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
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
|
context("saemix parent models")
test_that("Parent fits using saemix are correctly implemented", {
skip_on_cran()
expect_error(saem(fits), "Only row objects")
# SFO
# mmkin_sfo_1 was generated in the setup script
# We did not introduce variance of parent_0 in the data generation
# This is correctly detected
expect_equal(illparms(sfo_saem_1), "sd(parent_0)")
# So we have also done a fit without this variance
expect_equal(illparms(sfo_saem_1_reduced), character(0))
expect_silent(print(illparms(sfo_saem_1_reduced)))
# We cannot currently do the fit with completely fixed initial values
mmkin_sfo_2 <- update(mmkin_sfo_1, fixed_initials = c(parent = 100))
sfo_saem_3 <- expect_error(saem(mmkin_sfo_2, quiet = TRUE), "at least two parameters")
# We get an error if we do not supply a suitable model specification
expect_error(update(mmkin_sfo_1, models = c("SFOOO")), "Please supply models.*")
sfo_saem_1_mkin <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "mkin")
expect_equal(illparms(sfo_saem_1_mkin), "sd(parent_0)")
sfo_saem_1_reduced_mkin <- update(sfo_saem_1_mkin, no_random_effect = "parent_0")
# The endpoints obtained do not depend on the transformation
expect_equal(endpoints(sfo_saem_1), endpoints(sfo_saem_1_mkin), tol = 0.01)
expect_equal(endpoints(sfo_saem_1_reduced), endpoints(sfo_saem_1_reduced_mkin), tol = 0.01)
s_sfo_saem_1 <- summary(sfo_saem_1)
s_sfo_saem_1_reduced <- summary(sfo_saem_1_reduced)
s_sfo_saem_1_mkin <- summary(sfo_saem_1_mkin)
s_sfo_saem_1_reduced_mkin <- summary(sfo_saem_1_reduced_mkin)
sfo_nlme_1 <- expect_warning(nlme(mmkin_sfo_1), "not converge")
s_sfo_nlme_1 <- summary(sfo_nlme_1)
# Compare with input
expect_equal(round(s_sfo_saem_1$confint_ranef["SD.k_parent", "est."], 1), 0.3)
expect_equal(round(s_sfo_saem_1_mkin$confint_ranef["SD.log_k_parent", "est."], 1), 0.3)
# k_parent is a bit different from input 0.03 here
expect_equal(round(s_sfo_saem_1$confint_back["k_parent", "est."], 3), 0.035)
expect_equal(round(s_sfo_saem_1_mkin$confint_back["k_parent", "est."], 3), 0.035)
# But the result is pretty unanimous between methods
expect_equal(round(s_sfo_saem_1_reduced$confint_back["k_parent", "est."], 3),
round(s_sfo_saem_1$confint_back["k_parent", "est."], 3))
expect_equal(round(s_sfo_saem_1_mkin$confint_back["k_parent", "est."], 3),
round(s_sfo_saem_1$confint_back["k_parent", "est."], 3))
expect_equal(round(s_sfo_saem_1_reduced_mkin$confint_back["k_parent", "est."], 3),
round(s_sfo_saem_1$confint_back["k_parent", "est."], 3))
expect_equal(round(s_sfo_nlme_1$confint_back["k_parent", "est."], 3),
round(s_sfo_saem_1$confint_back["k_parent", "est."], 3))
# Compare fits with heavy rounding to avoid platform dependent results
anova_sfo <- anova(
sfo_saem_1, sfo_saem_1_reduced,
sfo_saem_1_mkin, sfo_saem_1_reduced_mkin,
test = TRUE)
anova_sfo_rounded <- round(anova_sfo, 0)
expect_known_output(print(anova_sfo_rounded), file = "anova_sfo_saem.txt")
# Check the influence of an invented covariate
set.seed(123456) # In my first attempt I hit a false positive by chance...
pH <- data.frame(pH = runif(15, 5, 8), row.names = as.character(1:15))
sfo_saem_pH <- update(sfo_saem_1_reduced_mkin, covariates = pH,
covariate_models = list(log_k_parent ~ pH))
# We expect that this is not significantly better, as the covariate values were completely random
expect_true(anova(sfo_saem_1_reduced_mkin, sfo_saem_pH, test = TRUE)[2, "Pr(>Chisq)"] > 0.05)
# FOMC
mmkin_fomc_1 <- mmkin("FOMC", ds_fomc, quiet = TRUE, error_model = "tc", cores = n_cores)
fomc_saem_1 <- saem(mmkin_fomc_1, quiet = TRUE, transformations = "saemix", no_random_effect = "parent_0")
fomc_saem_2 <- update(fomc_saem_1, transformations = "mkin")
ci_fomc_s1 <- summary(fomc_saem_1)$confint_back
fomc_pop <- as.numeric(fomc_pop)
expect_true(all(ci_fomc_s1[, "lower"] < fomc_pop))
expect_true(all(ci_fomc_s1[, "upper"] > fomc_pop))
expect_equal(endpoints(fomc_saem_1), endpoints(fomc_saem_2), tol = 0.01)
mmkin_fomc_2 <- update(mmkin_fomc_1, state.ini = 100, fixed_initials = "parent")
fomc_saem_2 <- saem(mmkin_fomc_2, quiet = TRUE, transformations = "mkin")
ci_fomc_s2 <- summary(fomc_saem_2)$confint_back
expect_true(all(ci_fomc_s2[, "lower"] < fomc_pop[2:3]))
expect_true(all(ci_fomc_s2[, "upper"] > fomc_pop[2:3]))
# DFOP
dfop_saemix_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix",
no_random_effect = "parent_0")
s_dfop_s1 <- summary(dfop_saemix_1)
s_dfop_s2 <- summary(dfop_saemix_2)
s_dfop_n <- summary(dfop_nlme_1)
dfop_pop <- as.numeric(dfop_pop)
# When using DFOP with mkin transformations, k1 and k2 are sometimes swapped
swap_k1_k2 <- function(p) c(p[1], p[3], p[2], 1 - p[4])
expect_true(all(s_dfop_s1$confint_back[, "lower"] < swap_k1_k2(dfop_pop)))
expect_true(all(s_dfop_s1$confint_back[, "upper"] > swap_k1_k2(dfop_pop)))
expect_true(all(s_dfop_s2$confint_back[, "lower"] < dfop_pop))
expect_true(all(s_dfop_s2$confint_back[, "upper"] > dfop_pop))
# We get < 20% deviations with transformations made in mkin (need to swap k1 and k2)
rel_diff_1 <- (swap_k1_k2(s_dfop_s1$confint_back[, "est."]) - dfop_pop) / dfop_pop
expect_true(all(rel_diff_1 < 0.20))
# We get < 20% deviations with transformations made in saemix
rel_diff_2 <- (s_dfop_s2$confint_back[, "est."] - dfop_pop) / dfop_pop
expect_true(all(rel_diff_2 < 0.2))
# SFORB
mmkin_sforb_1 <- mmkin("SFORB", ds_dfop, quiet = TRUE, cores = n_cores)
sforb_saemix_1 <- saem(mmkin_sforb_1, quiet = TRUE,
no_random_effect = c("parent_free_0"),
transformations = "mkin")
sforb_saemix_2 <- saem(mmkin_sforb_1, quiet = TRUE,
no_random_effect = c("parent_free_0"),
transformations = "saemix")
expect_equal(
log(endpoints(dfop_saemix_1)$distimes[1:2]),
log(endpoints(sforb_saemix_1)$distimes[1:2]), tolerance = 0.03)
expect_equal(
log(endpoints(sforb_saemix_1)$distimes[1:2]),
log(endpoints(sforb_saemix_2)$distimes[1:2]), tolerance = 0.01)
mmkin_hs_1 <- mmkin("HS", ds_hs, quiet = TRUE, error_model = "const", cores = n_cores)
hs_saem_1 <- saem(mmkin_hs_1, quiet = TRUE)
hs_saem_2 <- saem(mmkin_hs_1, quiet = TRUE, transformations = "mkin")
expect_equal(endpoints(hs_saem_1), endpoints(hs_saem_2), tol = 0.01)
ci_hs_s1 <- summary(hs_saem_1)$confint_back
hs_pop <- as.numeric(hs_pop)
#expect_true(all(ci_hs_s1[, "lower"] < hs_pop)) # k1 is overestimated
expect_true(all(ci_hs_s1[, "upper"] > hs_pop))
mmkin_hs_2 <- update(mmkin_hs_1, state.ini = 100, fixed_initials = "parent")
hs_saem_3 <- saem(mmkin_hs_2, quiet = TRUE)
ci_hs_s3 <- summary(hs_saem_3)$confint_back
#expect_true(all(ci_hs_s3[, "lower"] < hs_pop[2:4])) # k1 again overestimated
expect_true(all(ci_hs_s3[, "upper"] > hs_pop[2:4]))
})
test_that("We can also use mkin solution methods for saem", {
expect_error(saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix", solution_type = "analytical"),
"saemix transformations is only supported if an analytical solution is implemented"
)
skip("This still takes almost 2.5 minutes although we do not solve ODEs")
dfop_saemix_3 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin",
solution_type = "analytical", no_random_effect = "parent_0")
distimes_dfop <- endpoints(dfop_saemix_1)$distimes
distimes_dfop_analytical <- endpoints(dfop_saemix_3)$distimes
rel_diff <- abs(distimes_dfop_analytical - distimes_dfop) / distimes_dfop
expect_true(all(rel_diff < 0.01))
})
|
