In jranke/mkin: Kinetic Evaluation of Chemical Degradation Data

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.

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(" 16-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)



if (dir.exists("~/git/mkin")) {
  benchmark_path = normalizePath("~/git/mkin/vignettes/web_only/saem_benchmarks.rda")
} else {
  benchmark_path = normalizePath("~/projects/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.

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)

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)
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.

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"]]

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.

kable(saem_benchmarks[, c(1:4, 6:9)])

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

kable(saem_benchmarks[, c(1:4, 10:13)])

One metabolite

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

kable(saem_benchmarks[, c(1:4, 14:15)])

Three metabolites

Two-component error for SFORB-SFO3-plus

kable(saem_benchmarks[, c(1:4, 16)])

jranke/mkin documentation built on April 29, 2024, 7:33 a.m.