diff options
Diffstat (limited to 'tests/testthat/test_irls.R')
| -rw-r--r-- | tests/testthat/test_irls.R | 92 |
1 files changed, 59 insertions, 33 deletions
diff --git a/tests/testthat/test_irls.R b/tests/testthat/test_irls.R index 65541fb5..5e09912f 100644 --- a/tests/testthat/test_irls.R +++ b/tests/testthat/test_irls.R @@ -42,62 +42,88 @@ test_that("Reweighting method 'obs' works", { test_that("Reweighting method 'tc' works", { skip_on_cran() - skip("IRLS reweighting with method 'tc' is currently under construction") + # 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, - c(k1 = 0.2, k2 = 0.02, g = 0.5), - c(parent = 100), + parms_DFOP, c(parent = 100), sampling_times) - d_100 <- add_err(d_DFOP, + d_2_100 <- add_err(d_DFOP, sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07), - n = 1, reps = 100, digits = 5, LOD = -Inf) - d_1000 <- add_err(d_DFOP, + n = 100, reps = 2, digits = 5, LOD = -Inf) + d_100_1 <- add_err(d_DFOP, sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07), - n = 1, reps = 1000, digits = 5, LOD = -Inf) + n = 1, reps = 100, digits = 5, LOD = -Inf) + + f_2_100 <- mmkin("DFOP", d_2_100, quiet = TRUE, + cores = if (Sys.getenv("TRAVIS") != "") 1 else 15) + parms_2_100 <- apply(sapply(f_2_100, function(x) x$bparms.optim), 1, mean) + parm_errors_2_100 <- (parms_2_100 - parms_DFOP_optim) / parms_DFOP_optim + expect_true(all(abs(parm_errors_2_100) < 0.2)) + + f_2_100_tc <- mmkin("DFOP", d_2_100, reweight.method = "tc", quiet = TRUE, + cores = if (Sys.getenv("TRAVIS") != "") 1 else 15) + parms_2_100_tc <- apply(sapply(f_2_100_tc, function(x) x$bparms.optim), 1, mean) + parm_errors_2_100_tc <- (parms_2_100_tc - parms_DFOP_optim) / parms_DFOP_optim + expect_true(all(abs(parm_errors_2_100_tc) < 0.1)) + + tcf_2_100_tc <- apply(sapply(f_2_100_tc, function(x) x$tc_fitted), 1, mean, na.rm = TRUE) - f_100 <- mkinfit(DFOP, d_100[[1]]) - f_100$bparms.optim - f_tc_100 <- mkinfit(DFOP, d_100[[1]], reweight.method = "tc") - f_tc_100$bparms.optim - f_tc_100$tc_fitted + tcf_2_100_error_model_errors <- (tcf_2_100_tc - c(0.5, 0.07)) / c(0.5, 0.07) + expect_true(all(abs(tcf_2_100_error_model_errors) < 0.2)) - f_tc_1000 <- mkinfit(DFOP, d_1000[[1]], reweight.method = "tc") - f_tc_1000$bparms.optim - f_tc_1000$tc_fitted + f_tc_100_1 <- suppressWarnings(mkinfit(DFOP, d_100_1[[1]], reweight.method = "tc", quiet = TRUE)) + parm_errors_100_1 <- (f_tc_100_1$bparms.optim - parms_DFOP_optim) / parms_DFOP_optim + expect_true(all(abs(parm_errors_100_1) < 0.05)) + tcf_100_1_error_model_errors <- (f_tc_100_1$tc_fitted - c(0.5, 0.07)) / + c(0.5, 0.07) + # Even with 100 (or even 1000, not shown) replicates at each observation time + # we only get a precision of 20% for the error model components + expect_true(all(abs(tcf_100_1_error_model_errors) < 0.2)) + + # 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) - d_synth_DFOP_lin <- mkinpredict(m_synth_DFOP_lin, - c(k1 = 0.2, k2 = 0.02, g = 0.5, + 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), + 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_100 <- add_err(d_synth_DFOP_lin, - sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07), - n = 1, reps = 100, digits = 5, LOD = -Inf) - d_met_1000 <- add_err(d_synth_DFOP_lin, + d_met_2_15 <- add_err(d_synth_DFOP_lin, sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07), - n = 1, reps = 1000, digits = 5, LOD = -Inf) + n = 15, reps = 1000, digits = 5, LOD = -Inf) - f_met_100 <- mkinfit(m_synth_DFOP_lin, d_met_100[[1]]) - summary(f_met_100)$bpar + time_met_2_15_tc_15 <- system.time( + f_met_2_15_tc_e4 <- mmkin(list(m_synth_DFOP_lin), d_met_2_15, quiet = TRUE, + reweight.method = "tc", reweight.tol = 1e-4, + cores = if (Sys.getenv("TRAVIS") != "") 1 else 15) + ) - f_met_100 <- mkinfit(m_synth_DFOP_lin, d_met_100[[1]], reweight.method = "tc") - summary(f.100)$bpar + parms_met_2_15_tc_e4 <- apply(sapply(f_met_2_15_tc_e4, function(x) x$bparms.optim), 1, mean) + parm_errors_met_2_15_tc_e4 <- (parms_met_2_15_tc_e4[names(parms_DFOP_lin_optim)] - + parms_DFOP_lin_optim) / parms_DFOP_lin_optim + expect_true(all(abs(parm_errors_met_2_15_tc_e4) < 0.01)) + tcf_met_2_15_tc <- apply(sapply(f_met_2_15_tc_e4, function(x) x$tc_fitted), 1, mean, na.rm = TRUE) - fit_irls_2 <- mkinfit(m_synth_DFOP_par, DFOP_par_c, reweight.method = "tc", quiet = TRUE) - parms_2 <- signif(fit_irls_2$bparms.optim, 3) - expect_equivalent(parms_2, c(99.3, 0.041, 0.00962, 0.597, 0.393, 0.298, 0.0203, 0.707)) + tcf_met_2_15_tc_error_model_errors <- (tcf_met_2_15_tc - c(0.5, 0.07)) / + c(0.5, 0.07) - fit_irls_3 <- mkinfit("DFOP", FOCUS_2006_C, reweight.method = "tc", quiet = TRUE) - parms_3 <- signif(fit_irls_3$bparms.optim, 3) - expect_equivalent(parms_3, c(85.0, 0.46, 0.0178, 0.854)) + # Here we only get a precision < 30% for retrieving the original error model components + # from 15 datasets + expect_true(all(abs(tcf_met_2_15_tc_error_model_errors) < 0.3)) }) |