diff options
Diffstat (limited to 'tests/testthat/test_error_models.R')
| -rw-r--r-- | tests/testthat/test_error_models.R | 138 |
1 files changed, 10 insertions, 128 deletions
diff --git a/tests/testthat/test_error_models.R b/tests/testthat/test_error_models.R index fbae6286..f4015e00 100644 --- a/tests/testthat/test_error_models.R +++ b/tests/testthat/test_error_models.R @@ -35,25 +35,18 @@ DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data test_that("Error model 'const' works", { skip_on_cran() fit_const_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "const", quiet = TRUE) - bpar_1 <- summary(fit_const_1)$bpar[, c("Estimate", "Lower", "Upper")] + bpar_1 <- fit_const_1$bparms.optim # The reference used here is mkin 0.9.48.1 bpar_1_mkin_0.9 <- read.table(text = -"parent_0 102.0000 98.6000 106.0000 -k_parent 0.7390 0.6770 0.8070 -k_M1 0.2990 0.2560 0.3490 -k_M2 0.0202 0.0176 0.0233 -f_parent_to_M1 0.7690 0.6640 0.8480 -f_M1_to_M2 0.7230 0.6030 0.8180", -col.names = c("parameter", "estimate", "lower", "upper")) - - expect_equivalent(signif(bpar_1[1:6, "Estimate"], 3), bpar_1_mkin_0.9$estimate) - # Relative difference of lower bound of confidence is < 0.02 - rel_diff <- function(v1, v2) { - (v1 - v2)/v2 - } - expect_equivalent(rel_diff(bpar_1[1:6, "Lower"], - bpar_1_mkin_0.9$lower), - rep(0, 6), tolerance = 0.02) +"parent_0 102.0000 +k_parent 0.7390 +k_M1 0.2990 +k_M2 0.0202 +f_parent_to_M1 0.7690 +f_M1_to_M2 0.7230", +col.names = c("parameter", "estimate")) + + expect_equivalent(signif(bpar_1, 3), bpar_1_mkin_0.9$estimate) }) test_that("Error model 'obs' works", { @@ -70,117 +63,6 @@ test_that("Error model 'tc' works", { expect_equivalent(parms_3, c(102.1, 0.7393, 0.2992, 0.0202, 0.7687, 0.7229)) }) -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) - - # 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 - - # 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$errparms - c(0.5, 0.07)) / - c(0.5, 0.07) - # When maximising the likelihood directly (not using IRLS), we get - # a precision of < 2% for the error model componentes as well - expect_true(all(abs(tcf_100_1_error_model_errors) < 0.02)) - - # Parent and two metabolites - m_synth_DFOP_lin <- mkinmod(parent = list(type = "DFOP", to = "M1"), - M1 = list(type = "SFO", to = "M2"), - M2 = list(type = "SFO"), use_of_ff = "max", - quiet = TRUE) - sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) - parms_DFOP_lin <- c(k1 = 0.2, k2 = 0.02, g = 0.5, - f_parent_to_M1 = 0.5, k_M1 = 0.3, - f_M1_to_M2 = 0.7, k_M2 = 0.02) - d_synth_DFOP_lin <- mkinpredict(m_synth_DFOP_lin, - parms_DFOP_lin, - c(parent = 100, M1 = 0, M2 = 0), - sampling_times) - parms_DFOP_lin_optim = c(parent_0 = 100, parms_DFOP_lin) - - d_met_2_15 <- add_err(d_synth_DFOP_lin, - sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07), - n = 15, reps = 100, digits = 5, LOD = 0.01, seed = 123456) - - # For a single fit, we get a relative error of less than 10% in the error - # model components - f_met_2_tc_e4 <- mkinfit(m_synth_DFOP_lin, d_met_2_15[[1]], quiet = TRUE, - error_model = "tc", error_model_algorithm = "direct") - parm_errors_met_2_tc_e4 <- (f_met_2_tc_e4$errparms - c(0.5, 0.07)) / c(0.5, 0.07) - expect_true(all(abs(parm_errors_met_2_tc_e4) < 0.1)) - - # 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() |