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
|
---
title: "Benchmark timings for saem.mmkin"
author: "Johannes Ranke"
date: Last change 14 November 2022 (rebuilt `r Sys.Date()`)
output:
html_document:
toc: true
toc_float: true
code_folding: show
fig_retina: null
vignette: >
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
library("knitr") # For the kable() function
opts_chunk$set(tidy = FALSE, cache = FALSE)
library("mkin")
```
Each system is characterized by operating system type, CPU type,
mkin version, saemix version and R version. A compiler was available, so if no
analytical solution was available, compiled ODE models are used.
Every fit is only performed once, so the accuracy of the benchmarks is limited.
```{r include = FALSE}
cpu_model <- benchmarkme::get_cpu()$model_name
# Abbreviate CPU identifiers
cpu_model <- gsub("AMD ", "", cpu_model)
cpu_model <- gsub("Intel\\(R\\) Core\\(TM\\) ", "", cpu_model)
cpu_model <- gsub(" Eight-Core Processor", "", cpu_model)
cpu_model <- gsub(" CPU @ 2.50GHz", "", cpu_model)
operating_system <- Sys.info()[["sysname"]]
mkin_version <- as.character(packageVersion("mkin"))
saemix_version <- as.character(packageVersion("saemix"))
R_version <- paste0(R.version$major, ".", R.version$minor)
system_string <- paste0(operating_system, ", ", cpu_model, ", mkin ", mkin_version, ", saemix ", saemix_version, ", R ", R_version)
benchmark_path = normalizePath("~/git/mkin/vignettes/web_only/saem_benchmarks.rda")
load(benchmark_path)
# Initialization 14 November 2022
#saem_benchmarks <- data.frame()
saem_benchmarks[system_string, c("CPU", "OS", "mkin", "saemix", "R")] <-
c(cpu_model, operating_system, mkin_version, saemix_version, R_version)
```
For the initial mmkin fits, we use all available cores.
```{r setup}
n_cores <- parallel::detectCores()
```
## Test data
Please refer to the vignette `dimethenamid_2018` for an explanation
of the following preprocessing.
```{r dimethenamid_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"]] <- NULL
dmta_ds[["Elliot 2"]] <- NULL
```
## Test cases
### Parent only
```{r parent_only}
parent_mods <- c("SFO", "DFOP", "SFORB", "HS")
parent_sep_const <- mmkin(parent_mods, dmta_ds, quiet = TRUE, cores = n_cores)
parent_sep_tc <- update(parent_sep_const, error_model = "tc")
t1 <- system.time(sfo_const <- saem(parent_sep_const["SFO", ]))[["elapsed"]]
t2 <- system.time(dfop_const <- saem(parent_sep_const["DFOP", ]))[["elapsed"]]
t3 <- system.time(sforb_const <- saem(parent_sep_const["SFORB", ]))[["elapsed"]]
t4 <- system.time(hs_const <- saem(parent_sep_const["HS", ]))[["elapsed"]]
t5 <- system.time(sfo_tc <- saem(parent_sep_tc["SFO", ]))[["elapsed"]]
t6 <- system.time(dfop_tc <- saem(parent_sep_tc["DFOP", ]))[["elapsed"]]
t7 <- system.time(sforb_tc <- saem(parent_sep_tc["SFORB", ]))[["elapsed"]]
t8 <- system.time(hs_tc <- saem(parent_sep_tc["HS", ]))[["elapsed"]]
```
```{r}
anova(
sfo_const, dfop_const, sforb_const, hs_const,
sfo_tc, dfop_tc, sforb_tc, hs_tc) |> kable(, digits = 1)
```
The above model comparison suggests to use the SFORB model with two-component
error. For comparison, we keep the DFOP model with two-component error,
as it competes with SFORB for biphasic curves.
```{r}
illparms(dfop_tc)
illparms(sforb_tc)
```
For these two models, random effects for the transformed parameters `k2` and
`k_DMTA_bound_free` could not be quantified.
### One metabolite
We remove parameters that were found to be ill-defined in the parent only fits.
```{r one_metabolite, message = FALSE}
one_met_mods <- list(
DFOP_SFO = mkinmod(
DMTA = mkinsub("DFOP", "M23"),
M23 = mkinsub("SFO")),
SFORB_SFO = mkinmod(
DMTA = mkinsub("SFORB", "M23"),
M23 = mkinsub("SFO")))
one_met_sep_const <- mmkin(one_met_mods, dmta_ds, error_model = "const",
cores = n_cores, quiet = TRUE)
one_met_sep_tc <- mmkin(one_met_mods, dmta_ds, error_model = "tc",
cores = n_cores, quiet = TRUE)
t9 <- system.time(dfop_sfo_tc <- saem(one_met_sep_tc["DFOP_SFO", ],
no_random_effect = "log_k2"))[["elapsed"]]
t10 <- system.time(sforb_sfo_tc <- saem(one_met_sep_tc["SFORB_SFO", ],
no_random_effect = "log_k_DMTA_bound_free"))[["elapsed"]]
```
### Three metabolites
For the case of three metabolites, we only keep the SFORB model in order
to limit the time for compiling this vignette, and as fitting in parallel
may disturb the benchmark. Again, we do not include random effects that
were ill-defined in previous fits of subsets of the degradation model.
```{r}
illparms(sforb_sfo_tc)
```
```{r three_metabolites, message = FALSE}
three_met_mods <- list(
SFORB_SFO3_plus = mkinmod(
DMTA = mkinsub("SFORB", c("M23", "M27", "M31")),
M23 = mkinsub("SFO"),
M27 = mkinsub("SFO"),
M31 = mkinsub("SFO", "M27", sink = FALSE)))
three_met_sep_tc <- mmkin(three_met_mods, dmta_ds, error_model = "tc",
cores = n_cores, quiet = TRUE)
t11 <- system.time(sforb_sfo3_plus_const <- saem(three_met_sep_tc["SFORB_SFO3_plus", ],
no_random_effect = "log_k_DMTA_bound_free"))[["elapsed"]]
```
```{r results, include = FALSE}
saem_benchmarks[system_string, paste0("t", 1:11)] <-
c(t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11)
save(saem_benchmarks, file = benchmark_path, version = 2)
# Hide rownames from kable for results section
rownames(saem_benchmarks) <- NULL
```
## Results
Benchmarks for all available error models are shown. They are intended for
improving mkin, not for comparing CPUs or operating systems. All trademarks
belong to their respective owners.
### Parent only
Constant variance for SFO, DFOP, SFORB and HS.
```{r, echo = FALSE}
kable(saem_benchmarks[, c(1:4, 6:9)])
```
Two-component error fits for SFO, DFOP, SFORB and HS.
```{r, echo = FALSE}
kable(saem_benchmarks[, c(1:4, 10:13)])
```
### One metabolite
Two-component error for DFOP-SFO and SFORB-SFO.
```{r, echo = FALSE}
kable(saem_benchmarks[, c(1:4, 14:15)])
```
### Three metabolites
Two-component error for SFORB-SFO3-plus
```{r, echo = FALSE}
kable(saem_benchmarks[, c(1:4, 16)])
```
|
