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, 71 insertions, 35 deletions

diff --git a/tests/testthat/test_irls.R b/tests/testthat/test_error_models.R
index f61f793d..bda8ca7f 100644
--- a/tests/testthat/test_irls.R
+++ b/tests/testthat/test_error_models.R
@@ -16,8 +16,7 @@
 # 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")
-
+context("Error model fitting")
 
 m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"),
                            M1 = mkinsub("SFO", "M2"),
@@ -33,19 +32,53 @@ 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", {
+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$lower),
+                    rep(0, 6), tolerance = 0.02)
+})
+
+test_that("Error model 'obs' works", {
   skip_on_cran()
-  fit_irls_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, reweight.method = "obs", quiet = TRUE)
-  parms_1 <- round(fit_irls_1$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))
+  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("Reweighting method 'tc' works", {
-  fit_irls_2 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, reweight.method = "tc", quiet = TRUE)
-  parms_2 <- round(fit_irls_2$bparms.optim, c(1, 4, 4, 4, 4, 4))
-  expect_equivalent(parms_2, c(102.1, 0.7393, 0.2992, 0.0202, 0.7687, 0.7229))
+test_that("Error model 'tc' works", {
+  skip_on_cran()
+  fit_tc_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "tc", quiet = TRUE)
+  parms_3 <- round(fit_tc_1$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", {
 
-  skip("Too much trouble with datasets that are randomly generated")
   # I need to make the tc method more robust against that
   # skip_on_cran()
 
@@ -57,42 +90,46 @@ test_that("Reweighting method 'tc' works", {
   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)
+    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)
+    n = 1, reps = 100, digits = 5, LOD = -Inf, seed = 123456)
 
-  f_2_10 <- mmkin("DFOP", d_2_10, quiet = TRUE,
+  # 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) < 0.45))
+  expect_true(all(abs(parm_errors_2_10) < 0.12))
 
-  f_2_10_tc <- mmkin("DFOP", d_2_10, reweight.method = "tc", quiet = TRUE,
+  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) < 0.25))
+  expect_true(all(abs(parm_errors_2_10_tc) < 0.05))
 
-  tcf_2_10_tc <- apply(sapply(f_2_10_tc, function(x) x$tc_fitted), 1, mean, na.rm = TRUE)
+  tcf_2_10_tc <- apply(sapply(f_2_10_tc, function(x) x$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) < 0.5))
+  expect_true(all(abs(tcf_2_10_error_model_errors) < 0.2))
 
-  f_tc_100_1 <- suppressWarnings(mkinfit(DFOP, d_100_1[[1]], reweight.method = "tc", quiet = TRUE))
+  # 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) < 0.1))
+  expect_true(all(abs(parm_errors_100_1) < 0.02))
 
-  tcf_100_1_error_model_errors <- (f_tc_100_1$tc_fitted - c(0.5, 0.07)) /
+  tcf_100_1_error_model_errors <- (f_tc_100_1$errparms - 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))
+  # 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"),
@@ -111,32 +148,31 @@ test_that("Reweighting method 'tc' works", {
 
   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)
+    n = 15, reps = 100, digits = 5, LOD = -Inf, seed = 123456)
 
-  # For a single fit, we get a relative error of less than 30%  in the error
+  # 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,
-                          reweight.method = "tc", reweight.tol = 1e-4)
-  parm_errors_met_2_tc_e4 <- (f_met_2_tc_e4$tc_fitted - c(0.5, 0.07)) / c(0.5, 0.07)
-  expect_true(all(abs(parm_errors_met_2_tc_e4) < 0.3))
+                          error_model = "tc")
+  parm_errors_met_2_tc_e4 <- (f_met_2_tc_e4$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,
-                            reweight.method = "tc", reweight.tol = 1e-4,
-                            cores = 14)
+                            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$tc_fitted), 1, mean, na.rm = TRUE)
+  tcf_met_2_15_tc <- apply(sapply(f_met_2_15_tc_e4, function(x) x$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 only get a precision < 30% for retrieving the original error model components
+  # 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) < 0.3))
+  expect_true(all(abs(tcf_met_2_15_tc_error_model_errors) < 0.15))
 })