# 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 <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$bparms.optim, c(1, 4, 4, 4, 4, 4))
expect_equivalent(parms_3, c(102.1, 0.7393, 0.2992, 0.0202, 0.7687, 0.7229))
})
test_that("Reweighting method 'tc' produces reasonable variance estimates", {
# Check if we can approximately obtain the parameters and the error model
# components that were used in the data generation
# Parent only
DFOP <- mkinmod(parent = mkinsub("DFOP"))
sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
parms_DFOP <- c(k1 = 0.2, k2 = 0.02, g = 0.5)
parms_DFOP_optim <- c(parent_0 = 100, parms_DFOP)
d_DFOP <- mkinpredict(DFOP,
parms_DFOP, c(parent = 100),
sampling_times)
d_2_10 <- add_err(d_DFOP,
sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
n = 10, reps = 2, digits = 5, LOD = -Inf, seed = 123456)
d_100_1 <- add_err(d_DFOP,
sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
n = 1, reps = 100, digits = 5, LOD = -Inf, seed = 123456)
# Per default (on my box) use all cores minus one
n_cores <- parallel::detectCores() - 1
# We are only allowed one core on travis
if (Sys.getenv("TRAVIS") != "") n_cores = 1
# Also on Windows we would need to make a cluster first,
# and I do not know how this would work on winbuilder or CRAN, so
if (Sys.info()["sysname"] == "Windows") n_cores = 1
# Unweighted fits
f_2_10 <- mmkin("DFOP", d_2_10, error_model = "const", quiet = TRUE,
cores = n_cores)
parms_2_10 <- apply(sapply(f_2_10, function(x) x$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$errparms), 1, mean, na.rm = TRUE)
tcf_met_2_15_tc_error_model_errors <- (tcf_met_2_15_tc - c(0.5, 0.07)) /
c(0.5, 0.07)
# Here we get a precision < 10% for retrieving the original error model components
# from 15 datasets
expect_true(all(abs(tcf_met_2_15_tc_error_model_errors) < 0.10))
})
test_that("The two-component error model finds the best known AIC values for parent models", {
skip_on_cran()
library(parallel)
source("~/git/mkin/R/mkinfit.R")
source("~/git/mkin/R/mmkin.R")
f_9 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$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]]$data,
error_model = "tc", error_model_algorithm = "IRLS")
f_9 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data,
error_model = "tc", error_model_algorithm = "d_3")
AIC(f_9)
f_10 <- mkinfit("DFOP", experimental_data_for_UBA_2019[[10]]$data,
error_model = "tc", error_model_algorithm = "IRLS")
f_tc_exp_direct <- mmkin(c("SFO", "DFOP", "HS"),
lapply(experimental_data_for_UBA_2019, function(x) x$data),
error_model = "tc",
error_model_algorithm = "direct",
quiet = TRUE)
f_tc_exp_twostep <- mmkin(c("SFO", "DFOP", "HS"),
lapply(experimental_data_for_UBA_2019, function(x) x$data),
error_model = "tc",
error_model_algorithm = "twostep",
quiet = TRUE)
f_tc_exp_threestep <- mmkin(c("SFO", "DFOP", "HS"),
lapply(experimental_data_for_UBA_2019, function(x) x$data),
error_model = "tc",
error_model_algorithm = "threestep",
quiet = TRUE)
f_tc_exp_fourstep <- mmkin(c("SFO", "DFOP", "HS"),
lapply(experimental_data_for_UBA_2019, function(x) x$data),
error_model = "tc",
error_model_algorithm = "fourstep",
quiet = TRUE)
f_tc_exp_IRLS <- mmkin(c("SFO", "DFOP", "HS"),
lapply(experimental_data_for_UBA_2019, function(x) x$data),
error_model = "tc",
error_model_algorithm = "IRLS",
quiet = TRUE)
AIC_exp_direct <- lapply(f_tc_exp_direct, AIC)
AIC_exp_direct <- lapply(AIC_exp_direct, round, 1)
dim(AIC_exp_direct) <- dim(f_tc_exp_direct)
dimnames(AIC_exp_direct) <- dimnames(f_tc_exp_direct)
AIC_exp_twostep <- lapply(f_tc_exp_twostep, AIC)
AIC_exp_twostep <- lapply(AIC_exp_twostep, round, 1)
dim(AIC_exp_twostep) <- dim(f_tc_exp_twostep)
dimnames(AIC_exp_twostep) <- dimnames(f_tc_exp_twostep)
AIC_exp_threestep <- lapply(f_tc_exp_threestep, AIC)
AIC_exp_threestep <- lapply(AIC_exp_threestep, round, 1)
dim(AIC_exp_threestep) <- dim(f_tc_exp_threestep)
dimnames(AIC_exp_threestep) <- dimnames(f_tc_exp_threestep)
AIC_exp_fourstep <- lapply(f_tc_exp_fourstep, AIC)
AIC_exp_fourstep <- lapply(AIC_exp_fourstep, round, 1)
dim(AIC_exp_fourstep) <- dim(f_tc_exp_fourstep)
dimnames(AIC_exp_fourstep) <- dimnames(f_tc_exp_fourstep)
AIC_exp_IRLS <- lapply(f_tc_exp_IRLS, AIC)
AIC_exp_IRLS <- lapply(AIC_exp_IRLS, round, 1)
dim(AIC_exp_IRLS) <- dim(f_tc_exp_IRLS)
dimnames(AIC_exp_IRLS) <- dimnames(f_tc_exp_IRLS)
AIC_exp <- lapply(f_tc_exp, AIC)
dim(AIC_exp) <- dim(f_tc_exp)
dimnames(AIC_exp) <- dimnames(f_tc_exp)
unlist(AIC_exp["SFO", c(9, 11, 12)])
expect_equivalent(round(unlist(AIC_exp["SFO", c(9, 11, 12)]), 1),
c(134.9, 125.5, 82.0))
})
