# Test of gamma methods
library(vdiffr)
set.seed(77)
## test data
gamma_test_matrix <- matrix(c(38, 4, 5, 0, 6, 40, 1, 2, 4, 8, 20, 30),
ncol = 4,
byrow = TRUE)
object<-dfba_gamma(gamma_test_matrix)
test_that("Show method works",{
expect_output(show(object))
})
test_that("Plot method works with prior",{
expect_doppelganger(
title = "gamma_plot",
fig = plot(object),
)
})
test_that("Plot method works with no prior",{
expect_doppelganger(
title = "gamma_plot_no_prior",
fig = plot(object,
plot.prior = FALSE),
)
})
# Phi_star
p <- seq(.05,.95,.05)
ypred <- 17.332-(50.261*p) + (48.308*p^2)
# # Note the coefficients in the ypred equation were found first
# # via a polynomial regression
yobs <- c(19.805, 10.105, 9.396, 8.219, 6.110, 4.543, 5.864, 4.861, 6.136,
5.789, 5.443, 5.548, 4.746, 6.484, 6.185, 6.202, 9.804, 9.332,
14.408)
objectstar <- dfba_bivariate_concordance(x = yobs,
y = ypred,
fitting.parameters = 3)
test_that("Show method works for phi_star",{
expect_output(show(objectstar))
})
test_that("Plot method works for phi_star",{
expect_doppelganger(
title = "bivariate_concordance_star_plot",
fig = plot(objectstar),
)
})
test_that("Plot method works for phi_star with no prior",{
expect_doppelganger(
title = "bivariate_concordance_star_plot_no_prior",
fig = plot(objectstar,
plot.prior = FALSE),
)
})
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