Avoid merge() and data.frame() in cost function

also for deSolve and eigenvalue based solutions. This noticeably increases
performance for these methods, see test.log and benchmark vignette.

3 files changed, 16 insertions, 14 deletions

diff --git a/tests/testthat/FOCUS_2006_D.csf b/tests/testthat/FOCUS_2006_D.csf
index d41f0a9f..c0c5c10c 100644
--- a/tests/testthat/FOCUS_2006_D.csf
+++ b/tests/testthat/FOCUS_2006_D.csf
@@ -5,7 +5,7 @@ Description:
 MeasurementUnits: % AR
 TimeUnits: days
 Comments: Created using mkin::CAKE_export
-Date: 2020-05-10
+Date: 2020-05-11
 Optimiser: IRLS
 
 [Data]
diff --git a/tests/testthat/test_mkinpredict_SFO_SFO.R b/tests/testthat/test_mkinpredict_SFO_SFO.R
index 347aa50b..d0dc7cdb 100644
--- a/tests/testthat/test_mkinpredict_SFO_SFO.R
+++ b/tests/testthat/test_mkinpredict_SFO_SFO.R
@@ -5,26 +5,28 @@ test_that("Variants of model predictions for SFO_SFO model give equivalent resul
   # Do not use time 0, as eigenvalue based solution does not give 0 at time 0 for metabolites

   # and relative tolerance is thus not met

   tol = 0.01

-  SFO_SFO.1 <- mkinmod(parent = list(type = "SFO", to = "m1"),

-         m1 = list(type = "SFO"), use_of_ff = "min", quiet = TRUE)

-  SFO_SFO.2 <- mkinmod(parent = list(type = "SFO", to = "m1"),

-         m1 = list(type = "SFO"), use_of_ff = "max", quiet = TRUE)

+  SFO_SFO.1 <- mkinmod(parent = mkinsub("SFO", to = "m1"),

+         m1 = mkinsub("SFO"), use_of_ff = "min", quiet = TRUE)

+  SFO_SFO.2 <- mkinmod(parent = mkinsub("SFO", to = "m1"),

+         m1 = mkinsub("SFO"), use_of_ff = "max", quiet = TRUE)

 

   ot = seq(0, 100, by = 1)

-  r.1.e <- subset(mkinpredict(SFO_SFO.1,

+  r.1.e <- subset(as.data.frame(mkinpredict(SFO_SFO.1,

              c(k_parent_m1 = 0.1, k_parent_sink = 0.1, k_m1_sink = 0.1),

-             c(parent = 100, m1 = 0), ot, solution_type = "eigen"),

+             c(parent = 100, m1 = 0), ot, solution_type = "eigen")),

                  time %in% c(1, 10, 50, 100))

-  r.1.d <- subset(mkinpredict(SFO_SFO.1,

+  r.1.d <- subset(as.data.frame(mkinpredict(SFO_SFO.1,

              c(k_parent_m1 = 0.1, k_parent_sink = 0.1, k_m1_sink = 0.1),

-             c(parent = 100, m1 = 0), ot, solution_type = "deSolve"),

+             c(parent = 100, m1 = 0), ot, solution_type = "deSolve")),

                  time %in% c(1, 10, 50, 100))

 

-  r.2.e <- subset(mkinpredict(SFO_SFO.2, c(k_parent = 0.2, f_parent_to_m1 = 0.5, k_m1 = 0.1),

-      c(parent = 100, m1 = 0), ot, solution_type = "eigen"),

+  r.2.e <- subset(as.data.frame(mkinpredict(SFO_SFO.2,

+      c(k_parent = 0.2, f_parent_to_m1 = 0.5, k_m1 = 0.1),

+      c(parent = 100, m1 = 0), ot, solution_type = "eigen")),

                   time %in% c(1, 10, 50, 100))

-  r.2.d <- subset(mkinpredict(SFO_SFO.2, c(k_parent = 0.2, f_parent_to_m1 = 0.5, k_m1 = 0.1),

-      c(parent = 100, m1 = 0), ot, solution_type = "deSolve"),

+  r.2.d <- subset(as.data.frame(mkinpredict(SFO_SFO.2,

+      c(k_parent = 0.2, f_parent_to_m1 = 0.5, k_m1 = 0.1),

+      c(parent = 100, m1 = 0), ot, solution_type = "deSolve")),

                   time %in% c(1, 10, 50, 100))

 

   # Compare eigen and deSolve for minimum use of formation fractions

diff --git a/tests/testthat/test_plots_summary_twa.R b/tests/testthat/test_plots_summary_twa.R
index 13d1dd0f..bc3d29a8 100644
--- a/tests/testthat/test_plots_summary_twa.R
+++ b/tests/testthat/test_plots_summary_twa.R
@@ -13,7 +13,7 @@ test_that("Time weighted average concentrations are correct", {
                 odeparms = bpar[2:length(bpar)],
                 odeini = c(parent = bpar[[1]]),
                 outtimes = outtimes_10)
-    twa_num <- mean(pred_10$parent)
+    twa_num <- mean(pred_10[, "parent"])
     names(twa_num) <- 10
     twa_ana <- max_twa_parent(fit, 10)