diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2020-05-11 05:15:19 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2020-05-11 05:18:32 +0200 |
| commit | 234c9059a95e104917e488a6ddd2313234a96cdc (patch) | |
| tree | f6e54098f79d94578434ef727b62f7cc5d5e79b7 /tests/testthat/test_mkinpredict_SFO_SFO.R | |
| parent | d113cd79b178fdc91aecb894707ed356129dfb75 (diff) | |
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.
Diffstat (limited to 'tests/testthat/test_mkinpredict_SFO_SFO.R')
| -rw-r--r-- | tests/testthat/test_mkinpredict_SFO_SFO.R | 26 |
1 files changed, 14 insertions, 12 deletions
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
|