Benchmark timings for saem.mmkin

+ + + + +

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.

For the initial mmkin fits, we use all available cores.

+n_cores <- parallel::detectCores()

Test data +

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

Test cases +

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"]]

+anova(
+  sfo_const, dfop_const, sforb_const, hs_const,
+  sfo_tc, dfop_tc, sforb_tc, hs_tc) |> kable(, digits = 1)

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	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.

One metabolite +

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"]]

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.

+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"]]

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.

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CPU	OS	mkin	saemix	t1	t2	t3	t4
Ryzen 7 1700	Linux	1.2.0	3.2	2.589	4.945	4.786	4.484

Two-component error fits for SFO, DFOP, SFORB and HS.

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CPU	OS	mkin	saemix	t5	t6	t7	t8
Ryzen 7 1700	Linux	1.2.0	3.2	5.645	8.115	7.737	7.782

One metabolite +

Two-component error for DFOP-SFO and SFORB-SFO.

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CPU	OS	mkin	saemix	t9	t10
Ryzen 7 1700	Linux	1.2.0	3.2	25.921	766.034

Three metabolites +

Two-component error for SFORB-SFO3-plus

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CPU	OS	mkin	saemix	t11
Ryzen 7 1700	Linux	1.2.0	3.2	1269.799

+ + + +

Johannes Ranke

Last change 14 November 2022 (rebuilt 2022-11-14)

Test data +

Test cases +

Parent only +

One metabolite +

Three metabolites +

Results +

Parent only +

One metabolite +

Three metabolites +