diff options
Diffstat (limited to 'tests')
| -rw-r--r-- | tests/testthat/DFOP_FOCUS_C_messages.txt | 2 | ||||
| -rw-r--r-- | tests/testthat/FOCUS_2006_D.csf | 2 | ||||
| -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.R | 141 | ||||
| -rw-r--r-- | tests/testthat/summary_DFOP_FOCUS_C.txt | 12 | ||||
| -rw-r--r-- | tests/testthat/test_confidence.R | 51 | ||||
| -rw-r--r-- | tests/testthat/test_error_models.R | 138 |
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() |