context("Nonlinear mixed effects models fitted with SAEM from saemix") set.seed(123456) 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 SFO <- mkinmod(parent = mkinsub("SFO")) k_parent = rlnorm(n, log(0.03), log_sd) 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]] }) 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$g), log_sd)))) ds_dfop <- lapply(1:n, function(i) { ds_mean <- mkinpredict(DFOP, dfop_parms[i, ], c(parent = dfop_pop$parent_0), sampling_times) add_err(ds_mean, const, n = 1)[[1]] }) set.seed(123456) DFOP_SFO <- mkinmod( parent = mkinsub("DFOP", "m1"), m1 = mkinsub("SFO"), quiet = TRUE) syn_biphasic_parms <- as.matrix(data.frame( k1 = rlnorm(n_biphasic, log(0.05), log_sd), k2 = rlnorm(n_biphasic, log(0.01), log_sd), g = plogis(rnorm(n_biphasic, 0, log_sd)), f_parent_to_m1 = plogis(rnorm(n_biphasic, 0, log_sd)), k_m1 = rlnorm(n_biphasic, log(0.002), 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) } ) 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]] }) test_that("Parent only models can be fitted with saemix", { # Some fits were done in the setup script mmkin_sfo_2 <- mmkin("SFO", ds_sfo, fixed_initials = c(parent = 100), quiet = TRUE) sfo_saemix_2 <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "mkin") sfo_saemix_3 <- expect_error(saem(mmkin_sfo_2, quiet = TRUE), "at least two parameters") s_sfo_s1 <- summary(sfo_saemix_1) s_sfo_s2 <- summary(sfo_saemix_2) sfo_nlme_1 <- expect_warning(nlme(mmkin_sfo_1), "not converge") s_sfo_n <- summary(sfo_nlme_1) # Compare with input expect_equal(round(s_sfo_s2$confint_ranef["SD.log_k_parent", "est."], 1), 0.3) # k_parent is a bit different from input 0.03 here expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3), 0.035) expect_equal(round(s_sfo_s2$confint_back["k_parent", "est."], 3), 0.035) # But the result is pretty unanimous between methods expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3), round(s_sfo_s2$confint_back["k_parent", "est."], 3)) expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3), round(s_sfo_n$confint_back["k_parent", "est."], 3)) mmkin_dfop_1 <- mmkin("DFOP", ds_dfop, quiet = TRUE) dfop_saemix_1 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin") dfop_saemix_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix") dfop_nlme_1 <- nlme(mmkin_dfop_1) s_dfop_s1 <- summary(dfop_saemix_1) s_dfop_s2 <- summary(dfop_saemix_2) s_dfop_n <- summary(dfop_nlme_1) dfop_pop <- as.numeric(dfop_pop) expect_true(all(s_dfop_s1$confint_back[, "lower"] < dfop_pop)) expect_true(all(s_dfop_s1$confint_back[, "upper"] > dfop_pop)) expect_true(all(s_dfop_s2$confint_back[, "lower"] < dfop_pop)) expect_true(all(s_dfop_s2$confint_back[, "upper"] > dfop_pop)) # We get < 20% deviations with transformations made in mkin rel_diff_1 <- (s_dfop_s1$confint_back[, "est."] - dfop_pop) / dfop_pop expect_true(all(rel_diff_1 < 0.2)) # We get < 8% deviations with transformations made in saemix rel_diff_2 <- (s_dfop_s2$confint_back[, "est."] - dfop_pop) / dfop_pop expect_true(all(rel_diff_2 < 0.08)) }) test_that("Simple models with metabolite can be fitted with saemix", { expect_known_output(print(saem_biphasic_s, digits = 1), "print_saem_biphasic_s.txt") test_summary <- summary(saem_biphasic_s) test_summary$saemixversion <- "Dummy 0.0 for testing" test_summary$mkinversion <- "Dummy 0.0 for testing" test_summary$Rversion <- "Dummy R version for testing" test_summary$date.fit <- "Dummy date for testing" test_summary$date.summary <- "Dummy date for testing" test_summary$time <- c(elapsed = "test time 0") expect_known_output(print(test_summary, digits = 2), "summary_saem_biphasic_s.txt") dfop_sfo_pop <- as.numeric(dfop_sfo_pop) ci_dfop_sfo_s_s <- summary(saem_biphasic_s)$confint_back expect_true(all(ci_dfop_sfo_s_s[, "lower"] < dfop_sfo_pop)) expect_true(all(ci_dfop_sfo_s_s[, "upper"] > dfop_sfo_pop)) # The following does not work, as k1 and k2 are not fitted well ci_dfop_sfo_s_m <- summary(saem_biphasic_m)$confint_back # expect_true(all(ci_dfop_sfo_s_m[, "lower"] < dfop_sfo_pop)) # expect_true(all(ci_dfop_sfo_s_m[, "upper"] > dfop_sfo_pop)) # Somehow this does not work at the moment. But it took forever (~ 10 min) anyways... #saem_biphasic_2 <- saem(mmkin_biphasic, solution_type = "deSolve", quiet = TRUE) })