# 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("Iteratively reweighted least squares (IRLS) 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("Reweighting method 'obs' works", {
skip_on_cran()
fit_irls_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, reweight.method = "obs", quiet = TRUE)
parms_1 <- round(fit_irls_1$bparms.optim, c(1, 4, 4, 4, 4, 4))
expect_equivalent(parms_1, c(102.1, 0.7389, 0.2982, 0.0203, 0.7677, 0.7246))
})
test_that("Reweighting method 'tc' works", {
skip() # Too much trouble with datasets that are randomly generated
# I need to make the tc method more robust against that
# skip_on_cran()
# Check if we can approximately obtain the parameters and the error model
# components that were used in the data generation
# Parent only
DFOP <- mkinmod(parent = mkinsub("DFOP"))
sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
parms_DFOP <- c(k1 = 0.2, k2 = 0.02, g = 0.5)
parms_DFOP_optim <- c(parent_0 = 100, parms_DFOP)
d_DFOP <- mkinpredict(DFOP,
parms_DFOP, c(parent = 100),
sampling_times)
d_2_10 <- add_err(d_DFOP,
sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
n = 10, reps = 2, digits = 5, LOD = -Inf)
d_100_1 <- add_err(d_DFOP,
sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
n = 1, reps = 100, digits = 5, LOD = -Inf)
f_2_10 <- mmkin("DFOP", d_2_10, quiet = TRUE,
cores = if (Sys.getenv("TRAVIS") != "") 1 else 15)
parms_2_10 <- apply(sapply(f_2_10, function(x) x$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) < 0.45))
f_2_10_tc <- mmkin("DFOP", d_2_10, reweight.method = "tc", quiet = TRUE,
cores = if (Sys.getenv("TRAVIS") != "") 1 else 15)
parms_2_10_tc <- apply(sapply(f_2_10_tc, function(x) x$bparms.optim), 1, mean)
parm_errors_2_10_tc <- (parms_2_10_tc - parms_DFOP_optim) / parms_DFOP_optim
expect_true(all(abs(parm_errors_2_10_tc) < 0.25))
tcf_2_10_tc <- apply(sapply(f_2_10_tc, function(x) x$tc_fitted), 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) < 0.5))
f_tc_100_1 <- suppressWarnings(mkinfit(DFOP, d_100_1[[1]], reweight.method = "tc", quiet = TRUE))
parm_errors_100_1 <- (f_tc_100_1$bparms.optim - parms_DFOP_optim) / parms_DFOP_optim
expect_true(all(abs(parm_errors_100_1) < 0.1))
tcf_100_1_error_model_errors <- (f_tc_100_1$tc_fitted - c(0.5, 0.07)) /
c(0.5, 0.07)
# Even with 100 (or even 1000, not shown) replicates at each observation time
# we only get a precision of 15% to 30% for the error model components
expect_true(all(abs(tcf_100_1_error_model_errors) < 0.3))
# 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 = 1000, digits = 5, LOD = -Inf)
# For a single fit, we get a relative error of less than 30% in the error
# model components
f_met_2_tc_e4 <- mkinfit(m_synth_DFOP_lin, d_met_2_15[[1]], quiet = TRUE,
reweight.method = "tc", reweight.tol = 1e-4)
parm_errors_met_2_tc_e4 <- (f_met_2_tc_e4$tc_fitted - c(0.5, 0.07)) / c(0.5, 0.07)
expect_true(all(abs(parm_errors_met_2_tc_e4) < 0.3))
# Doing more takes a lot of computing power
skip_on_travis()
f_met_2_15_tc_e4 <- mmkin(list(m_synth_DFOP_lin), d_met_2_15, quiet = TRUE,
reweight.method = "tc", reweight.tol = 1e-4,
cores = 14)
parms_met_2_15_tc_e4 <- apply(sapply(f_met_2_15_tc_e4, function(x) x$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$tc_fitted), 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 only get a precision < 30% for retrieving the original error model components
# from 15 datasets
expect_true(all(abs(tcf_met_2_15_tc_error_model_errors) < 0.3))
})
