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Diffstat (limited to 'tests/testthat/test_error_models.R')
| -rw-r--r-- | tests/testthat/test_error_models.R | 178 |
1 files changed, 178 insertions, 0 deletions
diff --git a/tests/testthat/test_error_models.R b/tests/testthat/test_error_models.R new file mode 100644 index 00000000..bda8ca7f --- /dev/null +++ b/tests/testthat/test_error_models.R @@ -0,0 +1,178 @@ +# Copyright (C) 2018 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("Error model fitting") + +m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"), + M1 = mkinsub("SFO", "M2"), + M2 = mkinsub("SFO"), + use_of_ff = "max", quiet = TRUE) + +m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")), + M1 = mkinsub("SFO"), + M2 = mkinsub("SFO"), + use_of_ff = "max", quiet = TRUE) + +SFO_lin_a <- synthetic_data_for_UBA_2014[[1]]$data + +DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$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)$bpar[, c("Estimate", "Lower", "Upper")] + # The reference used here is mkin 0.9.48.1 + bpar_1_mkin_0.9 <- read.table(text = +"parent_0 102.0000 98.6000 106.0000 +k_parent 0.7390 0.6770 0.8070 +k_M1 0.2990 0.2560 0.3490 +k_M2 0.0202 0.0176 0.0233 +f_parent_to_M1 0.7690 0.6640 0.8480 +f_M1_to_M2 0.7230 0.6030 0.8180", +col.names = c("parameter", "estimate", "lower", "upper")) + + expect_equivalent(signif(bpar_1[1:6, "Estimate"], 3), bpar_1_mkin_0.9$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$lower), + rep(0, 6), tolerance = 0.02) +}) + +test_that("Error model 'obs' works", { + skip_on_cran() + fit_obs_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "obs", quiet = TRUE) + parms_2 <- round(fit_obs_1$bparms.optim, c(1, 4, 4, 4, 4, 4)) + expect_equivalent(parms_2, c(102.1, 0.7389, 0.2982, 0.0203, 0.7677, 0.7246)) +}) + +test_that("Error model 'tc' works", { + skip_on_cran() + fit_tc_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "tc", quiet = TRUE) + parms_3 <- round(fit_tc_1$bparms.optim, c(1, 4, 4, 4, 4, 4)) + expect_equivalent(parms_3, c(102.1, 0.7393, 0.2992, 0.0202, 0.7687, 0.7229)) +}) + +test_that("Error model 'obs_tc' works", { + skip_on_cran() + fit_obs_tc_1 <- expect_warning(mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "obs_tc", quiet = TRUE), "NaN") + # Here the error model is overparameterised + expect_warning(summary(fit_obs_tc_1), "singular system") +}) + +test_that("Reweighting method 'tc' produces reasonable variance estimates", { + + # I need to make the tc method more robust against that + # skip_on_cran() + + # Check if we can approximately obtain the parameters and the error model + # components that were used in the data generation + + # Parent only + DFOP <- mkinmod(parent = mkinsub("DFOP")) + sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) + parms_DFOP <- c(k1 = 0.2, k2 = 0.02, g = 0.5) + parms_DFOP_optim <- c(parent_0 = 100, parms_DFOP) + + d_DFOP <- mkinpredict(DFOP, + parms_DFOP, c(parent = 100), + sampling_times) + d_2_10 <- add_err(d_DFOP, + sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07), + n = 10, reps = 2, digits = 5, LOD = -Inf, seed = 123456) + d_100_1 <- add_err(d_DFOP, + sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07), + n = 1, reps = 100, digits = 5, LOD = -Inf, seed = 123456) + + # Unweighted fits + f_2_10 <- mmkin("DFOP", d_2_10, error_model = "const", quiet = TRUE, + cores = if (Sys.getenv("TRAVIS") != "") 1 else 15) + parms_2_10 <- apply(sapply(f_2_10, function(x) x$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 = if (Sys.getenv("TRAVIS") != "") 1 else 15) + 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 = -Inf, 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") + parm_errors_met_2_tc_e4 <- (f_met_2_tc_e4$errparms - c(0.5, 0.07)) / c(0.5, 0.07) + expect_true(all(abs(parm_errors_met_2_tc_e4) < 0.1)) + + # Doing more takes a lot of computing power + skip_on_travis() + f_met_2_15_tc_e4 <- mmkin(list(m_synth_DFOP_lin), d_met_2_15, quiet = TRUE, + error_model = "tc", cores = 15) + + parms_met_2_15_tc_e4 <- apply(sapply(f_met_2_15_tc_e4, function(x) x$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) < 0.01)) + + 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 < 15% for retrieving the original error model components + # from 15 datasets + expect_true(all(abs(tcf_met_2_15_tc_error_model_errors) < 0.15)) +}) |