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-rw-r--r--test.log112
1 files changed, 19 insertions, 93 deletions
diff --git a/test.log b/test.log
index 7754d6ad..5a71534f 100644
--- a/test.log
+++ b/test.log
@@ -1,102 +1,28 @@
Loading mkin
-Loading required package: testthat
Testing mkin
✔ | OK F W S | Context
- ⠏ | 0 | Calculation of FOCUS chi2 error levels ⠋ | 1 | Calculation of FOCUS chi2 error levels ⠙ | 2 | Calculation of FOCUS chi2 error levels ✔ | 2 | Calculation of FOCUS chi2 error levels [2.2 s]
- ⠏ | 0 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠋ | 1 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠙ | 2 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠹ | 3 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠸ | 4 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠼ | 5 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠴ | 6 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠦ | 7 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠧ | 8 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ✔ | 8 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [6.5 s]
- ⠏ | 0 | Iteratively reweighted least squares (IRLS) fitting ⠋ | 0 1 | Iteratively reweighted least squares (IRLS) fitting ⠙ | 1 1 | Iteratively reweighted least squares (IRLS) fitting ⠹ | 1 1 1 | Iteratively reweighted least squares (IRLS) fitting ⠹ | 1 1 1 | Iteratively reweighted least squares (IRLS) fitting ⠹ | 1 1 1 | Iteratively reweighted least squares (IRLS) fitting ⠸ | 1 1 2 | Iteratively reweighted least squares (IRLS) fitting ⠹ | 1 1 1 | Iteratively reweighted least squares (IRLS) fitting ⠹ | 1 1 1 | Iteratively reweighted least squares (IRLS) fitting ⠸ | 1 1 2 | Iteratively reweighted least squares (IRLS) fitting ⠹ | 1 1 1 | Iteratively reweighted least squares (IRLS) fitting ⠼ | 1 1 3 | Iteratively reweighted least squares (IRLS) fitting ⠴ | 1 1 4 | Iteratively reweighted least squares (IRLS) fitting ⠹ | 1 1 1 | Iteratively reweighted least squares (IRLS) fitting ⠹ | 2 1 | Iteratively reweighted least squares (IRLS) fitting ⠸ | 3 1 | Iteratively reweighted least squares (IRLS) fitting ⠼ | 4 1 | Iteratively reweighted least squares (IRLS) fitting ⠴ | 5 1 | Iteratively reweighted least squares (IRLS) fitting ⠦ | 6 1 | Iteratively reweighted least squares (IRLS) fitting ⠧ | 7 1 | Iteratively reweighted least squares (IRLS) fitting ✖ | 7 1 | Iteratively reweighted least squares (IRLS) fitting [172.0 s]
+ ⠏ | 0 | Calculation of FOCUS chi2 error levels ⠋ | 1 | Calculation of FOCUS chi2 error levels ⠙ | 2 | Calculation of FOCUS chi2 error levels ✔ | 2 | Calculation of FOCUS chi2 error levels [2.5 s]
+ ⠏ | 0 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠋ | 1 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠙ | 2 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠹ | 3 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠸ | 4 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠼ | 5 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠴ | 6 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠦ | 7 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ⠧ | 8 | Results for FOCUS D established in expertise for UBA (Ranke 2014) ✔ | 8 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [7.2 s]
+ ⠏ | 0 | Iteratively reweighted least squares (IRLS) fitting ⠋ | 1 | Iteratively reweighted least squares (IRLS) fitting ⠙ | 1 1 | Iteratively reweighted least squares (IRLS) fitting ✔ | 1 1 | Iteratively reweighted least squares (IRLS) fitting [9.0 s]
────────────────────────────────────────────────────────────────────────────────
-test_irls.R:38: error: Reweighting method 'obs' works
-Objekt 'tc_fit' nicht gefunden
-1: mkinfit(m_synth_SFO_lin, SFO_lin_a, reweight.method = "obs", quiet = TRUE) at /home/jranke/git/mkin/tests/testthat/test_irls.R:38
-2: system.time({
- fit <- modFit(cost, c(state.ini.optim, transparms.optim), method = method.modFit,
- control = control.modFit, lower = lower, upper = upper, ...)
- if (!is.null(reweight.method)) {
- if (!reweight.method %in% c("obs", "tc"))
- stop("Only reweighting methods 'obs' and 'tc' are implemented")
- if (reweight.method == "obs") {
- if (!quiet) {
- cat("IRLS based on variance estimates for each observed variable\n")
- cat("Initial variance estimates are:\n")
- print(signif(fit$var_ms_unweighted, 8))
- }
- }
- if (reweight.method == "tc") {
- tc_fit <- fit_error_model_mad_obs(cost(fit$par)$residuals, tc, 0)
- if (is.character(tc_fit)) {
- if (!quiet) {
- cat(tc_fit, ".\n", "No reweighting will be performed.")
- }
- tc_fitted <- c(sigma_low = NA, rsd_high = NA)
- }
- else {
- tc_fitted <- coef(tc_fit)
- if (!quiet) {
- cat("IRLS based on variance estimates according to the two component error model\n")
- cat("Initial variance components are:\n")
- print(signif(tc_fitted))
- }
- }
- }
- reweight.diff = 1
- n.iter <- 0
- if (!is.null(err))
- observed$err.ini <- observed[[err]]
- err = "err.irls"
- while (reweight.diff > reweight.tol & n.iter < reweight.max.iter & !is.character(tc_fit)) {
- n.iter <- n.iter + 1
- if (reweight.method == "obs") {
- sr_old <- fit$var_ms_unweighted
- observed[err] <- sqrt(fit$var_ms_unweighted[as.character(observed$name)])
- }
- if (reweight.method == "tc") {
- sr_old <- tc_fitted
- tmp_predicted <- mkin_wide_to_long(out_predicted, time = "time")
- tmp_data <- suppressMessages(join(observed, tmp_predicted, by = c("time",
- "name")))
- observed[err] <- predict(tc_fit, newdata = data.frame(obs = observed$value))
- }
- fit <- modFit(cost, fit$par, method = method.modFit, control = control.modFit,
- lower = lower, upper = upper, ...)
- if (reweight.method == "obs") {
- sr_new <- fit$var_ms_unweighted
- }
- if (reweight.method == "tc") {
- tc_fit <- fit_error_model_mad_obs(cost(fit$par)$residuals, tc_fitted,
- n.iter)
- if (is.character(tc_fit)) {
- if (!quiet) {
- cat(tc_fit, ".\n")
- }
- break
- }
- else {
- tc_fitted <- coef(tc_fit)
- sr_new <- tc_fitted
- }
- }
- reweight.diff = sum((sr_new - sr_old)^2)
- if (!quiet) {
- cat("Iteration", n.iter, "yields variance estimates:\n")
- print(signif(sr_new, 8))
- cat("Sum of squared differences to last variance (component) estimates:",
- signif(reweight.diff, 2), "\n")
- }
- }
- }
- }) at /home/jranke/git/mkin/R/mkinfit.R:396
+test_irls.R:44: skip: Reweighting method 'tc' works
+Too much trouble with datasets that are randomly generated
────────────────────────────────────────────────────────────────────────────────
- ⠏ | 0 | Model predictions with mkinpredict ⠋ | 1 | Model predictions with mkinpredict ⠙ | 2 | Model predictions with mkinpredict ⠹ | 3 | Model predictions with mkinpredict ✔ | 3 | Model predictions with mkinpredict [0.4 s]
- ⠏ | 0 | Fitting of parent only models ⠋ | 1 | Fitting of parent only models ⠙ | 2 | Fitting of parent only models ⠹ | 3 | Fitting of parent only models ⠸ | 4 | Fitting of parent only models ⠼ | 5 | Fitting of parent only models ⠴ | 6 | Fitting of parent only models ⠦ | 7 | Fitting of parent only models ⠧ | 8 | Fitting of parent only models ⠇ | 9 | Fitting of parent only models ⠏ | 10 | Fitting of parent only models ⠋ | 11 | Fitting of parent only models ⠙ | 12 | Fitting of parent only models ⠹ | 13 | Fitting of parent only models ⠸ | 14 | Fitting of parent only models ⠼ | 15 | Fitting of parent only models ⠴ | 16 | Fitting of parent only models ⠦ | 17 | Fitting of parent only models ⠧ | 18 | Fitting of parent only models ⠇ | 19 | Fitting of parent only models ⠏ | 20 | Fitting of parent only models ⠋ | 21 | Fitting of parent only models ✔ | 21 | Fitting of parent only models [22.1 s]
- ⠏ | 0 | Complex test case from Schaefer et al. (2007) Piacenza paper ⠋ | 1 | Complex test case from Schaefer et al. (2007) Piacenza paper ⠙ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper ✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [5.2 s]
- ⠏ | 0 | Results for synthetic data established in expertise for UBA (Ranke 2014) ⠋ | 1 | Results for synthetic data established in expertise for UBA (Ranke 2014) ⠙ | 2 | Results for synthetic data established in expertise for UBA (Ranke 2014) ⠹ | 3 | Results for synthetic data established in expertise for UBA (Ranke 2014) ⠸ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) ✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [6.5 s]
- ⠏ | 0 | Calculation of maximum time weighted average concentrations (TWAs) ⠋ | 1 | Calculation of maximum time weighted average concentrations (TWAs) ⠙ | 2 | Calculation of maximum time weighted average concentrations (TWAs) ⠹ | 3 | Calculation of maximum time weighted average concentrations (TWAs) ⠸ | 4 | Calculation of maximum time weighted average concentrations (TWAs) ⠼ | 5 | Calculation of maximum time weighted average concentrations (TWAs) ⠴ | 6 | Calculation of maximum time weighted average concentrations (TWAs) ⠦ | 7 | Calculation of maximum time weighted average concentrations (TWAs) ⠧ | 8 | Calculation of maximum time weighted average concentrations (TWAs) ✔ | 8 | Calculation of maximum time weighted average concentrations (TWAs) [7.6 s]
+ ⠏ | 0 | Fitting the logistic model ⠋ | 1 | Fitting the logistic model ⠙ | 1 1 | Fitting the logistic model ✔ | 1 1 | Fitting the logistic model [0.5 s]
+────────────────────────────────────────────────────────────────────────────────
+test_logistic.R:42: skip: The logistic fit can be done via differential equation
+Skip slow fit of logistic model using deSolve without compilation
+────────────────────────────────────────────────────────────────────────────────
+ ⠏ | 0 | Model predictions with mkinpredict ⠋ | 1 | Model predictions with mkinpredict ⠙ | 2 | Model predictions with mkinpredict ⠹ | 3 | Model predictions with mkinpredict ✔ | 3 | Model predictions with mkinpredict [0.3 s]
+ ⠏ | 0 | Fitting of parent only models ⠋ | 1 | Fitting of parent only models ⠙ | 2 | Fitting of parent only models ⠹ | 3 | Fitting of parent only models ⠸ | 4 | Fitting of parent only models ⠼ | 5 | Fitting of parent only models ⠴ | 6 | Fitting of parent only models ⠦ | 7 | Fitting of parent only models ⠧ | 8 | Fitting of parent only models ⠇ | 9 | Fitting of parent only models ⠏ | 10 | Fitting of parent only models ⠋ | 11 | Fitting of parent only models ⠙ | 12 | Fitting of parent only models ⠹ | 13 | Fitting of parent only models ⠸ | 14 | Fitting of parent only models ⠼ | 15 | Fitting of parent only models ⠴ | 16 | Fitting of parent only models ⠦ | 17 | Fitting of parent only models ⠧ | 18 | Fitting of parent only models ⠇ | 19 | Fitting of parent only models ⠏ | 20 | Fitting of parent only models ⠋ | 21 | Fitting of parent only models ✔ | 21 | Fitting of parent only models [23.9 s]
+ ⠏ | 0 | Complex test case from Schaefer et al. (2007) Piacenza paper ⠋ | 1 | Complex test case from Schaefer et al. (2007) Piacenza paper ⠙ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper ✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [6.0 s]
+ ⠏ | 0 | Results for synthetic data established in expertise for UBA (Ranke 2014) ⠋ | 1 | Results for synthetic data established in expertise for UBA (Ranke 2014) ⠙ | 2 | Results for synthetic data established in expertise for UBA (Ranke 2014) ⠹ | 3 | Results for synthetic data established in expertise for UBA (Ranke 2014) ⠸ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) ✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [7.5 s]
+ ⠏ | 0 | Calculation of maximum time weighted average concentrations (TWAs) ⠋ | 1 | Calculation of maximum time weighted average concentrations (TWAs) ⠙ | 2 | Calculation of maximum time weighted average concentrations (TWAs) ⠹ | 3 | Calculation of maximum time weighted average concentrations (TWAs) ⠸ | 4 | Calculation of maximum time weighted average concentrations (TWAs) ⠼ | 5 | Calculation of maximum time weighted average concentrations (TWAs) ⠴ | 6 | Calculation of maximum time weighted average concentrations (TWAs) ⠦ | 7 | Calculation of maximum time weighted average concentrations (TWAs) ⠧ | 8 | Calculation of maximum time weighted average concentrations (TWAs) ✔ | 8 | Calculation of maximum time weighted average concentrations (TWAs) [8.4 s]
══ Results ═════════════════════════════════════════════════════════════════════
-Duration: 222.6 s
+Duration: 65.5 s
-OK: 55
-Failed: 1
+OK: 50
+Failed: 0
Warnings: 0
-Skipped: 0
+Skipped: 2

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