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
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
|
require(mkin)
require(testthat)
# Per default (on my box where I set NOT_CRAN) use all cores minus one
if (identical(Sys.getenv("NOT_CRAN"), "true")) {
n_cores <- parallel::detectCores() - 1
} else {
n_cores <- 1
}
# We are only allowed one core on travis, but they also set NOT_CRAN=true
if (Sys.getenv("TRAVIS") != "") n_cores = 1
# On Windows we would need to make a cluster first
if (Sys.info()["sysname"] == "Windows") n_cores = 1
# We set up some models and fits with nls for comparisons
SFO_trans <- function(t, parent_0, log_k_parent_sink) {
parent_0 * exp(- exp(log_k_parent_sink) * t)
}
SFO_notrans <- function(t, parent_0, k_parent_sink) {
parent_0 * exp(- k_parent_sink * t)
}
f_1_nls_trans <- nls(value ~ SFO_trans(time, parent_0, log_k_parent_sink),
data = FOCUS_2006_A,
start = list(parent_0 = 100, log_k_parent_sink = log(0.1)))
f_1_nls_notrans <- nls(value ~ SFO_notrans(time, parent_0, k_parent_sink),
data = FOCUS_2006_A,
start = list(parent_0 = 100, k_parent_sink = 0.1))
f_1_mkin_trans <- mkinfit("SFO", FOCUS_2006_A, quiet = TRUE)
f_1_mkin_notrans <- mkinfit("SFO", FOCUS_2006_A, quiet = TRUE,
transform_rates = FALSE)
# mmkin object of parent fits for tests
models <- c("SFO", "FOMC", "DFOP", "HS")
fits <- suppressWarnings( # FOCUS A FOMC was, it seems, in testthat output
mmkin(models,
list(FOCUS_A = FOCUS_2006_A, FOCUS_C = FOCUS_2006_C, FOCUS_D = FOCUS_2006_D),
quiet = TRUE, cores = n_cores))
# One metabolite
SFO_SFO <- mkinmod(parent = mkinsub("SFO", to = "m1"),
m1 = mkinsub("SFO"),
use_of_ff = "min", quiet = TRUE)
SFO_SFO.ff <- mkinmod(parent = mkinsub("SFO", to = "m1"),
m1 = mkinsub("SFO"),
use_of_ff = "max", quiet = TRUE)
SFO_SFO.ff.nosink <- mkinmod(
parent = mkinsub("SFO", "m1", sink = FALSE),
m1 = mkinsub("SFO"), quiet = TRUE, use_of_ff = "max")
FOMC_SFO <- mkinmod(parent = mkinsub("FOMC", to = "m1"),
m1 = mkinsub("SFO"), quiet = TRUE)
DFOP_SFO <- mkinmod(parent = mkinsub("DFOP", to = "m1"),
m1 = mkinsub("SFO"),
use_of_ff = "max", quiet = TRUE)
# Avoid warning when fitting a dataset where zero value is removed
FOCUS_D <- subset(FOCUS_2006_D, value != 0)
f_sfo_sfo_desolve <- mkinfit(SFO_SFO, FOCUS_D,
solution_type = "deSolve", quiet = TRUE)
f_sfo_sfo_eigen <- mkinfit(SFO_SFO, FOCUS_D,
solution_type = "eigen", quiet = TRUE)
f_sfo_sfo.ff <- mkinfit(SFO_SFO.ff, FOCUS_D,
quiet = TRUE)
SFO_lin_a <- synthetic_data_for_UBA_2014[[1]]$data
DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data
f_2_mkin <- mkinfit("DFOP", DFOP_par_c, quiet = TRUE)
f_2_nls <- nls(value ~ SSbiexp(time, A1, lrc1, A2, lrc2), data = subset(DFOP_par_c, name == "parent"))
f_2_anova <- lm(value ~ as.factor(time), data = subset(DFOP_par_c, name == "parent"))
# Two metabolites
m_synth_SFO_lin <- mkinmod(
parent = mkinsub("SFO", "M1"),
M1 = mkinsub("SFO", "M2"),
M2 = mkinsub("SFO"),
use_of_ff = "max", quiet = TRUE)
m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")),
M1 = mkinsub("SFO"),
M2 = mkinsub("SFO"),
use_of_ff = "max", quiet = TRUE)
fit_nw_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, quiet = TRUE)
# We know direct optimization is OK and direct is faster than the default d_3
fit_obs_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "obs", quiet = TRUE,
error_model_algorithm = "direct")
# We know threestep is OK, and threestep (and IRLS) is faster here
fit_tc_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "tc", quiet = TRUE,
error_model_algorithm = "threestep")
# Mixed models data and
sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
n <- n_biphasic <- 15
log_sd <- 0.3
err_1 = list(const = 1, prop = 0.05)
tc <- function(value) sigma_twocomp(value, err_1$const, err_1$prop)
const <- function(value) 2
set.seed(123456)
SFO <- mkinmod(parent = mkinsub("SFO"))
k_parent = rlnorm(n, log(0.03), log_sd)
ds_sfo <- lapply(1:n, function(i) {
ds_mean <- mkinpredict(SFO, c(k_parent = k_parent[i]),
c(parent = 100), sampling_times)
add_err(ds_mean, tc, n = 1)[[1]]
})
set.seed(123456)
FOMC <- mkinmod(parent = mkinsub("FOMC"))
fomc_pop <- list(parent_0 = 100, alpha = 2, beta = 8)
fomc_parms <- as.matrix(data.frame(
alpha = rlnorm(n, log(fomc_pop$alpha), 0.4),
beta = rlnorm(n, log(fomc_pop$beta), 0.2)))
ds_fomc <- lapply(1:3, function(i) {
ds_mean <- mkinpredict(FOMC, fomc_parms[i, ],
c(parent = 100), sampling_times)
add_err(ds_mean, tc, n = 1)[[1]]
})
set.seed(123456)
DFOP <- mkinmod(parent = mkinsub("DFOP"))
dfop_pop <- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4)
dfop_parms <- as.matrix(data.frame(
k1 = rlnorm(n, log(dfop_pop$k1), log_sd),
k2 = rlnorm(n, log(dfop_pop$k2), log_sd),
g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd))))
ds_dfop <- lapply(1:n, function(i) {
ds_mean <- mkinpredict(DFOP, dfop_parms[i, ],
c(parent = dfop_pop$parent_0), sampling_times)
add_err(ds_mean, const, n = 1)[[1]]
})
set.seed(123456)
HS <- mkinmod(parent = mkinsub("HS"))
hs_pop <- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15)
hs_parms <- as.matrix(data.frame(
k1 = rlnorm(n, log(hs_pop$k1), log_sd),
k2 = rlnorm(n, log(hs_pop$k2), log_sd),
tb = rlnorm(n, log(hs_pop$tb), 0.1)))
ds_hs <- lapply(1:10, function(i) {
ds_mean <- mkinpredict(HS, hs_parms[i, ],
c(parent = hs_pop$parent_0), sampling_times)
add_err(ds_mean, const, n = 1)[[1]]
})
set.seed(123456)
DFOP_SFO <- mkinmod(
parent = mkinsub("DFOP", "m1"),
m1 = mkinsub("SFO"),
quiet = TRUE)
dfop_sfo_pop <- list(parent_0 = 100,
k_m1 = 0.002, f_parent_to_m1 = 0.5,
k1 = 0.05, k2 = 0.01, g = 0.5)
syn_biphasic_parms <- as.matrix(data.frame(
k1 = rlnorm(n_biphasic, log(dfop_sfo_pop$k1), log_sd),
k2 = rlnorm(n_biphasic, log(dfop_sfo_pop$k2), log_sd),
g = plogis(rnorm(n_biphasic, qlogis(dfop_sfo_pop$g), log_sd)),
f_parent_to_m1 = plogis(rnorm(n_biphasic,
qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)),
k_m1 = rlnorm(n_biphasic, log(dfop_sfo_pop$k_m1), log_sd)))
ds_biphasic_mean <- lapply(1:n_biphasic,
function(i) {
mkinpredict(DFOP_SFO, syn_biphasic_parms[i, ],
c(parent = 100, m1 = 0), sampling_times)
}
)
ds_biphasic <- lapply(ds_biphasic_mean, function(ds) {
add_err(ds,
sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2),
n = 1, secondary = "m1")[[1]]
})
# Mixed model fits
mmkin_sfo_1 <- mmkin("SFO", ds_sfo, quiet = TRUE, error_model = "tc")
sfo_saem_1 <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "saemix")
mmkin_dfop_1 <- mmkin("DFOP", ds_dfop, quiet = TRUE)
dfop_saemix_1 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin")
dfop_saemix_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix")
dfop_nlme_1 <- nlme(mmkin_dfop_1)
mmkin_biphasic <- mmkin(list("DFOP-SFO" = DFOP_SFO), ds_biphasic, quiet = TRUE)
mmkin_biphasic_mixed <- mixed(mmkin_biphasic)
nlme_biphasic <- nlme(mmkin_biphasic)
saem_biphasic_m <- saem(mmkin_biphasic, transformations = "mkin", quiet = TRUE)
saem_biphasic_s <- saem(mmkin_biphasic, transformations = "saemix", quiet = TRUE)
ds_uba <- lapply(experimental_data_for_UBA_2019[6:10],
function(x) subset(x$data[c("name", "time", "value")]))
names(ds_uba) <- paste("Dataset", 6:10)
sfo_sfo_uba <- mkinmod(parent = mkinsub("SFO", "A1"),
A1 = mkinsub("SFO"), quiet = TRUE)
dfop_sfo_uba <- mkinmod(parent = mkinsub("DFOP", "A1"),
A1 = mkinsub("SFO"), quiet = TRUE)
f_uba_mmkin <- mmkin(list("SFO-SFO" = sfo_sfo_uba, "DFOP-SFO" = dfop_sfo_uba),
ds_uba, quiet = TRUE)
f_uba_dfop_sfo_mixed <- mixed(f_uba_mmkin[2, ])
f_uba_sfo_sfo_saem <- saem(f_uba_mmkin["SFO-SFO", ], quiet = TRUE, transformations = "saemix")
#f_uba_sfo_sfo_saem <- saem(f_uba_mmkin["SFO-SFO", ], solution_type = "deSolve", quiet = TRUE) # currently fails
f_uba_dfop_sfo_saem <- saem(f_uba_mmkin["DFOP-SFO", ], quiet = TRUE, transformations = "saemix")
|
