context("Roundtripping error model parameters") # 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 test_that("Reweighting method 'tc' produces reasonable variance estimates", { # Check if we can approximately obtain the parameters and the error model # components that were used in the data generation # Parent only DFOP <- mkinmod(parent = mkinsub("DFOP")) sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) parms_DFOP <- c(k1 = 0.2, k2 = 0.02, g = 0.5) parms_DFOP_optim <- c(parent_0 = 100, parms_DFOP) d_DFOP <- mkinpredict(DFOP, parms_DFOP, c(parent = 100), sampling_times) d_2_10 <- add_err(d_DFOP, sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07), n = 10, reps = 2, digits = 5, LOD = -Inf, seed = 123456) d_100_1 <- add_err(d_DFOP, sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07), n = 1, reps = 100, digits = 5, LOD = -Inf, seed = 123456) # Unweighted fits f_2_10 <- mmkin("DFOP", d_2_10, error_model = "const", quiet = TRUE, cores = n_cores) parms_2_10 <- apply(sapply(f_2_10, function(x) x$bparms.optim), 1, mean) parm_errors_2_10 <- (parms_2_10 - parms_DFOP_optim) / parms_DFOP_optim expect_true(all(abs(parm_errors_2_10) < 0.12)) f_2_10_tc <- mmkin("DFOP", d_2_10, error_model = "tc", quiet = TRUE, cores = n_cores) parms_2_10_tc <- apply(sapply(f_2_10_tc, function(x) x$bparms.optim), 1, mean) parm_errors_2_10_tc <- (parms_2_10_tc - parms_DFOP_optim) / parms_DFOP_optim expect_true(all(abs(parm_errors_2_10_tc) < 0.05)) tcf_2_10_tc <- apply(sapply(f_2_10_tc, function(x) x$errparms), 1, mean, na.rm = TRUE) tcf_2_10_error_model_errors <- (tcf_2_10_tc - c(0.5, 0.07)) / c(0.5, 0.07) expect_true(all(abs(tcf_2_10_error_model_errors) < 0.2)) # When we have 100 replicates in the synthetic data, we can roundtrip # the parameters with < 2% precision f_tc_100_1 <- mkinfit(DFOP, d_100_1[[1]], error_model = "tc", quiet = TRUE) parm_errors_100_1 <- (f_tc_100_1$bparms.optim - parms_DFOP_optim) / parms_DFOP_optim expect_true(all(abs(parm_errors_100_1) < 0.02)) tcf_100_1_error_model_errors <- (f_tc_100_1$errparms - c(0.5, 0.07)) / c(0.5, 0.07) # We also get a precision of < 2% for the error model components expect_true(all(abs(tcf_100_1_error_model_errors) < 0.02)) # Parent and two metabolites m_synth_DFOP_lin <- mkinmod(parent = list(type = "DFOP", to = "M1"), M1 = list(type = "SFO", to = "M2"), M2 = list(type = "SFO"), use_of_ff = "max", quiet = TRUE) sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) parms_DFOP_lin <- c(k1 = 0.2, k2 = 0.02, g = 0.5, f_parent_to_M1 = 0.5, k_M1 = 0.3, f_M1_to_M2 = 0.7, k_M2 = 0.02) d_synth_DFOP_lin <- mkinpredict(m_synth_DFOP_lin, parms_DFOP_lin, c(parent = 100, M1 = 0, M2 = 0), sampling_times) parms_DFOP_lin_optim = c(parent_0 = 100, parms_DFOP_lin) d_met_2_15 <- add_err(d_synth_DFOP_lin, sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07), n = 15, reps = 100, digits = 5, LOD = 0.01, seed = 123456) # For a single fit, we get a relative error of less than 5% in the error # model components f_met_2_tc_e4 <- mkinfit(m_synth_DFOP_lin, d_met_2_15[[1]], quiet = TRUE, error_model = "tc", error_model_algorithm = "direct") parm_errors_met_2_tc_e4 <- (f_met_2_tc_e4$errparms - c(0.5, 0.07)) / c(0.5, 0.07) expect_true(all(abs(parm_errors_met_2_tc_e4) < 0.05)) # Doing more takes a lot of computing power skip_on_travis() skip_on_cran() f_met_2_15_tc_e4 <- mmkin(list(m_synth_DFOP_lin), d_met_2_15, quiet = TRUE, error_model = "tc", cores = n_cores) parms_met_2_15_tc_e4 <- apply(sapply(f_met_2_15_tc_e4, function(x) x$bparms.optim), 1, mean) parm_errors_met_2_15_tc_e4 <- (parms_met_2_15_tc_e4[names(parms_DFOP_lin_optim)] - parms_DFOP_lin_optim) / parms_DFOP_lin_optim expect_true(all(abs(parm_errors_met_2_15_tc_e4) < 0.015)) tcf_met_2_15_tc <- apply(sapply(f_met_2_15_tc_e4, function(x) x$errparms), 1, mean, na.rm = TRUE) tcf_met_2_15_tc_error_model_errors <- (tcf_met_2_15_tc - c(0.5, 0.07)) / c(0.5, 0.07) # Here we get a precision < 10% for retrieving the original error model components # from 15 datasets expect_true(all(abs(tcf_met_2_15_tc_error_model_errors) < 0.10)) }) test_that("The different error model fitting methods work for parent fits", { skip_on_cran() f_9_OLS <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data, quiet = TRUE) expect_equivalent(round(AIC(f_9_OLS), 2), 137.43) f_9_direct <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data, error_model = "tc", error_model_algorithm = "direct", quiet = TRUE) expect_equivalent(round(AIC(f_9_direct), 2), 134.94) f_9_twostep <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data, error_model = "tc", error_model_algorithm = "twostep", quiet = TRUE) expect_equivalent(round(AIC(f_9_twostep), 2), 134.94) f_9_threestep <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data, error_model = "tc", error_model_algorithm = "threestep", quiet = TRUE) expect_equivalent(round(AIC(f_9_threestep), 2), 139.43) f_9_fourstep <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data, error_model = "tc", error_model_algorithm = "fourstep", quiet = TRUE) expect_equivalent(round(AIC(f_9_fourstep), 2), 139.43) f_9_IRLS <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data, error_model = "tc", error_model_algorithm = "IRLS", quiet = TRUE) expect_equivalent(round(AIC(f_9_IRLS), 2), 139.43) f_9_d_3 <- mkinfit("SFO", experimental_data_for_UBA_2019[[9]]$data, error_model = "tc", error_model_algorithm = "d_3", quiet = TRUE) expect_equivalent(round(AIC(f_9_d_3), 2), 134.94) })