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authorJohannes Ranke <jranke@uni-bremen.de>2019-06-04 11:15:52 +0200
committerJohannes Ranke <jranke@uni-bremen.de>2019-06-04 11:18:56 +0200
commit9a96391589fef9f80f9c6c4881cc48a509cb75f2 (patch)
tree52fe94f8f50d1e20f10573e5fdc0308a5fcc4a69 /tests/testthat/test_error_models.R
parentb6ea4f22fc1b6d1caea29f6b1e44774d14d6697c (diff)
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
Diffstat (limited to 'tests/testthat/test_error_models.R')
-rw-r--r--tests/testthat/test_error_models.R50
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))
})

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