test-02_MGamma.R
In estimators: Parameter Estimation

# test_that("Log-Likelihood works", {
#
#   shape <- 1:3
#   scale <- 2
#   prm <- c(shape, scale)
#   D <- MGamma(shape, scale)
#   x <- rmgamma(100, shape, scale)
#
#   expect_identical(llmgamma(x, shape, scale), ll(x, prm, D))
#
# })
#
# test_that("e functions work", {
#
#   shape <- 1:3
#   scale <- 2
#   prm <- c(shape, scale)
#   D <- MGamma(shape, scale)
#   x <- rmgamma(100, shape, scale)
#
#   expect_identical(emgamma(x, "mle"), mle(x, D))
#   expect_identical(emgamma(x, "me"), me(x, D))
#   expect_identical(emgamma(x, "same"), same(x, D))
#
# })
#
# test_that("v functions work", {
#
#   shape <- 1:3
#   scale <- 2
#   prm <- c(shape, scale)
#   D <- MGamma(shape, scale)
#
#   expect_identical(vmgamma(shape, scale, "mle"), avar_mle(D))
#   expect_identical(vmgamma(shape, scale, "me"), avar_me(D))
#   expect_identical(vmgamma(shape, scale, "same"), avar_same(D))
#
# })
#
# test_that("ME is consistent", {
#
#   est <- "me"
#   D0 <- MGamma(shape = 1:3, scale = 4)
#   d <- test_consistency(est, D0)
#   expect_equal(d$prm_true, d$prm_est, tolerance = 0.5)
#
# })
#
# test_that("SAME is consistent", {
#
#   est <- "same"
#   D0 <- MGamma(shape = 1:3, scale = 4)
#   d <- test_consistency(est, D0)
#   expect_equal(d$prm_true, d$prm_est, tolerance = 0.5)
#
# })
#
# test_that("Dirichlet-based ME is consistent", {
#
#   est <- "me"
#   D0 <- MGamma(shape = 1:3, scale = 4)
#   d <- test_consistency(est, D0, dirich = TRUE)
#   expect_equal(d$prm_true, d$prm_est, tolerance = 0.5)
#
# })
#
# test_that("Dirichlet-based SAME is consistent", {
#
#   est <- "same"
#   D0 <- MGamma(shape = 1:3, scale = 4)
#   d <- test_consistency(est, D0, dirich = TRUE)
#   expect_equal(d$prm_true, d$prm_est, tolerance = 0.5)
#
# })
#
# test_that("MLE is consistent", {
#
#   est <- "mle"
#   D0 <- MGamma(shape = 1:3, scale = 4)
#   d <- test_consistency(est, D0)
#   expect_equal(d$prm_true, d$prm_est, tolerance = 0.5)
#
# })
#
# test_that("ME avar is correct", {
#
#   est <- "me"
#   D0 <- MGamma(shape = 1:3, scale = 4)
#   d <- test_avar(est, D0)
#   expect_equal(d$avar_true, d$avar_est, tolerance = 0.5)
#
# })
#
# test_that("SAME avar is correct", {
#
#   est <- "same"
#   D0 <- MGamma(shape = 1:3, scale = 4)
#   d <- test_avar(est, D0)
#   expect_equal(d$avar_true, d$avar_est, tolerance = 0.5)
#
# })
#
# test_that("Dirichlet-based ME avar is correct", {
#
#   est <- "me"
#   D0 <- MGamma(shape = 1:3, scale = 4)
#   d <- test_avar(est, D0, dirich = TRUE)
#   expect_equal(d$avar_true, d$avar_est, tolerance = 0.5)
#
# })
#
# test_that("Dirichlet-based SAME avar is correct", {
#
#   est <- "same"
#   D0 <- MGamma(shape = 1:3, scale = 4)
#   d <- test_avar(est, D0, dirich = TRUE)
#   expect_equal(d$avar_true, d$avar_est, tolerance = 0.5)
#
# })
#
# test_that("MLE avar is correct", {
#
#   est <- "mle"
#   D0 <- MGamma(shape = 1:3, scale = 4)
#   d <- test_avar(est, D0)
#   expect_equal(d$avar_true, d$avar_est, tolerance = 0.5)
#
# })
#
# test_that("small_metrics works", {
#
#   D <- MGamma(shape = 1:3, scale = 2)
#
#   prm <- list(name = "shape",
#               pos = 2,
#               val = seq(0.5, 2, by = 0.5))
#
#   expect_no_error(
#     x <- small_metrics(D, prm,
#                        est = c("mle", "me", "same"),
#                        obs = c(20, 50),
#                        sam = 1e2,
#                        seed = 1)
#   )
#   expect_s3_class(x, "data.frame")
#
#   expect_no_error(
#     plot_small_metrics(x,
#                        save = TRUE,
#                        path = tempdir())
#   )
#
# })
#
# test_that("large_metrics works", {
#
#   D <- MGamma(shape = 1:3, scale = 2)
#
#   prm <- list(name = "shape",
#               pos = 2,
#               val = seq(0.5, 2, by = 0.5))
#
#   expect_no_error(
#     x <- large_metrics(D, prm,
#                        est = c("mle", "me", "same"))
#   )
#
#   expect_s3_class(x, "data.frame")
#
#   expect_no_error(
#     plot_large_metrics(x,
#                        save = TRUE,
#                        path = tempdir())
#   )
#
# })