tests/testthat/test-single_continuous_mle.R

In ToxicR: Analyzing Toxicology Dose-Response Data

context("Single Continuous Models MLE")

test_that("Normal Ewald Hill", {
     set.seed(5981)
     M2 <- build_single_continuous_dataset()
     c = single_continuous_fit(M2[,1,drop=F],M2[,2:4],BMD_TYPE="sd",BMR=1, ewald = T,
                               distribution = "normal",fit_type="mle",model_type = "hill",degree = 4)
     validate_model(c, "Model: Hill Distribution: Normal", c(6.15, -5.33, 39.89, 3.12, -0.15), c(24.01, 20.12, 28.66))
})

test_that("Normal Ewald exp-3", {
     #set.seed(5981)
     set.seed(5983)
     M2 <- build_single_continuous_dataset()
     c = single_continuous_fit(M2[,1,drop=F],M2[,2:4],BMD_TYPE="sd",BMR=1, ewald = T,
                               distribution = "normal",fit_type="mle",model_type = "exp-3",degree = 4)
     validate_model(c, "Model: Exponential-3 Distribution: Normal", c(6.166, 0.0161, 1.43, 0.00217), c(18.86, 13.949, 25.51))
})

test_that("Normal Ewald exp-5", {
     set.seed(5981)
     M2 <- build_single_continuous_dataset()
     c = single_continuous_fit(M2[,1,drop=F],M2[,2:4],BMD_TYPE="sd",BMR=1, ewald = T,
                               distribution = "normal",fit_type="mle",model_type = "exp-5",degree = 4)
     validate_model(c, "Model: Exponential-5 Distribution: Normal", c(5.99, 0.02, -1.89, 2.487, -0.17), c(25.2, 20.3, 31.4))
})

test_that("Normal Ewald power", {
     set.seed(5981)
     M2 <- build_single_continuous_dataset()
     c = single_continuous_fit(M2[,1,drop=F],M2[,2:4],BMD_TYPE="sd",BMR=1, ewald = T,
                               distribution = "normal",fit_type="mle",model_type = "power",degree = 4)
     validate_model(c, "Model: Power Distribution: Normal", c(6.077, -0.380, 0.517, 0.339), c(7.16, 3.60, 14.23))
})

test_that("Normal Ewald polynomial", {
     set.seed(5981)
     M2 <- build_single_continuous_dataset()
     c = single_continuous_fit(M2[,1,drop=F],M2[,2:4],BMD_TYPE="sd",BMR=1, ewald = T,
                               distribution = "normal",fit_type="mle",model_type = "polynomial",degree = 4)
     validate_model(c, "Model: Polynomial Distribution: Normal", c(5.203563e+00, -1.220068e-02, -8.294745e-05,
                                                                   -4.788429e-06, 2.476356e-08, 6.817159e-01), c(53.2, 22.9, 123.7))
})

test_that("Plots", {
        set.seed(5981)
        M2 <- build_single_continuous_dataset()
        c = single_continuous_fit(M2[,1,drop=F],M2[,2:4],BMD_TYPE="sd",BMR=1, ewald = T,
                                  distribution = "normal",fit_type="mle",model_type = "polynomial",degree = 4)
        mle_plot <- plot(c)
        expect_identical(mle_plot$labels$x, "Dose")
        expect_identical(mle_plot$labels$y, "Response")
        expect_identical(mle_plot$labels$title, "Model: Polynomial Distribution: Normal,  Fit Type: Maximized")
        c = single_continuous_fit(M2[,1,drop=F],M2[,2:4],BMD_TYPE="sd",BMR=1, ewald = T,
                                  distribution = "normal",fit_type="mle",model_type = "hill",degree = 4)
        mle_plot <- plot(c)
        expect_identical(mle_plot$labels$x, "Dose")
        expect_identical(mle_plot$labels$y, "Response")
        expect_identical(mle_plot$labels$title, "Model: Hill Distribution: Normal,  Fit Type: Maximized")
})