Direct error model fitting works

- No IRLS required
- Removed optimization algorithms other than Port
- Removed the dependency on FME
- Fitting the error model 'obs' is much faster for the FOCUS_2006_D
  dataset and the FOMC_SFO model (1 second versus 3.4 seconds)
- Vignettes build slower. Compiled models needs 3 minutes instead of 1.5
- For other vignettes, the trend is less clear. Some fits are faster,
  even for error_model = "const". FOCUS_Z is faster (34.9 s versus
  44.1 s)
- Standard errors and confidence intervals are slightly smaller
- Removed code for plotting during the fit, as I hardly ever used it
- Merged the two cost functions (using transformed and untransformed
  parameters) into one log-likelihood function

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))
+})