diff options
Diffstat (limited to 'tests/testthat/test_mkinpredict_SFO_SFO.R')
| -rw-r--r-- | tests/testthat/test_mkinpredict_SFO_SFO.R | 27 |
1 files changed, 18 insertions, 9 deletions
diff --git a/tests/testthat/test_mkinpredict_SFO_SFO.R b/tests/testthat/test_mkinpredict_SFO_SFO.R index b7004f64..b6e5f9e2 100644 --- a/tests/testthat/test_mkinpredict_SFO_SFO.R +++ b/tests/testthat/test_mkinpredict_SFO_SFO.R @@ -28,19 +28,22 @@ test_that("Variants of model predictions for SFO_SFO model give equivalent resul # 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)
+ m1 = list(type = "SFO"), use_of_ff = "min", quiet = TRUE, odeintr_compile = "yes")
SFO_SFO.2 <- mkinmod(parent = list(type = "SFO", to = "m1"),
m1 = list(type = "SFO"), use_of_ff = "max", quiet = TRUE)
ot = seq(0, 100, by = 1)
- r.1.e <- subset(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"),
- time %in% c(1, 10, 50, 100))
- r.1.d <- subset(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"),
- time %in% c(1, 10, 50, 100))
+ test_parms = c(k_parent_m1 = 0.1, k_parent_sink = 0.1, k_m1_sink = 0.1)
+ test_ini = c(parent = 100, m1 = 0)
+ r.1.e <- subset(mkinpredict(SFO_SFO.1, test_parms, test_ini, ot,
+ solution_type = "eigen"),
+ time %in% c(1, 10, 50, 100))
+ r.1.d <- subset(mkinpredict(SFO_SFO.1, test_parms, test_ini, ot,
+ solution_type = "deSolve"),
+ time %in% c(1, 10, 50, 100))
+ r.1.o <- subset(mkinpredict(SFO_SFO.1, test_parms, test_ini, ot,
+ solution_type = "odeintr"),
+ 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"),
@@ -55,6 +58,12 @@ test_that("Variants of model predictions for SFO_SFO model give equivalent resul dev.1.e_d.percent = ifelse(is.na(dev.1.e_d.percent), 0, dev.1.e_d.percent)
expect_equivalent(dev.1.e_d.percent < tol, rep(TRUE, length(dev.1.e_d.percent)))
+ # Compare eigen and odeintr for minimum use of formation fractions
+ dev.1.e_o.percent = 100 * (r.1.e[-1] - r.1.o[-1])/r.1.e[-1]
+ dev.1.e_o.percent = as.numeric(unlist((dev.1.e_o.percent)))
+ dev.1.e_o.percent = ifelse(is.na(dev.1.e_o.percent), 0, dev.1.e_o.percent)
+ expect_equivalent(dev.1.e_o.percent < tol, rep(TRUE, length(dev.1.e_o.percent)))
+
# Compare eigen and deSolve for maximum use of formation fractions
dev.2.e_d.percent = 100 * (r.1.e[-1] - r.1.d[-1])/r.1.e[-1]
dev.2.e_d.percent = as.numeric(unlist((dev.2.e_d.percent)))
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