Algorithms direct, two-, three-, fourstep, IRLS

All of them are working now and allow for comparison
Based on SFO, DFOP and HS fits to twelve test datasets, only
the combination of direct and threestep is needed to find
the lowest AIC

1 files changed, 45 insertions, 5 deletions

diff --git a/tests/testthat/test_error_models.R b/tests/testthat/test_error_models.R
index 94703e7f..c656f7d2 100644
--- a/tests/testthat/test_error_models.R
+++ b/tests/testthat/test_error_models.R
@@ -180,7 +180,6 @@ test_that("Reweighting method 'tc' produces reasonable variance estimates", {
 
 test_that("The two-component error model finds the best known AIC values for parent models", {
   skip_on_cran()
-  experimental_data_for_UBA_2019
   library(parallel)
   source("~/git/mkin/R/mkinfit.R")
   source("~/git/mkin/R/mmkin.R")
@@ -195,27 +194,68 @@ test_that("The two-component error model finds the best known AIC values for par
                  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_tc_exp <- mmkin(c("SFO"),
+  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 <- mmkin(c("SFO"),
+  f_tc_exp_twostep <- mmkin(c("SFO", "DFOP", "HS"),
     lapply(experimental_data_for_UBA_2019, function(x) x$data),
     error_model = "tc",
     error_model_algorithm = "twostep",
     quiet = TRUE)
-  f_tc_exp <- mmkin(c("SFO"),
+  f_tc_exp_threestep <- mmkin(c("SFO", "DFOP", "HS"),
     lapply(experimental_data_for_UBA_2019, function(x) x$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$data),
+    error_model = "tc",
+    error_model_algorithm = "fourstep",
+    quiet = TRUE)
+  f_tc_exp_IRLS <- mmkin(c("SFO", "DFOP", "HS"),
+    lapply(experimental_data_for_UBA_2019, function(x) x$data),
+    error_model = "tc",
+    error_model_algorithm = "IRLS",
+    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)
-  expect_equivalent(round(AIC_exp["SFO", c(9, 11, 12)], 1), c(134.9, 125.5, 82.0))
+  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))
 })