diff options
Diffstat (limited to 'tests/testthat/slow')
| -rw-r--r-- | tests/testthat/slow/test_parent_only.R | 218 | ||||
| -rw-r--r-- | tests/testthat/slow/test_roundtrip_error_parameters.R | 141 |
2 files changed, 359 insertions, 0 deletions
diff --git a/tests/testthat/slow/test_parent_only.R b/tests/testthat/slow/test_parent_only.R new file mode 100644 index 00000000..7521e145 --- /dev/null +++ b/tests/testthat/slow/test_parent_only.R @@ -0,0 +1,218 @@ +# Copyright (C) 2015,2018 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("Fitting of parent only models") + +calc_dev.percent <- function(fitlist, reference, endpoints = TRUE, round_results = NULL) { + dev.percent <- list() + for (i in 1:length(fitlist)) { + fit <- fitlist[[i]] + if (endpoints) { + results <- c(fit$bparms.optim, + endpoints(fit)$distimes$DT50, + endpoints(fit)$distimes$DT90) + } else { + results <- fit$bparms.optim + } + if (!missing(round_results)) results <- round(results, round_results) + dev.percent[[i]] <- abs(100 * ((reference - results)/reference)) + } + return(dev.percent) +} + +SFO <- mkinmod(parent = list(type = "SFO")) +FOMC <- mkinmod(parent = list(type = "FOMC")) +DFOP <- mkinmod(parent = list(type = "DFOP")) +HS <- mkinmod(parent = list(type = "HS")) +SFORB <- mkinmod(parent = list(type = "SFORB")) + +test_that("Fits for FOCUS A deviate less than 0.1% from median of values from FOCUS report", { + fit.A.SFO <- list(mkinfit("SFO", FOCUS_2006_A, quiet = TRUE)) + + median.A.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F, + dataset == "A", + c(M0, k, DT50, DT90)), "median")) + + dev.percent.A.SFO <- calc_dev.percent(fit.A.SFO, median.A.SFO) + expect_equivalent(dev.percent.A.SFO[[1]] < 0.1, rep(TRUE, 4)) + + # Fitting FOCUS A with FOMC is possible, but the correlation between + # alpha and beta, when obtained, is 1.0000, and the fit does not + # always converge using the Port algorithm (platform dependent), so + # we need to suppress a potential warning + suppressWarnings(fit.A.FOMC <- try(list(mkinfit("FOMC", FOCUS_2006_A, quiet = TRUE)))) + + if (!inherits(fit.A.FOMC, "try-error")) { + + median.A.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F, + dataset == "A", + c(M0, alpha, beta, DT50, DT90)), "median")) + + dev.percent.A.FOMC <- calc_dev.percent(fit.A.FOMC, median.A.FOMC) + # alpha and are beta ill-determined, do not compare those + expect_equivalent(dev.percent.A.FOMC[[1]][c(1, 4, 5)] < 0.1, rep(TRUE, 3)) + } + + fit.A.DFOP <- list(mkinfit("DFOP", FOCUS_2006_A, quiet = TRUE)) + + median.A.DFOP <- as.numeric(lapply(subset(FOCUS_2006_DFOP_ref_A_to_B, + dataset == "A", + c(M0, k1, k2, f, DT50, DT90)), "median")) + + dev.percent.A.DFOP <- calc_dev.percent(fit.A.DFOP, median.A.DFOP) + #expect_equivalent(dev.percent.A.DFOP[[1]] < 0.1, rep(TRUE, 6)) # g/f is ill-determined + expect_equivalent(dev.percent.A.DFOP[[1]][c(1, 2, 3, 5, 6)] < 0.1, rep(TRUE, 5)) + + fit.A.HS <- list(mkinfit("HS", FOCUS_2006_A, quiet = TRUE)) + + median.A.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F, + dataset == "A", + c(M0, k1, k2, tb, DT50, DT90)), "median")) + + dev.percent.A.HS <- calc_dev.percent(fit.A.HS, median.A.HS) + expect_equivalent(dev.percent.A.HS[[1]] < 0.1, rep(TRUE, 6)) +}) + +test_that("Fits for FOCUS B deviate less than 0.1% from median of values from FOCUS report", { + skip_on_cran() + fit.B.SFO <- list(mkinfit("SFO", FOCUS_2006_B, quiet = TRUE)) + + median.B.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F, + dataset == "B", + c(M0, k, DT50, DT90)), "median")) + + dev.percent.B.SFO <- calc_dev.percent(fit.B.SFO, median.B.SFO) + expect_equivalent(dev.percent.B.SFO[[1]] < 0.1, rep(TRUE, 4)) + + fit.B.FOMC <- list(mkinfit("FOMC", FOCUS_2006_B, quiet = TRUE)) + + median.B.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F, + dataset == "B", + c(M0, alpha, beta, DT50, DT90)), "median")) + + dev.percent.B.FOMC <- calc_dev.percent(fit.B.FOMC, median.B.FOMC) + expect_equivalent(dev.percent.B.FOMC[[1]] < 0.1, rep(TRUE, 5)) + + fit.B.DFOP <- list(mkinfit("DFOP", FOCUS_2006_B, quiet = TRUE)) + + median.B.DFOP <- as.numeric(lapply(subset(FOCUS_2006_DFOP_ref_A_to_B, + dataset == "B", + c(M0, k1, k2, f, DT50, DT90)), "median")) + + dev.percent.B.DFOP <- calc_dev.percent(fit.B.DFOP, median.B.DFOP) + #expect_equivalent(dev.percent.B.DFOP[[1]] < 0.1, rep(TRUE, 6)) # g/f is ill-determined + expect_equivalent(dev.percent.B.DFOP[[1]][c(1, 2, 3, 5, 6)] < 0.1, rep(TRUE, 5)) + + fit.B.HS <- list(mkinfit("HS", FOCUS_2006_B, quiet = TRUE)) + + median.B.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F, + dataset == "B", + c(M0, k1, k2, tb, DT50, DT90)), + "median", na.rm = TRUE)) + + dev.percent.B.HS <- calc_dev.percent(fit.B.HS, median.B.HS) + expect_equivalent(dev.percent.B.HS[[1]] < 0.1, rep(TRUE, 6)) + + fit.B.SFORB <- list(mkinfit(SFORB, FOCUS_2006_B, quiet=TRUE)) + dev.percent.B.SFORB <- calc_dev.percent(fit.B.SFORB, median.B.DFOP) + expect_equivalent(dev.percent.B.SFORB[[1]][c(1, 5, 6)] < 0.1, rep(TRUE, 3)) +}) + +test_that("Fits for FOCUS C deviate less than 0.1% from median of values from FOCUS report", { + fit.C.SFO <- list(mkinfit("SFO", FOCUS_2006_C, quiet = TRUE)) + + median.C.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F, + dataset == "C", + c(M0, k, DT50, DT90)), "median")) + + dev.percent.C.SFO <- calc_dev.percent(fit.C.SFO, median.C.SFO) + expect_equivalent(dev.percent.C.SFO[[1]] < 0.1, rep(TRUE, 4)) + + fit.C.FOMC <- list(mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)) + + median.C.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F, + dataset == "C", + c(M0, alpha, beta, DT50, DT90)), "median")) + + dev.percent.C.FOMC <- calc_dev.percent(fit.C.FOMC, median.C.FOMC, + round_results = 2) # Not enough precision in FOCUS results + expect_equivalent(dev.percent.C.FOMC[[1]] < 0.1, rep(TRUE, 5)) + + fit.C.HS <- list(mkinfit("HS", FOCUS_2006_C, quiet = TRUE)) + + median.C.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F, + dataset == "C", + c(M0, k1, k2, tb, DT50, DT90)), "median")) + + dev.percent.C.HS <- calc_dev.percent(fit.C.HS, median.C.HS, round_results = c(2, 4, 6, 2, 2)) + # Not enouth precision in k2 available + expect_equivalent(dev.percent.C.HS[[1]] < c(0.1, 0.1, 0.3, 0.1, 0.1, 0.1), rep(TRUE, 6)) +}) + +test_that("SFO fits give approximately (0.001%) equal results with different solution methods", { + skip_on_cran() + fit.A.SFO.default <- mkinfit("SFO", FOCUS_2006_A, quiet = TRUE)$bparms.optim + + fits.A.SFO <- list() + fits.A.SFO[[1]] <- mkinfit(SFO, FOCUS_2006_A, quiet = TRUE) + fits.A.SFO[[2]] <- mkinfit(SFO, FOCUS_2006_A, quiet = TRUE, solution_type = "eigen") + fits.A.SFO[[3]] <- mkinfit(SFO, FOCUS_2006_A, quiet = TRUE, solution_type = "deSolve") + + dev.percent <- calc_dev.percent(fits.A.SFO, fit.A.SFO.default, endpoints = FALSE) + expect_equivalent(dev.percent[[1]] < 0.001, rep(TRUE, 2)) + expect_equivalent(dev.percent[[2]] < 0.001, rep(TRUE, 2)) + expect_equivalent(dev.percent[[3]] < 0.001, rep(TRUE, 2)) +}) + +test_that("FOMC fits give approximately (0.001%) equal results with different solution methods", { + skip_on_cran() + fit.C.FOMC.default <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)$bparms.optim + + fits.C.FOMC <- list() + fits.C.FOMC[[1]] <- mkinfit(FOMC, FOCUS_2006_C, quiet = TRUE) + fits.C.FOMC[[2]] <- mkinfit(FOMC, FOCUS_2006_C, quiet = TRUE, solution_type = "deSolve") + + dev.percent <- calc_dev.percent(fits.C.FOMC, fit.C.FOMC.default, endpoints = FALSE) + expect_equivalent(dev.percent[[1]] < 0.001, rep(TRUE, 3)) + expect_equivalent(dev.percent[[2]] < 0.001, rep(TRUE, 3)) +}) + +test_that("DFOP fits give approximately (0.001%) equal results with different solution methods", { + skip_on_cran() + fit.C.DFOP.default <- mkinfit("DFOP", FOCUS_2006_C, quiet = TRUE)$bparms.optim + + fits.C.DFOP <- list() + fits.C.DFOP[[1]] <- mkinfit(DFOP, FOCUS_2006_C, quiet = TRUE) + fits.C.DFOP[[2]] <- mkinfit(DFOP, FOCUS_2006_C, quiet = TRUE, solution_type = "deSolve") + + dev.percent <- calc_dev.percent(fits.C.DFOP, fit.C.DFOP.default, endpoints = FALSE) + expect_equivalent(dev.percent[[1]] < 0.001, rep(TRUE, 4)) + expect_equivalent(dev.percent[[2]] < 0.001, rep(TRUE, 4)) +}) + +test_that("SFORB fits give approximately (0.002%) equal results with different solution methods", { + skip_on_cran() + fit.B.SFORB.default <- mkinfit(SFORB, FOCUS_2006_B, quiet=TRUE)$bparms.optim + + fits.B.SFORB <- list() + fits.B.SFORB[[1]] <- mkinfit(SFORB, FOCUS_2006_B, quiet=TRUE, solution_type = "eigen") + fits.B.SFORB[[2]] <- mkinfit(SFORB, FOCUS_2006_B, quiet=TRUE, solution_type = "deSolve") + dev.percent <- calc_dev.percent(fits.B.SFORB, fit.B.SFORB.default, endpoints = FALSE) + expect_equivalent(dev.percent[[1]] < 0.001, rep(TRUE, 4)) + expect_equivalent(dev.percent[[2]] < 0.002, rep(TRUE, 4)) +}) 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) +}) |