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-rw-r--r--tests/testthat/DFOP_FOCUS_C_messages.txt2
-rw-r--r--tests/testthat/FOCUS_2006_D.csf2
-rw-r--r--tests/testthat/slow/test_parent_only.R (renamed from tests/testthat/test_parent_only.R)0
-rw-r--r--tests/testthat/slow/test_roundtrip_error_parameters.R141
-rw-r--r--tests/testthat/summary_DFOP_FOCUS_C.txt12
-rw-r--r--tests/testthat/test_confidence.R51
-rw-r--r--tests/testthat/test_error_models.R138
7 files changed, 209 insertions, 137 deletions
diff --git a/tests/testthat/DFOP_FOCUS_C_messages.txt b/tests/testthat/DFOP_FOCUS_C_messages.txt
index d3d7688b..78438d06 100644
--- a/tests/testthat/DFOP_FOCUS_C_messages.txt
+++ b/tests/testthat/DFOP_FOCUS_C_messages.txt
@@ -1,4 +1,4 @@
-parent_0 log_k1 log_k2 g_ilr sigma
+parent_0 log_k1 log_k2 g_ilr
85.1 -2.302585 -4.60517 0
Sum of squared residuals at call 1: 7391.39
85.1 -2.302585 -4.60517 0
diff --git a/tests/testthat/FOCUS_2006_D.csf b/tests/testthat/FOCUS_2006_D.csf
index f9233770..171abbb0 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: 2019-07-08
+Date: 2019-10-21
Optimiser: IRLS
[Data]
diff --git a/tests/testthat/test_parent_only.R b/tests/testthat/slow/test_parent_only.R
index 7521e145..7521e145 100644
--- a/tests/testthat/test_parent_only.R
+++ b/tests/testthat/slow/test_parent_only.R
diff --git a/tests/testthat/slow/test_roundtrip_error_parameters.R b/tests/testthat/slow/test_roundtrip_error_parameters.R
new file mode 100644
index 00000000..97510563
--- /dev/null
+++ b/tests/testthat/slow/test_roundtrip_error_parameters.R
@@ -0,0 +1,141 @@
+test_that("Reweighting method 'tc' produces reasonable variance estimates", {
+
+ # Check if we can approximately obtain the parameters and the error model
+ # components that were used in the data generation
+
+ # Parent only
+ DFOP <- mkinmod(parent = mkinsub("DFOP"))
+ sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+ parms_DFOP <- c(k1 = 0.2, k2 = 0.02, g = 0.5)
+ parms_DFOP_optim <- c(parent_0 = 100, parms_DFOP)
+
+ d_DFOP <- mkinpredict(DFOP,
+ parms_DFOP, c(parent = 100),
+ sampling_times)
+ d_2_10 <- add_err(d_DFOP,
+ sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
+ n = 10, reps = 2, digits = 5, LOD = -Inf, seed = 123456)
+ d_100_1 <- add_err(d_DFOP,
+ sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
+ n = 1, reps = 100, digits = 5, LOD = -Inf, seed = 123456)
+
+ # Per default (on my box where I set NOT_CRAN) use all cores minus one
+ if (identical(Sys.getenv("NOT_CRAN"), "true")) {
+ n_cores <- parallel::detectCores() - 1
+ } else {
+ n_cores <- 1
+ }
+
+ # We are only allowed one core on travis, but they also set NOT_CRAN=true
+ if (Sys.getenv("TRAVIS") != "") n_cores = 1
+
+ # On Windows we would need to make a cluster first
+ if (Sys.info()["sysname"] == "Windows") n_cores = 1
+
+ # Unweighted fits
+ f_2_10 <- mmkin("DFOP", d_2_10, error_model = "const", quiet = TRUE,
+ cores = n_cores)
+ parms_2_10 <- apply(sapply(f_2_10, function(x) x$bparms.optim), 1, mean)
+ parm_errors_2_10 <- (parms_2_10 - parms_DFOP_optim) / parms_DFOP_optim
+ expect_true(all(abs(parm_errors_2_10) < 0.12))
+
+ f_2_10_tc <- mmkin("DFOP", d_2_10, error_model = "tc", quiet = TRUE,
+ cores = n_cores)
+ parms_2_10_tc <- apply(sapply(f_2_10_tc, function(x) x$bparms.optim), 1, mean)
+ parm_errors_2_10_tc <- (parms_2_10_tc - parms_DFOP_optim) / parms_DFOP_optim
+ expect_true(all(abs(parm_errors_2_10_tc) < 0.05))
+
+ tcf_2_10_tc <- apply(sapply(f_2_10_tc, function(x) x$errparms), 1, mean, na.rm = TRUE)
+
+ tcf_2_10_error_model_errors <- (tcf_2_10_tc - c(0.5, 0.07)) / c(0.5, 0.07)
+ expect_true(all(abs(tcf_2_10_error_model_errors) < 0.2))
+
+ # When we have 100 replicates in the synthetic data, we can roundtrip
+ # the parameters with < 2% precision
+ f_tc_100_1 <- mkinfit(DFOP, d_100_1[[1]], error_model = "tc", quiet = TRUE)
+ parm_errors_100_1 <- (f_tc_100_1$bparms.optim - parms_DFOP_optim) / parms_DFOP_optim
+ expect_true(all(abs(parm_errors_100_1) < 0.02))
+
+ tcf_100_1_error_model_errors <- (f_tc_100_1$errparms - c(0.5, 0.07)) /
+ c(0.5, 0.07)
+ # We also get a precision of < 2% for the error model components
+ expect_true(all(abs(tcf_100_1_error_model_errors) < 0.02))
+
+ # Parent and two metabolites
+ m_synth_DFOP_lin <- mkinmod(parent = list(type = "DFOP", to = "M1"),
+ M1 = list(type = "SFO", to = "M2"),
+ M2 = list(type = "SFO"), use_of_ff = "max",
+ quiet = TRUE)
+ sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+ parms_DFOP_lin <- c(k1 = 0.2, k2 = 0.02, g = 0.5,
+ f_parent_to_M1 = 0.5, k_M1 = 0.3,
+ f_M1_to_M2 = 0.7, k_M2 = 0.02)
+ d_synth_DFOP_lin <- mkinpredict(m_synth_DFOP_lin,
+ parms_DFOP_lin,
+ c(parent = 100, M1 = 0, M2 = 0),
+ sampling_times)
+ parms_DFOP_lin_optim = c(parent_0 = 100, parms_DFOP_lin)
+
+ d_met_2_15 <- add_err(d_synth_DFOP_lin,
+ sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
+ n = 15, reps = 100, digits = 5, LOD = 0.01, seed = 123456)
+
+ # For a single fit, we get a relative error of less than 5% in the error
+ # model components
+ f_met_2_tc_e4 <- mkinfit(m_synth_DFOP_lin, d_met_2_15[[1]], quiet = TRUE,
+ error_model = "tc", error_model_algorithm = "direct")
+ parm_errors_met_2_tc_e4 <- (f_met_2_tc_e4$errparms - c(0.5, 0.07)) / c(0.5, 0.07)
+ expect_true(all(abs(parm_errors_met_2_tc_e4) < 0.05))
+
+ # Doing more takes a lot of computing power
+ skip_on_travis()
+ skip_on_cran()
+ f_met_2_15_tc_e4 <- mmkin(list(m_synth_DFOP_lin), d_met_2_15, quiet = TRUE,
+ error_model = "tc", cores = n_cores)
+
+ parms_met_2_15_tc_e4 <- apply(sapply(f_met_2_15_tc_e4, function(x) x$bparms.optim), 1, mean)
+ parm_errors_met_2_15_tc_e4 <- (parms_met_2_15_tc_e4[names(parms_DFOP_lin_optim)] -
+ parms_DFOP_lin_optim) / parms_DFOP_lin_optim
+ expect_true(all(abs(parm_errors_met_2_15_tc_e4) < 0.015))
+
+ tcf_met_2_15_tc <- apply(sapply(f_met_2_15_tc_e4, function(x) x$errparms), 1, mean, na.rm = TRUE)
+
+ tcf_met_2_15_tc_error_model_errors <- (tcf_met_2_15_tc - c(0.5, 0.07)) /
+ c(0.5, 0.07)
+
+ # Here we get a precision < 10% for retrieving the original error model components
+ # from 15 datasets
+ expect_true(all(abs(tcf_met_2_15_tc_error_model_errors) < 0.10))
+})
+
+test_that("The different error model fitting methods work for parent fits", {
+ skip_on_cran()
+
+ 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)
+})
diff --git a/tests/testthat/summary_DFOP_FOCUS_C.txt b/tests/testthat/summary_DFOP_FOCUS_C.txt
index b1afeff6..90ce82e2 100644
--- a/tests/testthat/summary_DFOP_FOCUS_C.txt
+++ b/tests/testthat/summary_DFOP_FOCUS_C.txt
@@ -17,12 +17,11 @@ Error model: Constant variance
Error model algorithm: OLS
Starting values for parameters to be optimised:
- value type
-parent_0 85.100000 state
-k1 0.100000 deparm
-k2 0.010000 deparm
-g 0.500000 deparm
-sigma 0.696237 error
+ value type
+parent_0 85.10 state
+k1 0.10 deparm
+k2 0.01 deparm
+g 0.50 deparm
Starting values for the transformed parameters actually optimised:
value lower upper
@@ -30,7 +29,6 @@ parent_0 85.100000 -Inf Inf
log_k1 -2.302585 -Inf Inf
log_k2 -4.605170 -Inf Inf
g_ilr 0.000000 -Inf Inf
-sigma 0.696237 0 Inf
Fixed parameter values:
None
diff --git a/tests/testthat/test_confidence.R b/tests/testthat/test_confidence.R
new file mode 100644
index 00000000..e5cc1954
--- /dev/null
+++ b/tests/testthat/test_confidence.R
@@ -0,0 +1,51 @@
+# Copyright (C) 2019 Johannes Ranke
+# Contact: jranke@uni-bremen.de
+
+# This file is part of the R package mkin
+
+# mkin is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+# details.
+
+# You should have received a copy of the GNU General Public License along with
+# this program. If not, see <http://www.gnu.org/licenses/>
+
+context("Confidence intervals and p-values")
+
+m_synth_SFO_lin <- mkinmod(
+ parent = mkinsub("SFO", "M1"),
+ M1 = mkinsub("SFO", "M2"),
+ M2 = mkinsub("SFO"),
+ use_of_ff = "max", quiet = TRUE)
+
+SFO_lin_a <- synthetic_data_for_UBA_2014[[1]]$data
+
+test_that("Confidence intervals are stable", {
+ f_1_mkin_OLS <- mkinfit(m_synth_SFO_lin, SFO_lin_a, quiet = TRUE)
+ f_1_mkin_ML <- mkinfit(m_synth_SFO_lin, SFO_lin_a, quiet = TRUE,
+ error_model = "const", error_model_algorithm = "direct")
+
+ bpar_1 <- summary(f_1_mkin_ML)$bpar[, c("Estimate", "Lower", "Upper")]
+ # The reference used here is mkin 0.9.48.1
+ bpar_1_mkin_0.9 <- read.table(text =
+"parent_0 102.0000 98.6000 106.0000
+k_parent 0.7390 0.6770 0.8070
+k_M1 0.2990 0.2560 0.3490
+k_M2 0.0202 0.0176 0.0233
+f_parent_to_M1 0.7690 0.6640 0.8480
+f_M1_to_M2 0.7230 0.6030 0.8180",
+col.names = c("parameter", "estimate", "lower", "upper"))
+
+ expect_equivalent(signif(bpar_1[1:6, "Estimate"], 3), bpar_1_mkin_0.9$estimate)
+
+ # Relative difference of lower bound of the confidence interval is < 0.02
+ expect_equivalent(bpar_1[1:6, "Lower"], bpar_1_mkin_0.9$lower,
+ scale = bpar_1_mkin_0.9$lower, tolerance = 0.02)
+ })
+
diff --git a/tests/testthat/test_error_models.R b/tests/testthat/test_error_models.R
index fbae6286..f4015e00 100644
--- a/tests/testthat/test_error_models.R
+++ b/tests/testthat/test_error_models.R
@@ -35,25 +35,18 @@ DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data
test_that("Error model 'const' works", {
skip_on_cran()
fit_const_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "const", quiet = TRUE)
- bpar_1 <- summary(fit_const_1)$bpar[, c("Estimate", "Lower", "Upper")]
+ bpar_1 <- fit_const_1$bparms.optim
# The reference used here is mkin 0.9.48.1
bpar_1_mkin_0.9 <- read.table(text =
-"parent_0 102.0000 98.6000 106.0000
-k_parent 0.7390 0.6770 0.8070
-k_M1 0.2990 0.2560 0.3490
-k_M2 0.0202 0.0176 0.0233
-f_parent_to_M1 0.7690 0.6640 0.8480
-f_M1_to_M2 0.7230 0.6030 0.8180",
-col.names = c("parameter", "estimate", "lower", "upper"))
-
- expect_equivalent(signif(bpar_1[1:6, "Estimate"], 3), bpar_1_mkin_0.9$estimate)
- # Relative difference of lower bound of confidence is < 0.02
- rel_diff <- function(v1, v2) {
- (v1 - v2)/v2
- }
- expect_equivalent(rel_diff(bpar_1[1:6, "Lower"],
- bpar_1_mkin_0.9$lower),
- rep(0, 6), tolerance = 0.02)
+"parent_0 102.0000
+k_parent 0.7390
+k_M1 0.2990
+k_M2 0.0202
+f_parent_to_M1 0.7690
+f_M1_to_M2 0.7230",
+col.names = c("parameter", "estimate"))
+
+ expect_equivalent(signif(bpar_1, 3), bpar_1_mkin_0.9$estimate)
})
test_that("Error model 'obs' works", {
@@ -70,117 +63,6 @@ test_that("Error model 'tc' works", {
expect_equivalent(parms_3, c(102.1, 0.7393, 0.2992, 0.0202, 0.7687, 0.7229))
})
-test_that("Reweighting method 'tc' produces reasonable variance estimates", {
-
- # Check if we can approximately obtain the parameters and the error model
- # components that were used in the data generation
-
- # Parent only
- DFOP <- mkinmod(parent = mkinsub("DFOP"))
- sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
- parms_DFOP <- c(k1 = 0.2, k2 = 0.02, g = 0.5)
- parms_DFOP_optim <- c(parent_0 = 100, parms_DFOP)
-
- d_DFOP <- mkinpredict(DFOP,
- parms_DFOP, c(parent = 100),
- sampling_times)
- d_2_10 <- add_err(d_DFOP,
- sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
- n = 10, reps = 2, digits = 5, LOD = -Inf, seed = 123456)
- d_100_1 <- add_err(d_DFOP,
- sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
- n = 1, reps = 100, digits = 5, LOD = -Inf, seed = 123456)
-
- # Per default (on my box where I set NOT_CRAN) use all cores minus one
- if (identical(Sys.getenv("NOT_CRAN"), "true")) {
- n_cores <- parallel::detectCores() - 1
- } else {
- n_cores <- 1
- }
-
- # We are only allowed one core on travis, but they also set NOT_CRAN=true
- if (Sys.getenv("TRAVIS") != "") n_cores = 1
-
- # On Windows we would need to make a cluster first
- if (Sys.info()["sysname"] == "Windows") n_cores = 1
-
- # Unweighted fits
- f_2_10 <- mmkin("DFOP", d_2_10, error_model = "const", quiet = TRUE,
- cores = n_cores)
- parms_2_10 <- apply(sapply(f_2_10, function(x) x$bparms.optim), 1, mean)
- parm_errors_2_10 <- (parms_2_10 - parms_DFOP_optim) / parms_DFOP_optim
- expect_true(all(abs(parm_errors_2_10) < 0.12))
-
- f_2_10_tc <- mmkin("DFOP", d_2_10, error_model = "tc", quiet = TRUE,
- cores = n_cores)
- parms_2_10_tc <- apply(sapply(f_2_10_tc, function(x) x$bparms.optim), 1, mean)
- parm_errors_2_10_tc <- (parms_2_10_tc - parms_DFOP_optim) / parms_DFOP_optim
- expect_true(all(abs(parm_errors_2_10_tc) < 0.05))
-
- tcf_2_10_tc <- apply(sapply(f_2_10_tc, function(x) x$errparms), 1, mean, na.rm = TRUE)
-
- tcf_2_10_error_model_errors <- (tcf_2_10_tc - c(0.5, 0.07)) / c(0.5, 0.07)
- expect_true(all(abs(tcf_2_10_error_model_errors) < 0.2))
-
- # When we have 100 replicates in the synthetic data, we can roundtrip
- # the parameters with < 2% precision
- f_tc_100_1 <- mkinfit(DFOP, d_100_1[[1]], error_model = "tc", quiet = TRUE)
- parm_errors_100_1 <- (f_tc_100_1$bparms.optim - parms_DFOP_optim) / parms_DFOP_optim
- expect_true(all(abs(parm_errors_100_1) < 0.02))
-
- tcf_100_1_error_model_errors <- (f_tc_100_1$errparms - c(0.5, 0.07)) /
- c(0.5, 0.07)
- # When maximising the likelihood directly (not using IRLS), we get
- # a precision of < 2% for the error model componentes as well
- expect_true(all(abs(tcf_100_1_error_model_errors) < 0.02))
-
- # Parent and two metabolites
- m_synth_DFOP_lin <- mkinmod(parent = list(type = "DFOP", to = "M1"),
- M1 = list(type = "SFO", to = "M2"),
- M2 = list(type = "SFO"), use_of_ff = "max",
- quiet = TRUE)
- sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
- parms_DFOP_lin <- c(k1 = 0.2, k2 = 0.02, g = 0.5,
- f_parent_to_M1 = 0.5, k_M1 = 0.3,
- f_M1_to_M2 = 0.7, k_M2 = 0.02)
- d_synth_DFOP_lin <- mkinpredict(m_synth_DFOP_lin,
- parms_DFOP_lin,
- c(parent = 100, M1 = 0, M2 = 0),
- sampling_times)
- parms_DFOP_lin_optim = c(parent_0 = 100, parms_DFOP_lin)
-
- d_met_2_15 <- add_err(d_synth_DFOP_lin,
- sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
- n = 15, reps = 100, digits = 5, LOD = 0.01, seed = 123456)
-
- # For a single fit, we get a relative error of less than 10% in the error
- # model components
- f_met_2_tc_e4 <- mkinfit(m_synth_DFOP_lin, d_met_2_15[[1]], quiet = TRUE,
- error_model = "tc", error_model_algorithm = "direct")
- parm_errors_met_2_tc_e4 <- (f_met_2_tc_e4$errparms - c(0.5, 0.07)) / c(0.5, 0.07)
- expect_true(all(abs(parm_errors_met_2_tc_e4) < 0.1))
-
- # Doing more takes a lot of computing power
- skip_on_travis()
- skip_on_cran()
- f_met_2_15_tc_e4 <- mmkin(list(m_synth_DFOP_lin), d_met_2_15, quiet = TRUE,
- error_model = "tc", cores = n_cores)
-
- parms_met_2_15_tc_e4 <- apply(sapply(f_met_2_15_tc_e4, function(x) x$bparms.optim), 1, mean)
- parm_errors_met_2_15_tc_e4 <- (parms_met_2_15_tc_e4[names(parms_DFOP_lin_optim)] -
- parms_DFOP_lin_optim) / parms_DFOP_lin_optim
- expect_true(all(abs(parm_errors_met_2_15_tc_e4) < 0.015))
-
- tcf_met_2_15_tc <- apply(sapply(f_met_2_15_tc_e4, function(x) x$errparms), 1, mean, na.rm = TRUE)
-
- tcf_met_2_15_tc_error_model_errors <- (tcf_met_2_15_tc - c(0.5, 0.07)) /
- c(0.5, 0.07)
-
- # Here we get a precision < 10% for retrieving the original error model components
- # from 15 datasets
- expect_true(all(abs(tcf_met_2_15_tc_error_model_errors) < 0.10))
-})
-
test_that("The different error model fitting methods work for parent fits", {
skip_on_cran()

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