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 <- mmkin(models, list(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) # 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 needs 4 sec versus 5.5 for threestep and 6 for IRLS 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) need 4.8 se versus 5.6 for direct fit_tc_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "tc", quiet = TRUE, error_model_algorithm = "threestep")