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Type 'q()' to quit R. > library(testthat) > library(BayesianDEB) BayesianDEB v0.2.1 > > test_check("BayesianDEB") -- BDEB Data Summary -- * Individuals: 1 * Growth observations: 4 * Time range: [0, 3] * Functional response (f): 1 -- BDEB Model Summary -- * Type: "individual" ('bdeb_individual_growth') * Individuals: 1 * Endpoints: growth * Observation: growth = "normal", reproduction = "negbinom" -- Priors (8 parameters) > p_Am: LogNormal(mu=1, sigma=1) > p_M: LogNormal(mu=-1, sigma=1) > kappa: Beta(a=2, b=2) > v: LogNormal(mu=-1.5, sigma=1) > E_G: LogNormal(mu=6, sigma=1) > E0: LogNormal(mu=0, sigma=1) > L0: LogNormal(mu=-2, sigma=1) > sigma_L: HalfNormal(sigma=0.1) > lognormal(mu = 1, sigma = 0.5) -- Prior: lognormal * mu = 1 * sigma = 0.5 * mean = 3.08 * sd = 1.642 * quantiles (5%, 50%, 95%) = (1.19, 2.72, 6.19) -- BDEB Data -- i Individuals: 1 i Endpoints: growth i Functional response (f): 1 > Growth: 6 observations, t = [0, 5] -- DEBtox Effect Concentrations parameter mean median sd lower upper EC50 143.0 139.1 24.23 105.9 178.2 NEC 15.1 15.3 1.77 12.3 17.6 -- DEBtox Effect Concentrations parameter mean median sd lower upper EC50 143.0 139.1 24.23 105.9 178.2 NEC 15.1 15.3 1.77 12.3 17.6 -- DEBtox Effect Concentrations parameter mean median sd lower upper EC50 140.3 137.3 25.56 102.7 172.9 NEC 14.7 14.7 1.29 12.4 16.9 -- DEBtox Effect Concentrations parameter mean median sd lower upper EC50 140.5 139 26.37 101.2 187.7 NEC 14.9 15 1.42 12.5 17.3 -- DEBtox Effect Concentrations parameter mean median sd lower upper EC50 140.5 139 26.37 122 154.9 NEC 14.9 15 1.42 14 15.9 -- DEBtox Effect Concentrations parameter mean median sd lower upper EC50 143.0 139.1 24.23 105.9 178.2 NEC 15.1 15.3 1.77 12.3 17.6 -- DEBtox Effect Concentrations parameter mean median sd lower upper EC50 143.0 139.1 24.23 105.9 178.2 NEC 15.1 15.3 1.77 12.3 17.6 -- DEBtox Effect Concentrations parameter mean median sd lower upper EC50 139.2 136.9 26.32 102.1 184.7 NEC 14.9 14.9 1.46 12.6 17.3 -- DEBtox Effect Concentrations parameter mean median sd lower upper EC50 139.2 136.9 26.32 120 156.2 NEC 14.9 14.9 1.46 14 15.9 -- DEBtox Effect Concentrations parameter mean median sd lower upper EC50 143.0 139.1 24.23 105.9 178.2 NEC 15.1 15.3 1.77 12.3 17.6 -- BDEB Diagnostics (individual) -- v No divergent transitions. v Treedepth OK. v E-BFMI OK (all > 0.3). -- Parameter Summary x R-hat > 1.01 for: L0, L_hat[2], L_hat[7], L_hat[8] ! Low bulk ESS (<400) for: p_Am, p_M, kappa, v, E_G, E0, L0, sigma_L, L_hat[1], L_hat[2], L_hat[3], L_hat[4], L_hat[5], L_hat[6], L_hat[7], L_hat[8], L_hat[9], L_hat[10] variable mean sd median 5% 95% rhat ess_bulk ess_tail p_Am 4.5201 0.46048 4.5221 3.7569 5.2130 1.000 90.3 72.3 p_M 0.4943 0.04541 0.4932 0.4276 0.5731 0.986 140.2 123.2 kappa 0.7221 0.13452 0.7359 0.4794 0.8964 0.999 117.6 110.0 v 0.2241 0.02080 0.2215 0.1943 0.2588 1.003 122.8 78.1 E_G 401.2765 40.83679 397.8263 341.1736 466.1875 1.004 95.1 104.7 E0 1.0042 0.11118 0.9949 0.8296 1.1733 1.001 100.3 79.9 L0 0.1344 0.01226 0.1348 0.1165 0.1526 1.084 22.8 73.8 sigma_L 0.0146 0.00269 0.0148 0.0101 0.0193 0.994 90.0 71.5 i 10 latent-state rows hidden; use `print(x, full = TRUE)` or `summary(x)$table` to see all. -- BDEB Diagnostics summary (individual) -- * Parameters monitored: 18 * Divergent transitions: 0 * Treedepth saturations: 0 * Chains with low E-BFMI: 0 * Parameters with R-hat > 1.01: 4 * Parameters with ESS-bulk < 400: 18 -- DEBtox Effect Concentrations parameter mean median sd lower upper EC50 137.2 137.5 27.2 98.8 186.9 NEC 14.9 14.8 1.6 12.5 17.3 -- BDEB Fit -- i Model type: individual i Algorithm: sampling (NUTS) i Chains: 2, Warmup: 500, Sampling: 25 v No divergent transitions -- BDEB Model Specification -- i Type: individual i Stan model: bdeb_individual_growth i Individuals: 1 i Endpoints: growth -- Priors > p_Am: LogNormal(1.0, 1.0) > p_M: LogNormal(-1.0, 1.0) > kappa: Beta(2.0, 2.0) > v: LogNormal(-1.5, 1.0) > E_G: LogNormal(6.0, 1.0) > E0: LogNormal(0.0, 1.0) > L0: LogNormal(-2.0, 1.0) > sigma_L: HalfNormal(0.10) -- BDEB Posterior Predictive Check -- i Type: growth > Growth: 10 observations, 50 posterior draws -- BDEB Prior Predictive Check -- i Draws: 20 i Time range: [0, 5] > Prior L_inf: median 2.85, 95% CI [0.43, 128.04] -- BDEB Prediction -- * Model type: "individual" * Posterior draws: 15 * Time points: 10 (range [0, 63]) i Use `summary()` for credible intervals or `plot()` for trajectories. time lower median upper 0.000000 0.1221512 0.1367962 0.1498994 4.166667 0.1226860 0.1369724 0.1502730 8.333333 0.1241895 0.1377318 0.1515128 12.500000 0.1262111 0.1390929 0.1532456 16.666667 0.1285358 0.1408514 0.1552843 20.833333 0.1310432 0.1427904 0.1575197 25.000000 0.1336627 0.1448520 0.1598853 29.166667 0.1363494 0.1469980 0.1623378 33.333333 0.1390677 0.1492011 0.1648467 37.500000 0.1417308 0.1514422 0.1673910 -- DEBtox Effect Concentrations parameter mean median sd lower upper EC50 140.5 139 26.37 101.2 187.7 NEC 14.9 15 1.42 12.5 17.3 [ FAIL 0 | WARN 5 | SKIP 1 | PASS 1071 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • {cmdstanr} is not installed (1): 'test-end-to-end.R:190:3' [ FAIL 0 | WARN 5 | SKIP 1 | PASS 1071 ] > > proc.time() user system elapsed 36.34 2.85 39.42