diff options
Diffstat (limited to 'tests/testthat/setup_script.R')
| -rw-r--r-- | tests/testthat/setup_script.R | 120 |
1 files changed, 20 insertions, 100 deletions
diff --git a/tests/testthat/setup_script.R b/tests/testthat/setup_script.R index 777c998a..4e2f76ab 100644 --- a/tests/testthat/setup_script.R +++ b/tests/testthat/setup_script.R @@ -2,17 +2,23 @@ require(mkin) require(testthat) # Per default (on my box where I set NOT_CRAN in .Rprofile) use all cores minus one +# Otherwise (CRAN check systems) use the allowed maximum of two cores if (identical(Sys.getenv("NOT_CRAN"), "true")) { n_cores <- parallel::detectCores() - 1 } else { - n_cores <- 1 + n_cores <- 2 } # Use the two available cores on travis if (Sys.getenv("TRAVIS") != "") n_cores = 2 -# On Windows we would need to make a cluster first -if (Sys.info()["sysname"] == "Windows") n_cores = 1 +# On Windows we need to make a cluster, or use one core +if (Sys.info()["sysname"] == "Windows") { + cl <- parallel::makePSOCKcluster(n_cores) + n_cores = 1 +} else { + cl <- parallel::makeForkCluster(n_cores) +} # Very simple example fits f_1_mkin_trans <- mkinfit("SFO", FOCUS_2006_A, quiet = TRUE) @@ -24,7 +30,7 @@ models <- c("SFO", "FOMC", "DFOP", "HS") fits <- suppressWarnings( # FOCUS A FOMC was, it seems, in testthat output mmkin(models, list(FOCUS_A = FOCUS_2006_A, FOCUS_C = FOCUS_2006_C, FOCUS_D = FOCUS_2006_D), - quiet = TRUE, cores = n_cores)) + quiet = TRUE, cluster = cl)) # One metabolite SFO_SFO <- mkinmod(parent = mkinsub("SFO", to = "m1"), @@ -81,112 +87,26 @@ fit_obs_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "obs", quiet = TR fit_tc_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "tc", quiet = TRUE, error_model_algorithm = "threestep") -# Mixed models data and fits -sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) -n <- n_biphasic <- 15 -log_sd <- 0.3 -err_1 = list(const = 1, prop = 0.05) -tc <- function(value) sigma_twocomp(value, err_1$const, err_1$prop) -const <- function(value) 2 - -set.seed(123456) -SFO <- mkinmod(parent = mkinsub("SFO")) -k_parent = rlnorm(n, log(0.03), log_sd) -set.seed(123456) -ds_sfo <- lapply(1:n, function(i) { - ds_mean <- mkinpredict(SFO, c(k_parent = k_parent[i]), - c(parent = 100), sampling_times) - add_err(ds_mean, tc, n = 1)[[1]] -}) - -set.seed(123456) -FOMC <- mkinmod(parent = mkinsub("FOMC")) -fomc_pop <- list(parent_0 = 100, alpha = 2, beta = 8) -fomc_parms <- as.matrix(data.frame( - alpha = rlnorm(n, log(fomc_pop$alpha), 0.4), - beta = rlnorm(n, log(fomc_pop$beta), 0.2))) -set.seed(123456) -ds_fomc <- lapply(1:3, function(i) { - ds_mean <- mkinpredict(FOMC, fomc_parms[i, ], - c(parent = 100), sampling_times) - add_err(ds_mean, tc, n = 1)[[1]] -}) - -set.seed(123456) -DFOP <- mkinmod(parent = mkinsub("DFOP")) -dfop_pop <- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4) -dfop_parms <- as.matrix(data.frame( - k1 = rlnorm(n, log(dfop_pop$k1), log_sd), - k2 = rlnorm(n, log(dfop_pop$k2), log_sd), - g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd)))) -set.seed(123456) -ds_dfop <- lapply(1:n, function(i) { - ds_mean <- mkinpredict(DFOP, dfop_parms[i, ], - c(parent = dfop_pop$parent_0), sampling_times) - add_err(ds_mean, tc, n = 1)[[1]] -}) - -set.seed(123456) -HS <- mkinmod(parent = mkinsub("HS")) -hs_pop <- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15) -hs_parms <- as.matrix(data.frame( - k1 = rlnorm(n, log(hs_pop$k1), log_sd), - k2 = rlnorm(n, log(hs_pop$k2), log_sd), - tb = rlnorm(n, log(hs_pop$tb), 0.1))) -set.seed(123456) -ds_hs <- lapply(1:10, function(i) { - ds_mean <- mkinpredict(HS, hs_parms[i, ], - c(parent = hs_pop$parent_0), sampling_times) - add_err(ds_mean, const, n = 1)[[1]] -}) - -set.seed(123456) -DFOP_SFO <- mkinmod( - parent = mkinsub("DFOP", "m1"), - m1 = mkinsub("SFO"), - quiet = TRUE) -dfop_sfo_pop <- list(parent_0 = 100, - k_m1 = 0.007, f_parent_to_m1 = 0.5, - k1 = 0.1, k2 = 0.02, g = 0.5) -syn_biphasic_parms <- as.matrix(data.frame( - k1 = rlnorm(n_biphasic, log(dfop_sfo_pop$k1), log_sd), - k2 = rlnorm(n_biphasic, log(dfop_sfo_pop$k2), log_sd), - g = plogis(rnorm(n_biphasic, qlogis(dfop_sfo_pop$g), log_sd)), - f_parent_to_m1 = plogis(rnorm(n_biphasic, - qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)), - k_m1 = rlnorm(n_biphasic, log(dfop_sfo_pop$k_m1), log_sd))) -ds_biphasic_mean <- lapply(1:n_biphasic, - function(i) { - mkinpredict(DFOP_SFO, syn_biphasic_parms[i, ], - c(parent = 100, m1 = 0), sampling_times) - } -) -set.seed(123456) -ds_biphasic <- lapply(ds_biphasic_mean, function(ds) { - add_err(ds, - sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2), - n = 1, secondary = "m1")[[1]] -}) - # Mixed model fits -mmkin_sfo_1 <- mmkin("SFO", ds_sfo, quiet = TRUE, error_model = "tc", cores = n_cores) -mmkin_dfop_1 <- mmkin("DFOP", ds_dfop, quiet = TRUE, cores = n_cores, +mmkin_sfo_1 <- mmkin("SFO", ds_sfo, quiet = TRUE, error_model = "tc", cluster = cl) +mmkin_dfop_1 <- mmkin("DFOP", ds_dfop, quiet = TRUE, cluster = cl, error_model = "tc") -mmkin_biphasic <- mmkin(list("DFOP-SFO" = DFOP_SFO), ds_biphasic, quiet = TRUE, cores = n_cores, +DFOP_SFO <- mkinmod(parent = mkinsub("DFOP", "m1"), + m1 = mkinsub("SFO"), quiet = TRUE) +mmkin_dfop_sfo <- mmkin(list("DFOP-SFO" = DFOP_SFO), ds_dfop_sfo, quiet = TRUE, + cluster = cl, control = list(eval.max = 500, iter.max = 400), error_model = "tc") # nlme dfop_nlme_1 <- suppressWarnings(nlme(mmkin_dfop_1)) -nlme_biphasic <- suppressWarnings(nlme(mmkin_biphasic)) +nlme_dfop_sfo <- suppressWarnings(nlme(mmkin_dfop_sfo)) # saemix sfo_saem_1 <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "saemix") sfo_saem_1_reduced <- update(sfo_saem_1, no_random_effect = "parent_0") -dfop_saemix_1 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin", - no_random_effect = "parent_0") - -saem_biphasic_m <- saem(mmkin_biphasic, transformations = "mkin", quiet = TRUE) -saem_biphasic_s <- saem(mmkin_biphasic, transformations = "saemix", quiet = TRUE) +dfop_saem_1 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin", + no_random_effect = c("parent_0", "g_qlogis")) +parallel::stopCluster(cl) |