_doppelganger('plm0_trace_plot_single_parameter',autoplot(plm0.fit,type='trace','sigma_eps'))

    expect_doppelganger('plm_trace_plot_hyperparameters',autoplot(plm.fit,type='trace','hyperparameters'))
    expect_doppelganger('plm_trace_plot_hyperparameters_transformed',autoplot(plm.fit,type='trace','hyperparameters',transformed=TRUE))
    expect_doppelganger('plm_trace_plot_single_parameter',autoplot(plm.fit,type='trace','sigma_eta'))

    expect_doppelganger('gplm0_trace_plot_hyperparameters',autoplot(gplm0.fit,type='trace','hyperparameters'))
    expect_doppelganger('gplm0_trace_plot_hyperparameters_transformed',autoplot(gplm0.fit,type='trace','hyperparameters',transformed=TRUE))
    expect_doppelganger('gplm0_trace_plot_single_parameter',autoplot(gplm0.fit,type='trace','sigma_eps'))

    expect_doppelganger('gplm_trace_plot_hyperparameters',autoplot(gplm.fit,type='trace','hyperparameters'))
    expect_doppelganger('gplm_trace_plot_hyperparameters_transformed',autoplot(gplm.fit,type='trace','hyperparameters',transformed=TRUE))
    expect_doppelganger('gplm_trace_plot_single_parameter',autoplot(gplm.fit,type='trace','sigma_eta'))
})
test_that("histogram have known output", {
    skip_on_cran()
    expect_doppelganger('plm0_histogram_hyperparameters',autoplot(plm0.fit,type='histogram','hyperparameters'))
    expect_doppelganger('plm0_histogram_hyperparameters_transformed',autoplot(plm0.fit,type='histogram','hyperparameters',transformed=TRUE))
    expect_doppelganger('plm0_histogram_single_parameter',autoplot(plm0.fit,type='histogram','sigma_eps'))

    expect_doppelganger('plm_histogram_plot_hyperparameters',autoplot(plm.fit,type='histogram','hyperparameters'))
    expect_doppelganger('plm_histogram_plot_hyperparameters_transformed',autoplot(plm.fit,type='histogram','hyperparameters',transformed=TRUE))
    expect_doppelganger('plm_histogram_plot_single_parameter',autoplot(plm.fit,type='histogram','sigma_eta'))

    expect_doppelganger('gplm0_histogram_plot_hyperparameters',autoplot(gplm0.fit,type='histogram','hyperparameters'))
    expect_doppelganger('gplm0_histogram_plot_hyperparameters_transformed',autoplot(gplm0.fit,type='histogram','hyperparameters',transformed=TRUE))
    expect_doppelganger('gplm0_histogram_plot_single_parameter',autoplot(gplm0.fit,type='histogram','sigma_eps'))

    expect_doppelganger('gplm_histogram_plot_hyperparameters',autoplot(gplm.fit,type='histogram','hyperparameters'))
    expect_doppelganger('gplm_histogram_plot_hyperparameters_transformed',autoplot(gplm.fit,type='histogram','hyperparameters',transformed=TRUE))
    expect_doppelganger('gplm_histogram_plot_single_parameter',autoplot(gplm.fit,type='histogram','sigma_eta'))
})

test_that("rating_curve has known output", {
    skip_on_cran()
    expect_doppelganger('plm0_rating_curve',autoplot(plm0.fit,type='rating_curve'))
    expect_doppelganger('plm0_rating_curve_transformed',autoplot(plm0.fit,type='rating_curve',transformed=TRUE))

    expect_doppelganger('plm_rating_curve',autoplot(plm.fit,type='rating_curve'))
    expect_doppelganger('plm_rating_curve_transformed',autoplot(plm.fit,type='rating_curve',transformed=TRUE))

    expect_doppelganger('gplm0_rating_curve',autoplot(gplm0.fit,type='rating_curve'))
    expect_doppelganger('gplm0_rating_curve_transformed',autoplot(gplm0.fit,type='rating_curve',transformed=TRUE))

    expect_doppelganger('gplm_rating_curve',autoplot(gplm.fit,type='rating_curve'))
    expect_doppelganger('gplm_rating_curve_transformed',autoplot(gplm.fit,type='rating_curve',transformed=TRUE))
})

test_that("rating_curve_mean has known output", {
    skip_on_cran()
    expect_doppelganger('plm0_rating_curve_mean',autoplot(plm0.fit,type='rating_curve_mean'))
    expect_doppelganger('plm0_rating_curve_mean_transformed',autoplot(plm0.fit,type='rating_curve_mean',transformed=TRUE))

    expect_doppelganger('plm_rating_curve_mean',autoplot(plm.fit,type='rating_curve_mean'))
    expect_doppelganger('plm_rating_curve_mean_transformed',autoplot(plm.fit,type='rating_curve_mean',transformed=TRUE))

    expect_doppelganger('gplm0_rating_curve_mean',autoplot(gplm0.fit,type='rating_curve_mean'))
    expect_doppelganger('gplm0_rating_curve_mean_transformed',autoplot(gplm0.fit,type='rating_curve_mean',transformed=TRUE))

    expect_doppelganger('gplm_rating_curve_mean',autoplot(gplm.fit,type='rating_curve_mean'))
    expect_doppelganger('gplm_rating_curve_mean_transformed',autoplot(gplm.fit,type='rating_curve_mean',transformed=TRUE))
})


test_that("sigma_eps has known output", {
    skip_on_cran()
    expect_doppelganger('plm0_sigma_eps',autoplot(plm0.fit,type='sigma_eps'))
    expect_doppelganger('plm_sigma_eps',autoplot(plm.fit,type='sigma_eps'))
    expect_doppelganger('gplm0_sigma_eps',autoplot(gplm0.fit,type='sigma_eps'))
    expect_doppelganger('gplm_sigma_eps',autoplot(gplm.fit,type='sigma_eps'))
})

test_that("beta has known output", {
    skip_on_cran()
    expect_error(autoplot(plm0.fit,type='beta'))
    expect_error(autoplot(plm.fit,type='beta'))
    expect_doppelganger('gplm0_beta',autoplot(gplm0.fit,type='beta'))
    expect_doppelganger('gplm_beta',autoplot(gplm.fit,type='beta'))
})

test_that("f has known output", {
    skip_on_cran()
    expect_doppelganger('plm0_f',autoplot(plm0.fit,type='f'))
    expect_doppelganger('plm_f',autoplot(plm.fit,type='f'))
    expect_doppelganger('gplm0_f',autoplot(gplm0.fit,type='f'))
    expect_doppelganger('gplm_f',autoplot(gplm.fit,type='f'))
})

test_that("residuals has known output", {
    skip_on_cran()
    expect_doppelganger('plm0_residuals',autoplot(plm0.fit,type='residuals'))
    expect_doppelganger('plm_residuals',autoplot(plm.fit,type='residuals'))
    expect_doppelganger('gplm0_residuals',autoplot(gplm0.fit,type='residuals'))
    expect_doppelganger('gplm_residuals',autoplot(gplm.fit,type='residuals'))
})