Nothing
context("test-PlotDistribution")
test_that("Error thrown for incorrect input to plot.distribution", {
expect_error(plot.distribution(1))
})
test_that("Error thrown for unsupported distribution", {
x <- 1
class(x) <- c("unsupported_name", "distribution")
expect_error(plot(x))
})
test_that("Binomial plot for scalar parameters", {
B <- Binomial(20, 0.7)
expect_equal(plot(B)$size, 20)
})
test_that("Binomial pmf plot for vector parameters, all = FALSE", {
B2 <- Binomial(20, c(0.1, 0.5, 0.9))
expect_equal(plot(B2)$size, c(20, 20, 20))
})
test_that("Binomial cdf plot for vector parameters, all = TRUE", {
B <- Binomial(20, c(0.1, 0.5, 0.9))
expect_equal(plot(B, cdf = TRUE, all = TRUE)$size, c(20, 20, 20))
})
test_that("Gamma plot for vector parameters, all = FALSE", {
G <- Gamma(c(1, 3), 1:2)
x <- plot(G, xlim = c(-1, 7))
expect_equal(x$rate, G$rate)
expect_equal(x$shape, G$shape)
})
test_that("Gamma plot for vector parameters, all = TRUE", {
G <- Gamma(c(1, 3), 1:2)
x <- plot(G, cdf = TRUE, all = TRUE)
expect_equal(x$rate, c(1, 1, 2, 2))
expect_equal(x$shape, c(1, 3, 1, 3))
})
test_that("ggplot2 implementation works", {
## tests only if is ggplot2 object and if can be printed
N1 <- Normal()
N2 <- Normal(0, c(1, 2))
B1 <- Binomial(10, 0.2)
B2 <- Binomial(10, c(0.2, 0.5))
## plot pdf of continous distribution
gg1 <- plot_pdf(N1) + geom_auc(to = -0.645)
expect_true(ggplot2::is.ggplot(gg1))
expect_silent(print(gg1))
gg2 <- plot_pdf(N1) + geom_auc(from = -0.645, to = 0.1, annotate = TRUE)
expect_true(ggplot2::is.ggplot(gg2))
expect_silent(print(gg2))
gg3 <- plot_pdf(N2) + geom_auc(to = 0)
expect_true(ggplot2::is.ggplot(gg3))
expect_silent(print(gg3))
gg4 <- plot_pdf(N2) + geom_auc(from = -2, to = 2, annotate = TRUE)
expect_true(ggplot2::is.ggplot(gg4))
expect_silent(print(gg4))
## plot pdf of discrete distribution
gg5 <- plot_pdf(B1)
expect_true(ggplot2::is.ggplot(gg5))
expect_silent(print(gg5))
gg6 <- plot_pdf(B2)
expect_true(ggplot2::is.ggplot(gg6))
expect_silent(print(gg6))
## plot pdf of discrete distribution
gg7 <- plot_cdf(N1)
expect_true(ggplot2::is.ggplot(gg7))
expect_silent(print(gg7))
gg8 <- plot_cdf(N2)
expect_true(ggplot2::is.ggplot(gg8))
expect_silent(print(gg8))
## plot pdf of discrete distribution
gg9 <- plot_cdf(B1)
expect_true(ggplot2::is.ggplot(gg9))
expect_silent(print(gg9))
gg10 <- plot_cdf(B2)
expect_true(ggplot2::is.ggplot(gg10))
expect_silent(print(gg10))
})
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