1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
|
context("Nonlinear mixed-effects models")
# Round error model parameters as they are not rounded in print methods
dfop_nlme_1$modelStruct$varStruct$const <-
signif(dfop_nlme_1$modelStruct$varStruct$const, 3)
dfop_nlme_1$modelStruct$varStruct$prop <-
signif(dfop_nlme_1$modelStruct$varStruct$prop, 4)
dfop_sfo_pop <- attr(ds_dfop_sfo, "pop")
test_that("Print methods work", {
expect_known_output(print(fits[, 2:3], digits = 2), "print_mmkin_parent.txt")
expect_known_output(print(mixed(mmkin_sfo_1), digits = 2), "print_mmkin_sfo_1_mixed.txt")
expect_known_output(print(dfop_nlme_1, digits = 1), "print_dfop_nlme_1.txt")
expect_known_output(print(sfo_saem_1_reduced, digits = 1), "print_sfo_saem_1_reduced.txt")
skip_on_cran() # The following test is platform dependent and fails on
# win-builder with current (18 Nov 2022) R-devel, on the Linux R-devel CRAN check systems
# and also using R-devel locally
expect_known_output(print(dfop_saem_1, digits = 1), "print_dfop_saem_1.txt")
})
test_that("nlme results are reproducible to some degree", {
skip_on_cran()
test_summary <- summary(dfop_nlme_1)
test_summary$nlmeversion <- "Dummy 0.0 for testing"
test_summary$mkinversion <- "Dummy 0.0 for testing"
test_summary$Rversion <- "Dummy R version for testing"
test_summary$date.fit <- "Dummy date for testing"
test_summary$date.summary <- "Dummy date for testing"
test_summary$time <- c(elapsed = "test time 0")
expect_known_output(print(test_summary, digits = 1), "summary_dfop_nlme_1.txt")
# The biphasic example data illustrate that DFOP parameters are difficult to
# quantify with the usual design
# k1 and k2 just fail the first test (lower bound of the ci), so we need to exclude it
dfop_no_k1_k2 <- c("parent_0", "k_m1", "f_parent_to_m1", "g")
dfop_sfo_pop_no_k1_k2 <- as.numeric(dfop_sfo_pop[dfop_no_k1_k2])
ci_dfop_sfo_n <- summary(nlme_dfop_sfo)$confint_back
expect_true(all(ci_dfop_sfo_n[dfop_no_k1_k2, "lower"] < dfop_sfo_pop_no_k1_k2))
expect_true(all(ci_dfop_sfo_n[, "upper"] > as.numeric(dfop_sfo_pop)))
})
test_that("saemix results are reproducible for biphasic fits", {
skip_on_cran()
saem_dfop_sfo_s <- saem(mmkin_dfop_sfo, transformations = "saemix", quiet = TRUE)
test_summary <- summary(saem_dfop_sfo_s)
test_summary$saemixversion <- "Dummy 0.0 for testing"
test_summary$mkinversion <- "Dummy 0.0 for testing"
test_summary$Rversion <- "Dummy R version for testing"
test_summary$date.fit <- "Dummy date for testing"
test_summary$date.summary <- "Dummy date for testing"
test_summary$time <- c(elapsed = "test time 0")
expect_known_output(print(test_summary, digits = 1), "summary_saem_dfop_sfo_s.txt")
dfop_sfo_pop <- as.numeric(dfop_sfo_pop)
no_k1 <- c(1, 2, 3, 5, 6)
no_k2 <- c(1, 2, 3, 4, 6)
no_k1_k2 <- c(1, 2, 3, 6)
ci_dfop_sfo_s_s <- summary(saem_dfop_sfo_s)$confint_back
expect_true(all(ci_dfop_sfo_s_s[, "lower"] < dfop_sfo_pop))
expect_true(all(ci_dfop_sfo_s_s[, "upper"] > dfop_sfo_pop))
# I tried to only do few iterations in routine tests as this is so slow
# but then deSolve fails at some point (presumably at the switch between
# the two types of iterations)
#saem_dfop_sfo_2 <- saem(mmkin_biphasic, solution_type = "deSolve",
# control = list(nbiter.saemix = c(10, 5), nbiter.burn = 5), quiet = TRUE)
skip("Fitting with saemix takes around 10 minutes when using deSolve")
saem_dfop_sfo_2 <- saem(mmkin_dfop_sfo, solution_type = "deSolve", quiet = TRUE)
# As with the analytical solution, k1 and k2 are not fitted well
ci_dfop_sfo_s_d <- summary(saem_dfop_sfo_2)$confint_back
expect_true(all(ci_dfop_sfo_s_d[no_k2, "lower"] < dfop_sfo_pop[no_k2]))
expect_true(all(ci_dfop_sfo_s_d[no_k1, "upper"] > dfop_sfo_pop[no_k1]))
})
test_that("Reading spreadsheets, finding ill-defined parameters and covariate modelling", {
skip_on_cran()
data_path <- system.file(
"testdata", "lambda-cyhalothrin_soil_efsa_2014.xlsx",
package = "mkin")
ds_lambda <- read_spreadsheet(data_path, valid_datasets = c(1:4, 7:13))
covariates <- attr(ds_lambda, "covariates")
lambda_sforb <- mmkin("SFORB", ds_lambda, quiet = TRUE,
cores = n_cores,
error_model = "const")
lambda_sforb_saem_pH <- saem(lambda_sforb, covariates = covariates,
covariate_models = list(log_k_lambda_bound_free ~ pH))
expect_equal(
as.character(illparms(lambda_sforb_saem_pH)),
c("sd(lambda_free_0)", "sd(log_k_lambda_free_bound)"))
lambda_endpoints <- endpoints(lambda_sforb_saem_pH)
expect_equal(lambda_endpoints$covariates$pH, 6.45)
expect_equal(
round(as.numeric(lambda_endpoints$distimes), 0),
c(47, 422, 127, 7, 162))
})
test_that("SFO-SFO saemix specific analytical solution work", {
skip_on_cran()
SFO_SFO <- mkinmod(DMTA = mkinsub("SFO", "M23"),
M23 = mkinsub("SFO"), quiet = TRUE)
mmkin_sfo_sfo <- mmkin(list("SFO-SFO" = SFO_SFO), dmta_ds, quiet = TRUE,
cores = n_cores,
error_model = "const")
saem_sfo_sfo_saemix_analytical <- saem(mmkin_sfo_sfo)
expect_error(saem(mmkin_sfo_sfo, solution_type = "analytical"), "not supported")
saem_sfo_sfo_mkin_desolve <- saem(mmkin_sfo_sfo, solution_type = "deSolve")
expect_equal(
endpoints(saem_sfo_sfo_saemix_analytical),
endpoints(saem_sfo_sfo_mkin_desolve))
skip("This is seldom used, so save some time")
saem_sfo_sfo_mkin_eigen<- saem(mmkin_sfo_sfo, solution_type = "eigen")
expect_equal(
endpoints(saem_sfo_sfo_saemix_analytical),
endpoints(saem_sfo_sfo_mkin_eigen))
})
|
