context("plot.fpca")
test_that("plot.fpca returns a cowplot object with classes 'gg' and 'ggplot' for all FPCA functions",{
testthat::skip_if_not_installed("ggplot2")
testthat::skip_if_not_installed("cowplot")
# Gaussian FPCA
data(growth_incomplete)
expect_warning({
fpca_obj1a = fpca_gauss(Y = growth_incomplete, npc = 2)
}, "fpca_gauss convergence not reached. Try increasing maxiter.")
fpca_obj1b = gfpca_twoStep(Y = growth_incomplete, npc = 2, family = "gaussian")
plot_obj1a = plot(fpca_obj1a)
plot_obj1b = plot(fpca_obj1b)
# Binomial FPCA
Y = simulate_functional_data()$Y
fpca_obj2a = bfpca(Y, npc = 2)
fpca_obj2b = gfpca_twoStep(Y = Y, npc = 2, family = "binomial")
plot_obj2a = plot(fpca_obj2a)
plot_obj2b = plot(fpca_obj2b)
# Gamma FPCA
Y$value = Y$value + 1 # get strictly positive data
fpca_obj3 = gfpca_twoStep(Y = Y, npc = 2, family = "gamma")
plot_obj3 = plot(fpca_obj3)
# Poisson FPCA
fpca_obj4 = gfpca_twoStep(Y = Y, npc = 2, family = "poisson")
plot_obj4 = plot(fpca_obj4)
expect_s3_class(plot_obj1a, class = c("gg","ggplot"))
expect_s3_class(plot_obj1b, class = c("gg","ggplot"))
expect_s3_class(plot_obj2a, class = c("gg","ggplot"))
expect_s3_class(plot_obj2b, class = c("gg","ggplot"))
expect_s3_class(plot_obj3, class = c("gg","ggplot"))
expect_s3_class(plot_obj4, class = c("gg","ggplot"))
})
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