vignettes/web_only/saem_benchmarks.rmd
saem_benchmarks.rmdEach 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.
For the initial mmkin fits, we use all available cores.
n_cores <- parallel::detectCores()Please refer to the vignette dimethenamid_2018 for an explanation of the following preprocessing.
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
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"]]
anova(
sfo_const, dfop_const, sforb_const, hs_const,
sfo_tc, dfop_tc, sforb_tc, hs_tc) |> kable(, digits = 1)| npar | AIC | BIC | Lik | |
|---|---|---|---|---|
| sfo_const | 5 | 796.3 | 795.3 | -393.2 |
| sfo_tc | 6 | 798.3 | 797.1 | -393.2 |
| dfop_const | 9 | 709.4 | 707.5 | -345.7 |
| sforb_const | 9 | 710.0 | 708.1 | -346.0 |
| hs_const | 9 | 713.7 | 711.8 | -347.8 |
| dfop_tc | 10 | 669.8 | 667.7 | -324.9 |
| sforb_tc | 10 | 662.8 | 660.7 | -321.4 |
| hs_tc | 10 | 667.3 | 665.2 | -323.6 |
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.
illparms(dfop_tc)## [1] "sd(log_k2)"
illparms(sforb_tc)## [1] "sd(log_k_DMTA_bound_free)"For these two models, random effects for the transformed parameters k2 and k_DMTA_bound_free could not be quantified.
We remove parameters that were found to be ill-defined in the parent only fits.
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"]]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.
illparms(sforb_sfo_tc)
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"]]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.
Constant variance for SFO, DFOP, SFORB and HS.
| CPU | OS | mkin | saemix | t1 | t2 | t3 | t4 |
|---|---|---|---|---|---|---|---|
| Ryzen 7 1700 | Linux | 1.2.0 | 3.2 | 2.140 | 4.626 | 4.328 | 4.998 |
| Ryzen 7 1700 | Linux | 1.2.2 | 3.2 | 2.427 | 4.550 | 4.217 | 4.851 |
Two-component error fits for SFO, DFOP, SFORB and HS.
| CPU | OS | mkin | saemix | t5 | t6 | t7 | t8 |
|---|---|---|---|---|---|---|---|
| Ryzen 7 1700 | Linux | 1.2.0 | 3.2 | 5.678 | 7.441 | 8.000 | 7.980 |
| Ryzen 7 1700 | Linux | 1.2.2 | 3.2 | 5.352 | 7.201 | 8.174 | 8.401 |