# Copyright (C) 2018,2019 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 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$bparms.optim, c(1, 4, 4, 4, 4, 4)) expect_equivalent(parms_3, c(102.1, 0.7393, 0.2992, 0.0202, 0.7687, 0.7229)) }) test_that("Reweighting method 'tc' produces reasonable variance estimates", { # Check if we can approximately obtain the parameters and the error model # components that were used in the data generation # Parent only DFOP <- mkinmod(parent = mkinsub("DFOP")) sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) parms_DFOP <- c(k1 = 0.2, k2 = 0.02, g = 0.5) parms_DFOP_optim <- c(parent_0 = 100, parms_DFOP) d_DFOP <- mkinpredict(DFOP, parms_DFOP, c(parent = 100), sampling_times) d_2_10 <- add_err(d_DFOP, sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07), n = 10, reps = 2, digits = 5, LOD = -Inf, seed = 123456) d_100_1 <- add_err(d_DFOP, sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07), n = 1, reps = 100, digits = 5, LOD = -Inf, seed = 123456) # Per default (on my box) use all cores minus one n_cores <- parallel::detectCores() - 1 # We are only allowed one core on travis if (Sys.getenv("TRAVIS") != "") n_cores = 1 # Also on Windows we would need to make a cluster first, # and I do not know how this would work on winbuilder or CRAN, so if (Sys.info()["sysname"] == "Windows") n_cores = 1 # Unweighted fits f_2_10 <- mmkin("DFOP", d_2_10, error_model = "const", quiet = TRUE, cores = n_cores) parms_2_10 <- apply(sapply(f_2_10, function(x) x$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") 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$errparms), 1, mean, na.rm = TRUE) tcf_met_2_15_tc_error_model_errors <- (tcf_met_2_15_tc - c(0.5, 0.07)) / c(0.5, 0.07) # Here we get a precision < 10% for retrieving the original error model components # from 15 datasets expect_true(all(abs(tcf_met_2_15_tc_error_model_errors) < 0.10)) }) test_that("The two-component error model finds the best known AIC values for parent models", { skip_on_cran() experimental_data_for_UBA_2019 library(parallel) source("~/git/mkin/R/mkinfit.R") source("~/git/mkin/R/mmkin.R") f_9 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data) f_9 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data, error_model = "tc", error_model_algorithm = "direct") f_9 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data, error_model = "tc", error_model_algorithm = "twostep") f_9 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data, error_model = "tc", error_model_algorithm = "threestep") f_9 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data, error_model = "tc", error_model_algorithm = "fourstep") f_9 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data, error_model = "tc", error_model_algorithm = "IRLS") AIC(f_9) f_tc_exp <- mmkin(c("SFO"), lapply(experimental_data_for_UBA_2019, function(x) x$data), error_model = "tc", error_model_algorithm = "direct", quiet = TRUE) f_tc_exp <- mmkin(c("SFO"), lapply(experimental_data_for_UBA_2019, function(x) x$data), error_model = "tc", error_model_algorithm = "twostep", quiet = TRUE) f_tc_exp <- mmkin(c("SFO"), lapply(experimental_data_for_UBA_2019, function(x) x$data), error_model = "tc", error_model_algorithm = "threestep", quiet = TRUE) 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)) })