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
|
context("Dimethenamid data from 2018, parent fits")
# Data
dmta_ds <- lapply(1:7, function(i) {
ds_i <- dimethenamid_2018$ds[[i]]$data
ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA"
ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]
ds_i
})
names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title)
dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]])
dmta_ds[["Elliot 1"]] <- dmta_ds[["Elliot 2"]] <- NULL
# mkin
dmta_dfop <- mmkin("DFOP", dmta_ds, quiet = TRUE)
dmta_dfop_tc <- mmkin("DFOP", dmta_ds, error_model = "tc", quiet = TRUE)
test_that("Different backends get consistent results for DFOP tc, dimethenamid data", {
# nlme
expect_warning(
nlme_dfop_tc <- nlme(dmta_dfop_tc),
"Iteration 3, .* false convergence")
ints_nlme <- intervals(nlme_dfop_tc)
# saemix
saem_saemix_dfop_tc <- saem(dmta_dfop_tc)
ints_saemix <- intervals(saem_saemix_dfop_tc)
# saemix mkin transformations
saem_saemix_dfop_tc_mkin <- saem(dmta_dfop_tc, transformations = "mkin")
ints_saemix_mkin <- intervals(saem_saemix_dfop_tc_mkin)
# nlmixr saem
saem_nlmixr_dfop_tc <- nlmixr(dmta_dfop_tc, est = "saem",
control = nlmixr::saemControl(nBurn = 300, nEm = 100, nmc = 9, print = 0))
ints_nlmixr_saem <- intervals(saem_nlmixr_dfop_tc)
# nlmixr focei
# We get three warnings about nudged etas, the initial optimization and
# gradient problems with initial estimate and covariance
# We need to capture output, otherwise it pops up in testthat output
expect_warning(tmp <- capture_output(focei_nlmixr_dfop_tc <- nlmixr(
dmta_dfop_tc, est = "focei",
control = nlmixr::foceiControl(print = 0), all = TRUE)))
ints_nlmixr_focei <- intervals(focei_nlmixr_dfop_tc)
# Fixed effects
## saemix vs. nlme
expect_true(all(ints_saemix$fixed[, "est."] >
backtransform_odeparms(ints_nlme$fixed[, "lower"], dmta_dfop$mkinmod)))
expect_true(all(ints_saemix$fixed[, "est."] <
backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod)))
## saemix mkin vs. nlme
expect_true(all(ints_saemix_mkin$fixed[, "est."] >
backtransform_odeparms(ints_nlme$fixed[, "lower"], dmta_dfop$mkinmod)))
expect_true(all(ints_saemix_mkin$fixed[, "est."] <
backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod)))
## nlmixr saem vs. nlme
expect_true(all(ints_nlmixr_saem$fixed[, "est."] >
backtransform_odeparms(ints_nlme$fixed[, "lower"], dmta_dfop$mkinmod)))
expect_true(all(ints_nlmixr_saem$fixed[, "est."] <
backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod)))
## nlmixr focei vs. nlme
expect_true(all(ints_nlmixr_focei$fixed[, "est."] >
backtransform_odeparms(ints_nlme$fixed[, "lower"], dmta_dfop$mkinmod)))
expect_true(all(ints_nlmixr_focei$fixed[, "est."] <
backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod)))
# Random effects
## for saemix with saemix transformations, the comparison would be complicated...
## saemix mkin vs. nlme
expect_true(all(ints_saemix$random[, "est."] >
backtransform_odeparms(ints_nlme$reStruct$ds[, "lower"], dmta_dfop$mkinmod)))
expect_true(all(ints_saemix$fixed[, "est."] <
backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod)))
## nlmixr saem vs. nlme
expect_true(all(ints_nlmixr_saem$random[, "est."] >
backtransform_odeparms(ints_nlme$reStruct$ds[, "lower"], dmta_dfop$mkinmod)))
expect_true(all(ints_nlmixr_saem$random[, "est."] <
backtransform_odeparms(ints_nlme$reStruct$ds[, "upper"], dmta_dfop$mkinmod)))
## nlmixr focei vs. nlme
expect_true(all(ints_nlmixr_focei$random[, "est."] >
backtransform_odeparms(ints_nlme$reStruct$ds[, "lower"], dmta_dfop$mkinmod)))
expect_true(all(ints_nlmixr_focei$random[, "est."] <
backtransform_odeparms(ints_nlme$reStruct$ds[, "upper"], dmta_dfop$mkinmod)))
# Variance function
# saemix vs. nlme
expect_true(all(ints_saemix[[3]][, "est."] >
ints_nlme$varStruct[, "lower"]))
expect_true(all(ints_saemix[[3]][, "est."] <
ints_nlme$varStruct[, "upper"]))
# saemix with mkin transformations vs. nlme
expect_true(all(ints_saemix_mkin[[3]][, "est."] >
ints_nlme$varStruct[, "lower"]))
expect_true(all(ints_saemix_mkin[[3]][, "est."] <
ints_nlme$varStruct[, "upper"]))
# nlmixr saem vs. nlme
expect_true(all(ints_nlmixr_saem[[3]][, "est."] >
ints_nlme$varStruct[, "lower"]))
expect_true(all(ints_nlmixr_saem[[3]][, "est."] <
ints_nlme$varStruct[, "upper"]))
# nlmixr focei vs. nlme
# We only test for the proportional part (rsd_high), as the
# constant part (sigma_low) obtained with nlmixr/FOCEI is below the lower
# bound of the confidence interval obtained with nlme
expect_true(ints_nlmixr_focei[[3]]["rsd_high", "est."] >
ints_nlme$varStruct["prop", "lower"])
expect_true(ints_nlmixr_focei[[3]]["rsd_high", "est."] <
ints_nlme$varStruct["prop", "upper"])
})
|
