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-rw-r--r--tests/testthat/AIC_exp_d_3.out0
-rw-r--r--tests/testthat/FOCUS_2006_D.csf2
-rw-r--r--tests/testthat/test_error_models.R115
3 files changed, 41 insertions, 76 deletions
diff --git a/tests/testthat/AIC_exp_d_3.out b/tests/testthat/AIC_exp_d_3.out
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/tests/testthat/AIC_exp_d_3.out
diff --git a/tests/testthat/FOCUS_2006_D.csf b/tests/testthat/FOCUS_2006_D.csf
index da2e2fbe..43215ec4 100644
--- a/tests/testthat/FOCUS_2006_D.csf
+++ b/tests/testthat/FOCUS_2006_D.csf
@@ -5,7 +5,7 @@ Description:
MeasurementUnits: % AR
TimeUnits: days
Comments: Created using mkin::CAKE_export
-Date: 2019-05-08
+Date: 2019-06-04
Optimiser: IRLS
[Data]
diff --git a/tests/testthat/test_error_models.R b/tests/testthat/test_error_models.R
index c656f7d2..6bb93d48 100644
--- a/tests/testthat/test_error_models.R
+++ b/tests/testthat/test_error_models.R
@@ -178,84 +178,49 @@ test_that("Reweighting method 'tc' produces reasonable variance estimates", {
expect_true(all(abs(tcf_met_2_15_tc_error_model_errors) < 0.10))
})
-test_that("The two-component error model finds the best known AIC values for parent models", {
+test_that("The different error model fitting methods work for parent fits", {
skip_on_cran()
- library(parallel)
- source("~/git/mkin/R/mkinfit.R")
- source("~/git/mkin/R/mmkin.R")
- f_9 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data)
- f_9 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
- error_model = "tc", error_model_algorithm = "direct")
- f_9 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
- error_model = "tc", error_model_algorithm = "twostep")
- f_9 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
- error_model = "tc", error_model_algorithm = "threestep")
- f_9 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
- error_model = "tc", error_model_algorithm = "fourstep")
- f_9 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
- error_model = "tc", error_model_algorithm = "IRLS")
- f_9 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
- error_model = "tc", error_model_algorithm = "d_3")
- AIC(f_9)
- f_10 <- mkinfit("DFOP", experimental_data_for_UBA_2019[[10]]$data,
- error_model = "tc", error_model_algorithm = "IRLS")
- f_tc_exp_direct <- mmkin(c("SFO", "DFOP", "HS"),
- lapply(experimental_data_for_UBA_2019, function(x) x$data),
- error_model = "tc",
- error_model_algorithm = "direct",
- quiet = TRUE)
- f_tc_exp_twostep <- mmkin(c("SFO", "DFOP", "HS"),
- lapply(experimental_data_for_UBA_2019, function(x) x$data),
- error_model = "tc",
- error_model_algorithm = "twostep",
- quiet = TRUE)
- f_tc_exp_threestep <- mmkin(c("SFO", "DFOP", "HS"),
- lapply(experimental_data_for_UBA_2019, function(x) x$data),
- error_model = "tc",
- error_model_algorithm = "threestep",
- quiet = TRUE)
- f_tc_exp_fourstep <- mmkin(c("SFO", "DFOP", "HS"),
- lapply(experimental_data_for_UBA_2019, function(x) x$data),
- error_model = "tc",
- error_model_algorithm = "fourstep",
- quiet = TRUE)
- f_tc_exp_IRLS <- mmkin(c("SFO", "DFOP", "HS"),
+
+ f_9_OLS <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
+ quiet = TRUE)
+ expect_equivalent(round(AIC(f_9_OLS), 2), 137.43)
+
+ f_9_direct <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
+ error_model = "tc", error_model_algorithm = "direct", quiet = TRUE)
+ expect_equivalent(round(AIC(f_9_direct), 2), 134.94)
+
+ f_9_twostep <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
+ error_model = "tc", error_model_algorithm = "twostep", quiet = TRUE)
+ expect_equivalent(round(AIC(f_9_twostep), 2), 134.94)
+
+ f_9_threestep <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
+ error_model = "tc", error_model_algorithm = "threestep", quiet = TRUE)
+ expect_equivalent(round(AIC(f_9_threestep), 2), 139.43)
+
+ f_9_fourstep <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
+ error_model = "tc", error_model_algorithm = "fourstep", quiet = TRUE)
+ expect_equivalent(round(AIC(f_9_fourstep), 2), 139.43)
+
+ f_9_IRLS <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
+ error_model = "tc", error_model_algorithm = "IRLS", quiet = TRUE)
+ expect_equivalent(round(AIC(f_9_IRLS), 2), 139.43)
+
+ f_9_d_3 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
+ error_model = "tc", error_model_algorithm = "d_3", quiet = TRUE)
+ expect_equivalent(round(AIC(f_9_d_3), 2), 134.94)
+})
+
+test_that("The default error model algorithm finds the best known AIC values for parent fits", {
+ f_tc_exp_d_3 <- mmkin(c("SFO", "DFOP", "HS"),
lapply(experimental_data_for_UBA_2019, function(x) x$data),
error_model = "tc",
- error_model_algorithm = "IRLS",
+ error_model_algorithm = "d_3",
quiet = TRUE)
- AIC_exp_direct <- lapply(f_tc_exp_direct, AIC)
- AIC_exp_direct <- lapply(AIC_exp_direct, round, 1)
- dim(AIC_exp_direct) <- dim(f_tc_exp_direct)
- dimnames(AIC_exp_direct) <- dimnames(f_tc_exp_direct)
-
- AIC_exp_twostep <- lapply(f_tc_exp_twostep, AIC)
- AIC_exp_twostep <- lapply(AIC_exp_twostep, round, 1)
- dim(AIC_exp_twostep) <- dim(f_tc_exp_twostep)
- dimnames(AIC_exp_twostep) <- dimnames(f_tc_exp_twostep)
-
- AIC_exp_threestep <- lapply(f_tc_exp_threestep, AIC)
- AIC_exp_threestep <- lapply(AIC_exp_threestep, round, 1)
- dim(AIC_exp_threestep) <- dim(f_tc_exp_threestep)
- dimnames(AIC_exp_threestep) <- dimnames(f_tc_exp_threestep)
-
- AIC_exp_fourstep <- lapply(f_tc_exp_fourstep, AIC)
- AIC_exp_fourstep <- lapply(AIC_exp_fourstep, round, 1)
- dim(AIC_exp_fourstep) <- dim(f_tc_exp_fourstep)
- dimnames(AIC_exp_fourstep) <- dimnames(f_tc_exp_fourstep)
-
- AIC_exp_IRLS <- lapply(f_tc_exp_IRLS, AIC)
- AIC_exp_IRLS <- lapply(AIC_exp_IRLS, round, 1)
- dim(AIC_exp_IRLS) <- dim(f_tc_exp_IRLS)
- dimnames(AIC_exp_IRLS) <- dimnames(f_tc_exp_IRLS)
-
- AIC_exp <- lapply(f_tc_exp, AIC)
- dim(AIC_exp) <- dim(f_tc_exp)
- dimnames(AIC_exp) <- dimnames(f_tc_exp)
- unlist(AIC_exp["SFO", c(9, 11, 12)])
- expect_equivalent(round(unlist(AIC_exp["SFO", c(9, 11, 12)]), 1),
- c(134.9, 125.5, 82.0))
-})
-
+ AIC_exp_d_3 <- lapply(f_tc_exp_d_3, AIC)
+ AIC_exp_d_3 <- lapply(AIC_exp_d_3, round, 1)
+ dim(AIC_exp_d_3) <- dim(f_tc_exp_d_3)
+ dimnames(AIC_exp_d_3) <- dimnames(f_tc_exp_d_3)
+ expect_known_output(AIC_exp_d_3, "AIC_exp_d_3.out")
+})

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