context("Nonlinear mixed-effects models") library(nlme) test_that("nlme_function works correctly", { sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) m_SFO <- mkinmod(parent = mkinsub("SFO")) d_SFO_1 <- mkinpredict(m_SFO, c(k_parent_sink = 0.1), c(parent = 98), sampling_times) d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = "time") d_SFO_2 <- mkinpredict(m_SFO, c(k_parent_sink = 0.05), c(parent = 102), sampling_times) d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = "time") d_SFO_3 <- mkinpredict(m_SFO, c(k_parent_sink = 0.02), c(parent = 103), sampling_times) d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time") d1 <- add_err(d_SFO_1, function(value) 3, n = 1, seed = 123456) d2 <- add_err(d_SFO_2, function(value) 2, n = 1, seed = 234567) d3 <- add_err(d_SFO_3, function(value) 4, n = 1, seed = 345678) ds <- c(d1 = d1, d2 = d2, d3 = d3) f <- mmkin("SFO", ds, cores = 1, quiet = TRUE) mean_dp <- mean_degparms(f) grouped_data <- nlme_data(f) nlme_f <- nlme_function(f) # The following assignment was introduced for nlme as evaluated by testthat # to find the function assign("nlme_f", nlme_f, pos = globalenv()) assign("sampling_times", sampling_times, pos = globalenv()) m_nlme_raw <- nlme(value ~ SSasymp(time, 0, parent_0, log_k_parent_sink), data = grouped_data, fixed = parent_0 + log_k_parent_sink ~ 1, random = pdDiag(parent_0 + log_k_parent_sink ~ 1), start = mean_dp) m_nlme_mkin <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink), data = grouped_data, fixed = parent_0 + log_k_parent_sink ~ 1, random = pdDiag(parent_0 + log_k_parent_sink ~ 1), start = mean_dp) expect_equal(m_nlme_raw$coefficients, m_nlme_mkin$coefficients) m_nlme_mmkin <- nlme(f) m_nlme_raw_2 <- nlme(value ~ SSasymp(time, 0, parent_0, log_k_parent_sink), data = grouped_data, fixed = parent_0 + log_k_parent_sink ~ 1, random = pdDiag(parent_0 + log_k_parent_sink ~ 1), start = mean_degparms(f, random = TRUE)) expect_equal(m_nlme_raw_2$coefficients, m_nlme_mmkin$coefficients) anova_nlme <- anova(m_nlme_mmkin, m_nlme_raw) # mmkin needs to go first as we had # to adapt the method due to # https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17761 # We get a slightly lower AIC with the improved starting values used within # nlme.mmkin expect_lt(anova_nlme["m_nlme_mmkin", "AIC"], anova_nlme["m_nlme_raw", "AIC"]) m_nlme_raw_up_1 <- update(m_nlme_raw, random = log_k_parent_sink ~ 1) # The following three calls give an error although they should # do the same as the call above # The error occurs in the evaluation of the modelExpression in the # call to .C(fit_nlme, ...) # m_nlme_mkin_up_1 <- update(m_nlme_mkin, random = log_k_parent_sink ~ 1) # m_nlme_mkin <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink), # data = grouped_data, # fixed = parent_0 + log_k_parent_sink ~ 1, # random = log_k_parent_sink ~ 1, # start = mean_dp) # update(m_nlme_mmkin, random = pdDiag(log_k_parent_sink ~ 1), # start = c(parent_0 = 100, log_k_parent_sink = 0.1)) m_nlme_raw_up_2 <- update(m_nlme_raw, random = parent_0 ~ 1) m_nlme_mkin_up_2 <- update(m_nlme_mkin, random = parent_0 ~ 1) expect_equal(m_nlme_raw_up_2$coefficients, m_nlme_mkin_up_2$coefficients) expect_silent(tmp <- update(m_nlme_mkin)) expect_silent(tmp <- update(m_nlme_mmkin)) }) test_that("nlme_function works correctly in other cases", { skip_on_cran() dt50_in <- c(400, 800, 1200, 1600, 2000) k_in <- log(2) / dt50_in SFO <- mkinmod(parent = mkinsub("SFO")) pred_sfo <- function(k) { mkinpredict(SFO, c(k_parent_sink = k), c(parent = 100), sampling_times) } ds_me_sfo <- mapply(pred_sfo, k_in, SIMPLIFY = FALSE) add_err_5 <- function(i) { add_err(ds_me_sfo[[i]], sdfunc = function(value) 5, n = 3, seed = i + 1) } ds_me_sfo_5 <- sapply(1:5, add_err_5) names(ds_me_sfo_5) <- paste("Dataset", 1:15) dimnames(ds_me_sfo_5) <- list(Subset = 1:3, DT50 = dt50_in) f_me_sfo_5 <- mmkin("SFO", ds_me_sfo_5, quiet = TRUE) ds_me_sfo_5_grouped_mkin <- nlme_data(f_me_sfo_5) ds_me_sfo_5_mean_dp <- mean_degparms(f_me_sfo_5) me_sfo_function <- nlme_function(f_me_sfo_5) assign("me_sfo_function", me_sfo_function, pos = globalenv()) f_nlme_sfo_5_all_mkin <- nlme(value ~ me_sfo_function(name, time, parent_0, log_k_parent_sink), data = ds_me_sfo_5_grouped_mkin, fixed = parent_0 + log_k_parent_sink ~ 1, random = pdDiag(parent_0 + log_k_parent_sink ~ 1), start = ds_me_sfo_5_mean_dp) f_nlme_sfo_5 <- nlme(value ~ SSasymp(time, 0, parent_0, log_k_parent_sink), data = ds_me_sfo_5_grouped_mkin, fixed = parent_0 + log_k_parent_sink ~ 1, random = pdDiag(parent_0 + log_k_parent_sink ~ 1), start = ds_me_sfo_5_mean_dp) expect_equal(f_nlme_sfo_5_all_mkin$coefficients, f_nlme_sfo_5$coefficients) # With less ideal starting values we get fits with lower AIC (not shown) f_nlme_sfo_5_all_mkin_nostart <- nlme(value ~ me_sfo_function(name, time, parent_0, log_k_parent_sink), data = ds_me_sfo_5_grouped_mkin, fixed = parent_0 + log_k_parent_sink ~ 1, random = pdDiag(parent_0 + log_k_parent_sink ~ 1), start = c(parent_0 = 100, log_k_parent_sink = log(0.1))) f_nlme_sfo_5_nostart <- nlme(value ~ SSasymp(time, 0, parent_0, log_k_parent_sink), data = ds_me_sfo_5_grouped_mkin, fixed = parent_0 + log_k_parent_sink ~ 1, random = pdDiag(parent_0 + log_k_parent_sink ~ 1), start = c(parent_0 = 100, log_k_parent_sink = log(0.1))) expect_equal(f_nlme_sfo_5_all_mkin_nostart$coefficients, f_nlme_sfo_5_nostart$coefficients) })