From 3eefecf0adfbb30b8fb895c244dea6903bcb3e9c Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 31 Jan 2019 16:55:20 +0100 Subject: Restore NAMESPACE which was accidentally overwritten by pkgdown -> roxygen --- docs/reference/AIC.mmkin.html | 16 +- docs/reference/DFOP.solution.html | 2 +- docs/reference/Extract.mmkin.html | 402 +++++++++++- docs/reference/FOMC.solution.html | 2 +- docs/reference/HS.solution.html | 2 +- docs/reference/IORE.solution.html | 15 +- docs/reference/SFO.solution.html | 2 +- docs/reference/SFORB.solution.html | 2 +- docs/reference/add_err.html | 20 +- docs/reference/endpoints.html | 12 +- docs/reference/geometric_mean.html | 2 +- docs/reference/ilr.html | 15 +- docs/reference/logLik.mkinfit.html | 14 +- docs/reference/max_twa_parent.html | 4 +- docs/reference/mccall81_245T.html | 183 +++++- docs/reference/mkin_long_to_wide.html | 24 +- docs/reference/mkin_wide_to_long.html | 8 +- docs/reference/mkinerrmin.html | 12 +- docs/reference/mkinfit.html | 906 +++++++++++++++++++++++++++- docs/reference/mkinmod.html | 35 +- docs/reference/mkinparplot.html | 3 +- docs/reference/mkinpredict.html | 165 ++++- docs/reference/mkinresplot.html | 3 +- docs/reference/mkinsub.html | 6 +- docs/reference/mmkin.html | 36 +- docs/reference/plot.mkinfit.html | 9 +- docs/reference/plot.mmkin.html | 5 +- docs/reference/print.mkinmod.html | 18 +- docs/reference/schaefer07_complex_case.html | 20 +- docs/reference/summary.mkinfit.html | 71 ++- docs/reference/test_data_from_UBA_2014.html | 48 +- docs/reference/transform_odeparms.html | 316 +++++++++- 32 files changed, 2260 insertions(+), 118 deletions(-) (limited to 'docs/reference') diff --git a/docs/reference/AIC.mmkin.html b/docs/reference/AIC.mmkin.html index a90e81bb..d90b325f 100644 --- a/docs/reference/AIC.mmkin.html +++ b/docs/reference/AIC.mmkin.html @@ -160,12 +160,22 @@

Examples

  f <- mmkin(c("SFO", "FOMC", "DFOP"),
     list("FOCUS A" = FOCUS_2006_A,
-         "FOCUS C" = FOCUS_2006_C))
#> Error in mmkin(c("SFO", "FOMC", "DFOP"), list(`FOCUS A` = FOCUS_2006_A,     `FOCUS C` = FOCUS_2006_C)): konnte Funktion "mmkin" nicht finden
  AIC(f[1, "FOCUS A"]) # We get a single number for a single fit
#> Error in AIC(f[1, "FOCUS A"]): Objekt 'f' nicht gefunden

+         "FOCUS C" = FOCUS_2006_C))
+  AIC(f[1, "FOCUS A"]) # We get a single number for a single fit
#> [1] 55.32452

   # For FOCUS A, the models fit almost equally well, so the higher the number
   # of parameters, the higher (worse) the AIC
-  AIC(f[, "FOCUS A"])
#> Error in AIC(f[, "FOCUS A"]): Objekt 'f' nicht gefunden
  AIC(f[, "FOCUS A"], k = 0) # If we do not penalize additional parameters, we get nearly the same
#> Error in AIC(f[, "FOCUS A"], k = 0): Objekt 'f' nicht gefunden

+  AIC(f[, "FOCUS A"])
#>      df      AIC
+#> SFO   3 55.32452
+#> FOMC  4 57.32477
+#> DFOP  5 59.32452
  AIC(f[, "FOCUS A"], k = 0) # If we do not penalize additional parameters, we get nearly the same
#>      df      AIC
+#> SFO   3 49.32452
+#> FOMC  4 49.32477
+#> DFOP  5 49.32452

   # For FOCUS C, the more complex models fit better
-  AIC(f[, "FOCUS C"])
#> Error in AIC(f[, "FOCUS C"]): Objekt 'f' nicht gefunden
+ AIC(f[, "FOCUS C"])
#> df AIC +#> SFO 3 59.84675 +#> FOMC 4 44.70584 +#> DFOP 5 29.08369
#> [[1]] +#> $par +#> parent_0 log_k_parent_sink +#> 99.174072 -2.549028 +#> +#> $ssr +#> [1] 30.65564 +#> +#> $convergence +#> [1] 0 +#> +#> $iterations +#> [1] 5 +#> +#> $evaluations +#> function gradient +#> 8 15 +#> +#> $counts +#> [1] "relative convergence (4)" +#> +#> $hessian +#> parent_0 log_k_parent_sink +#> parent_0 4.163631 -94.09343 +#> log_k_parent_sink -94.093431 6311.34610 +#> +#> $residuals +#> parent parent parent parent parent parent +#> 0.55407218 -2.98452128 4.20445742 -1.68599939 -0.58185357 -0.72033730 +#> parent parent +#> -0.24260405 -0.07020339 +#> +#> $ms +#> [1] 3.831956 +#> +#> $var_ms +#> parent +#> 3.831956 +#> +#> $var_ms_unscaled +#> parent +#> 3.831956 +#> +#> $var_ms_unweighted +#> parent +#> 3.831956 +#> +#> $rank +#> [1] 2 +#> +#> $df.residual +#> [1] 6 +#> +#> $solution_type +#> [1] "analytical" +#> +#> $transform_rates +#> [1] TRUE +#> +#> $transform_fractions +#> [1] TRUE +#> +#> $method.modFit +#> [1] "Port" +#> +#> $maxit.modFit +#> [1] "auto" +#> +#> $calls +#> [1] 29 +#> +#> $time +#> User System verstrichen +#> 0.197 0.000 0.198 +#> +#> $mkinmod +#> <mkinmod> model generated with +#> Use of formation fractions $use_of_ff: min +#> Specification $spec: +#> $parent +#> $type: SFO; $sink: TRUE +#> Coefficient matrix $coefmat available +#> Differential equations: +#> d_parent/dt = - k_parent_sink * parent +#> +#> $observed +#> name time value +#> 1 parent 0 98.62 +#> 2 parent 3 81.43 +#> 3 parent 7 53.18 +#> 4 parent 14 34.89 +#> 5 parent 30 10.09 +#> 6 parent 62 1.50 +#> 7 parent 90 0.33 +#> 8 parent 118 0.08 +#> +#> $obs_vars +#> [1] "parent" +#> +#> $predicted +#> name time value +#> 1 parent 0.000000 99.17407218 +#> 2 parent 1.191919 90.35253561 +#> 3 parent 2.383838 82.31567498 +#> 4 parent 3.000000 78.44547872 +#> 5 parent 3.575758 74.99369333 +#> 6 parent 4.767677 68.32300215 +#> 7 parent 5.959596 62.24566915 +#> 8 parent 7.000000 57.38445742 +#> 9 parent 7.151515 56.70891509 +#> 10 parent 8.343434 51.66465547 +#> 11 parent 9.535354 47.06908288 +#> 12 parent 10.727273 42.88228661 +#> 13 parent 11.919192 39.06790599 +#> 14 parent 13.111111 35.59281463 +#> 15 parent 14.000000 33.20400061 +#> 16 parent 14.303030 32.42683275 +#> 17 parent 15.494949 29.54246504 +#> 18 parent 16.686869 26.91466193 +#> 19 parent 17.878788 24.52060198 +#> 20 parent 19.070707 22.33949373 +#> 21 parent 20.262626 20.35239512 +#> 22 parent 21.454545 18.54204899 +#> 23 parent 22.646465 16.89273320 +#> 24 parent 23.838384 15.39012410 +#> 25 parent 25.030303 14.02117212 +#> 26 parent 26.222222 12.77398846 +#> 27 parent 27.414141 11.63774182 +#> 28 parent 28.606061 10.60256435 +#> 29 parent 29.797980 9.65946594 +#> 30 parent 30.000000 9.50814643 +#> 31 parent 30.989899 8.80025617 +#> 32 parent 32.181818 8.01747313 +#> 33 parent 33.373737 7.30431867 +#> 34 parent 34.565657 6.65459931 +#> 35 parent 35.757576 6.06267251 +#> 36 parent 36.949495 5.52339762 +#> 37 parent 38.141414 5.03209124 +#> 38 parent 39.333333 4.58448658 +#> 39 parent 40.525253 4.17669637 +#> 40 parent 41.717172 3.80517911 +#> 41 parent 42.909091 3.46670832 +#> 42 parent 44.101010 3.15834451 +#> 43 parent 45.292929 2.87740968 +#> 44 parent 46.484848 2.62146400 +#> 45 parent 47.676768 2.38828471 +#> 46 parent 48.868687 2.17584671 +#> 47 parent 50.060606 1.98230508 +#> 48 parent 51.252525 1.80597899 +#> 49 parent 52.444444 1.64533711 +#> 50 parent 53.636364 1.49898432 +#> 51 parent 54.828283 1.36564963 +#> 52 parent 56.020202 1.24417505 +#> 53 parent 57.212121 1.13350565 +#> 54 parent 58.404040 1.03268029 +#> 55 parent 59.595960 0.94082335 +#> 56 parent 60.787879 0.85713708 +#> 57 parent 61.979798 0.78089471 +#> 58 parent 62.000000 0.77966270 +#> 59 parent 63.171717 0.71143411 +#> 60 parent 64.363636 0.64815202 +#> 61 parent 65.555556 0.59049888 +#> 62 parent 66.747475 0.53797399 +#> 63 parent 67.939394 0.49012119 +#> 64 parent 69.131313 0.44652489 +#> 65 parent 70.323232 0.40680649 +#> 66 parent 71.515152 0.37062104 +#> 67 parent 72.707071 0.33765429 +#> 68 parent 73.898990 0.30761993 +#> 69 parent 75.090909 0.28025713 +#> 70 parent 76.282828 0.25532825 +#> 71 parent 77.474747 0.23261679 +#> 72 parent 78.666667 0.21192552 +#> 73 parent 79.858586 0.19307474 +#> 74 parent 81.050505 0.17590074 +#> 75 parent 82.242424 0.16025436 +#> 76 parent 83.434343 0.14599973 +#> 77 parent 84.626263 0.13301305 +#> 78 parent 85.818182 0.12118154 +#> 79 parent 87.010101 0.11040244 +#> 80 parent 88.202020 0.10058214 +#> 81 parent 89.393939 0.09163535 +#> 82 parent 90.000000 0.08739595 +#> 83 parent 90.585859 0.08348439 +#> 84 parent 91.777778 0.07605845 +#> 85 parent 92.969697 0.06929305 +#> 86 parent 94.161616 0.06312943 +#> 87 parent 95.353535 0.05751406 +#> 88 parent 96.545455 0.05239819 +#> 89 parent 97.737374 0.04773737 +#> 90 parent 98.929293 0.04349113 +#> 91 parent 100.121212 0.03962259 +#> 92 parent 101.313131 0.03609816 +#> 93 parent 102.505051 0.03288723 +#> 94 parent 103.696970 0.02996191 +#> 95 parent 104.888889 0.02729679 +#> 96 parent 106.080808 0.02486874 +#> 97 parent 107.272727 0.02265667 +#> 98 parent 108.464646 0.02064136 +#> 99 parent 109.656566 0.01880531 +#> 100 parent 110.848485 0.01713257 +#> 101 parent 112.040404 0.01560863 +#> 102 parent 113.232323 0.01422024 +#> 103 parent 114.424242 0.01295535 +#> 104 parent 115.616162 0.01180297 +#> 105 parent 116.808081 0.01075310 +#> 106 parent 118.000000 0.00979661 +#> +#> $cost +#> function (P) +#> { +#> assign("calls", calls + 1, inherits = TRUE) +#> if (trace_parms) +#> cat(P, "\n") +#> if (length(state.ini.optim) > 0) { +#> odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed) +#> names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames) +#> } +#> else { +#> odeini <- state.ini.fixed +#> names(odeini) <- state.ini.fixed.boxnames +#> } +#> odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], +#> transparms.fixed) +#> parms <- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates, +#> transform_fractions = transform_fractions) +#> out <- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, +#> use_compiled = use_compiled, method.ode = method.ode, +#> atol = atol, rtol = rtol, ...) +#> assign("out_predicted", out, inherits = TRUE) +#> mC <- modCost(out, observed, y = "value", err = err, weight = weight, +#> scaleVar = scaleVar) +#> if (mC$model < cost.old) { +#> if (!quiet) +#> cat("Model cost at call ", calls, ": ", mC$model, +#> "\n") +#> if (plot) { +#> outtimes_plot = seq(min(observed$time), max(observed$time), +#> length.out = 100) +#> out_plot <- mkinpredict(mkinmod, parms, odeini, outtimes_plot, +#> solution_type = solution_type, use_compiled = use_compiled, +#> method.ode = method.ode, atol = atol, rtol = rtol, +#> ...) +#> plot(0, type = "n", xlim = range(observed$time), +#> ylim = c(0, max(observed$value, na.rm = TRUE)), +#> xlab = "Time", ylab = "Observed") +#> col_obs <- pch_obs <- 1:length(obs_vars) +#> lty_obs <- rep(1, length(obs_vars)) +#> names(col_obs) <- names(pch_obs) <- names(lty_obs) <- obs_vars +#> for (obs_var in obs_vars) { +#> points(subset(observed, name == obs_var, c(time, +#> value)), pch = pch_obs[obs_var], col = col_obs[obs_var]) +#> } +#> matlines(out_plot$time, out_plot[-1], col = col_obs, +#> lty = lty_obs) +#> legend("topright", inset = c(0.05, 0.05), legend = obs_vars, +#> col = col_obs, pch = pch_obs, lty = 1:length(pch_obs)) +#> } +#> assign("cost.old", mC$model, inherits = TRUE) +#> } +#> return(mC) +#> } +#> <bytecode: 0x55555916c520> +#> <environment: 0x55555b68a808> +#> +#> $cost_notrans +#> function (P) +#> { +#> if (length(state.ini.optim) > 0) { +#> odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed) +#> names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames) +#> } +#> else { +#> odeini <- state.ini.fixed +#> names(odeini) <- state.ini.fixed.boxnames +#> } +#> odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], +#> parms.fixed) +#> out <- mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = solution_type, +#> use_compiled = use_compiled, method.ode = method.ode, +#> atol = atol, rtol = rtol, ...) +#> mC <- modCost(out, observed, y = "value", err = err, weight = weight, +#> scaleVar = scaleVar) +#> return(mC) +#> } +#> <bytecode: 0x55555a1242b0> +#> <environment: 0x55555b68a808> +#> +#> $hessian_notrans +#> parent_0 k_parent_sink +#> parent_0 4.163631 -1203.894 +#> k_parent_sink -1203.893702 1033188.753 +#> +#> $start +#> value type +#> parent_0 98.62 state +#> k_parent_sink 0.10 deparm +#> +#> $start_transformed +#> value lower upper +#> parent_0 98.620000 -Inf Inf +#> log_k_parent_sink -2.302585 -Inf Inf +#> +#> $fixed +#> [1] value type +#> <0 Zeilen> (oder row.names mit Länge 0) +#> +#> $data +#> time variable observed predicted residual +#> 1 0 parent 98.62 99.17407218 -0.55407218 +#> 2 3 parent 81.43 78.44547872 2.98452128 +#> 3 7 parent 53.18 57.38445742 -4.20445742 +#> 4 14 parent 34.89 33.20400061 1.68599939 +#> 5 30 parent 10.09 9.50814643 0.58185357 +#> 6 62 parent 1.50 0.77966270 0.72033730 +#> 7 90 parent 0.33 0.08739595 0.24260405 +#> 8 118 parent 0.08 0.00979661 0.07020339 +#> +#> $atol +#> [1] 1e-08 +#> +#> $rtol +#> [1] 1e-10 +#> +#> $weight.ini +#> [1] "none" +#> +#> $tc.ini +#> sigma_low rsd_high +#> 0.50 0.07 +#> +#> $reweight.tol +#> [1] 1e-08 +#> +#> $reweight.max.iter +#> [1] 10 +#> +#> $bparms.optim +#> parent_0 k_parent_sink +#> 99.17407218 0.07815759 +#> +#> $bparms.fixed +#> numeric(0) +#> +#> $bparms.ode +#> k_parent_sink +#> 0.07815759 +#> +#> $bparms.state +#> parent +#> 99.17407 +#> +#> $date +#> [1] "Thu Jan 31 16:50:45 2019" +#> +#> $version +#> [1] "0.9.47.6" +#> +#> $Rversion +#> [1] "3.5.2" +#> +#> attr(,"class") +#> [1] "mkinfit" "modFit" +#>
#> coef.fit.fomc. coef.fit.iore. coef.fit.iore.deS. +#> model par 1 85.87489063 85.874890 85.874890 +#> model par 2 0.05192238 -4.826631 -4.826631 +#> model par 3 0.65096665 1.949403 1.949403
print(rbind(fomc = endpoints(fit.fomc)$distimes, iore = endpoints(fit.iore)$distimes, + iore.deS = endpoints(fit.iore)$distimes))
#> DT50 DT90 DT50back +#> fomc 1.785233 15.1479 4.559973 +#> iore 1.785233 15.1479 4.559973 +#> iore.deS 1.785233 15.1479 4.559973
#> [1] 4
ilr(c(b[1:3], 1 - sum(b[1:3]))) # Gives c(0.1, 0.3, 0.5)
#> [1] 0.1 0.3 0.5
#> df AIC +#> f_nw 5 204.4619 +#> f_obs 6 205.8727 +#> f_tc 6 143.8773 +#> f_man 4 291.8000
#> mkin version used for fitting: 0.9.47.6 +#> R version used for fitting: 3.5.2 +#> Date of fit: Thu Jan 31 16:51:07 2019 +#> Date of summary: Thu Jan 31 16:51:07 2019 +#> +#> Equations: +#> d_T245/dt = - k_T245_sink * T245 - k_T245_phenol * T245 +#> d_phenol/dt = + k_T245_phenol * T245 - k_phenol_sink * phenol - +#> k_phenol_anisole * phenol +#> d_anisole/dt = + k_phenol_anisole * phenol - k_anisole_sink * anisole +#> +#> Model predictions using solution type deSolve +#> +#> Fitted with method Port using 246 model solutions performed in 1.618 s +#> +#> Weighting: none +#> +#> Starting values for parameters to be optimised: +#> value type +#> T245_0 100.9000 state +#> k_T245_sink 0.1000 deparm +#> k_T245_phenol 0.1001 deparm +#> k_phenol_anisole 0.1002 deparm +#> k_anisole_sink 0.1003 deparm +#> +#> Starting values for the transformed parameters actually optimised: +#> value lower upper +#> T245_0 100.900000 -Inf Inf +#> log_k_T245_sink -2.302585 -Inf Inf +#> log_k_T245_phenol -2.301586 -Inf Inf +#> log_k_phenol_anisole -2.300587 -Inf Inf +#> log_k_anisole_sink -2.299590 -Inf Inf +#> +#> Fixed parameter values: +#> value type +#> phenol_0 0 state +#> anisole_0 0 state +#> k_phenol_sink 0 deparm +#> +#> Optimised, transformed parameters with symmetric confidence intervals: +#> Estimate Std. Error Lower Upper +#> T245_0 103.9000 2.35200 98.930 108.8000 +#> log_k_T245_sink -4.1130 0.13250 -4.390 -3.8350 +#> log_k_T245_phenol -3.6120 0.05002 -3.716 -3.5070 +#> log_k_phenol_anisole -0.9037 0.30580 -1.544 -0.2637 +#> log_k_anisole_sink -5.0090 0.11180 -5.243 -4.7750 +#> +#> Parameter correlation: +#> T245_0 log_k_T245_sink log_k_T245_phenol +#> T245_0 1.00000 0.63761 -0.1742 +#> log_k_T245_sink 0.63761 1.00000 -0.3831 +#> log_k_T245_phenol -0.17416 -0.38313 1.0000 +#> log_k_phenol_anisole -0.05948 0.08745 -0.3047 +#> log_k_anisole_sink -0.16208 -0.60469 0.5227 +#> log_k_phenol_anisole log_k_anisole_sink +#> T245_0 -0.05948 -0.1621 +#> log_k_T245_sink 0.08745 -0.6047 +#> log_k_T245_phenol -0.30470 0.5227 +#> log_k_phenol_anisole 1.00000 -0.1774 +#> log_k_anisole_sink -0.17744 1.0000 +#> +#> Residual standard error: 2.706 on 19 degrees of freedom +#> +#> Backtransformed parameters: +#> Confidence intervals for internally transformed parameters are asymmetric. +#> t-test (unrealistically) based on the assumption of normal distribution +#> for estimators of untransformed parameters. +#> Estimate t value Pr(>t) Lower Upper +#> T245_0 1.039e+02 44.160 6.462e-21 98.930000 108.80000 +#> k_T245_sink 1.636e-02 7.545 1.978e-07 0.012400 0.02159 +#> k_T245_phenol 2.701e-02 19.990 1.607e-14 0.024320 0.02999 +#> k_phenol_anisole 4.051e-01 3.270 2.014e-03 0.213600 0.76820 +#> k_anisole_sink 6.679e-03 8.942 1.544e-08 0.005285 0.00844 +#> +#> Chi2 error levels in percent: +#> err.min n.optim df +#> All data 9.831 5 17 +#> T245 7.908 3 5 +#> phenol 99.808 1 6 +#> anisole 5.379 1 6 +#> +#> Resulting formation fractions: +#> ff +#> T245_sink 0.3772 +#> T245_phenol 0.6228 +#> phenol_anisole 1.0000 +#> phenol_sink 0.0000 +#> anisole_sink 1.0000 +#> +#> Estimated disappearance times: +#> DT50 DT90 +#> T245 15.982 53.091 +#> phenol 1.711 5.685 +#> anisole 103.784 344.763
#> name time value +#> 1 x 1 1 +#> 2 x 2 4 +#> 3 x 3 7 +#> 4 y 1 3 +#> 5 y 2 4 +#> 6 y 3 5
#> Successfully compiled differential equation model from auto-generated C code.
+fit_FOCUS_D = mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE) +round(mkinerrmin(fit_FOCUS_D), 4)
#> err.min n.optim df +#> All data 0.0640 4 15 +#> parent 0.0646 2 7 +#> m1 0.0469 2 8
fit_FOCUS_E = mkinfit(SFO_SFO, FOCUS_2006_E, quiet = TRUE) + round(mkinerrmin(fit_FOCUS_E), 4)
#> err.min n.optim df +#> All data 0.1544 4 13 +#> parent 0.1659 2 7 +#> m1 0.1095 2 6
#> mkin version used for fitting: 0.9.47.6 +#> R version used for fitting: 3.5.2 +#> Date of fit: Thu Jan 31 16:51:28 2019 +#> Date of summary: Thu Jan 31 16:51:28 2019 +#> +#> Equations: +#> d_parent/dt = - k_parent * parent +#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 +#> +#> Model predictions using solution type deSolve +#> +#> Fitted with method Port using 270 model solutions performed in 1.257 s +#> +#> Weighting: manual +#> +#> Starting values for parameters to be optimised: +#> value type +#> parent_0 100.7500 state +#> k_parent 0.1000 deparm +#> k_m1 0.1001 deparm +#> f_parent_to_m1 0.5000 deparm +#> +#> Starting values for the transformed parameters actually optimised: +#> value lower upper +#> parent_0 100.750000 -Inf Inf +#> log_k_parent -2.302585 -Inf Inf +#> log_k_m1 -2.301586 -Inf Inf +#> f_parent_ilr_1 0.000000 -Inf Inf +#> +#> Fixed parameter values: +#> value type +#> m1_0 0 state +#> +#> Optimised, transformed parameters with symmetric confidence intervals: +#> Estimate Std. Error Lower Upper +#> parent_0 99.49000 1.33200 96.7800 102.2000 +#> log_k_parent -2.32100 0.03550 -2.3930 -2.2490 +#> log_k_m1 -5.24100 0.21280 -5.6730 -4.8100 +#> f_parent_ilr_1 0.04571 0.08966 -0.1361 0.2275 +#> +#> Parameter correlation: +#> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 +#> parent_0 1.00000 0.5312 -0.09456 -0.3351 +#> log_k_parent 0.53123 1.0000 -0.17800 -0.3360 +#> log_k_m1 -0.09456 -0.1780 1.00000 0.7616 +#> f_parent_ilr_1 -0.33514 -0.3360 0.76156 1.0000 +#> +#> Residual standard error: 2.628 on 36 degrees of freedom +#> +#> Backtransformed parameters: +#> Confidence intervals for internally transformed parameters are asymmetric. +#> t-test (unrealistically) based on the assumption of normal distribution +#> for estimators of untransformed parameters. +#> Estimate t value Pr(>t) Lower Upper +#> parent_0 99.490000 74.69 2.221e-41 96.780000 1.022e+02 +#> k_parent 0.098140 28.17 2.012e-26 0.091320 1.055e-01 +#> k_m1 0.005292 4.70 1.873e-05 0.003437 8.148e-03 +#> f_parent_to_m1 0.516200 16.30 1.686e-18 0.452000 5.798e-01 +#> +#> Chi2 error levels in percent: +#> err.min n.optim df +#> All data 6.400 4 15 +#> parent 6.454 2 7 +#> m1 4.708 2 8 +#> +#> Resulting formation fractions: +#> ff +#> parent_m1 0.5162 +#> parent_sink 0.4838 +#> +#> Estimated disappearance times: +#> DT50 DT90 +#> parent 7.063 23.46 +#> m1 130.971 435.08 +#> +#> Data: +#> time variable observed predicted residual err +#> 0 parent 99.46 99.48598 -0.025979 1 +#> 0 parent 102.04 99.48598 2.554021 1 +#> 1 parent 93.50 90.18612 3.313880 1 +#> 1 parent 92.50 90.18612 2.313880 1 +#> 3 parent 63.23 74.11316 -10.883163 1 +#> 3 parent 68.99 74.11316 -5.123163 1 +#> 7 parent 52.32 50.05030 2.269705 1 +#> 7 parent 55.13 50.05030 5.079705 1 +#> 14 parent 27.27 25.17975 2.090250 1 +#> 14 parent 26.64 25.17975 1.460250 1 +#> 21 parent 11.50 12.66765 -1.167654 1 +#> 21 parent 11.64 12.66765 -1.027654 1 +#> 35 parent 2.85 3.20616 -0.356164 1 +#> 35 parent 2.91 3.20616 -0.296164 1 +#> 50 parent 0.69 0.73562 -0.045619 1 +#> 50 parent 0.63 0.73562 -0.105619 1 +#> 75 parent 0.05 0.06326 -0.013256 1 +#> 75 parent 0.06 0.06326 -0.003256 1 +#> 0 m1 0.00 0.00000 0.000000 2 +#> 0 m1 0.00 0.00000 0.000000 2 +#> 1 m1 4.84 4.78729 0.052713 2 +#> 1 m1 5.64 4.78729 0.852713 2 +#> 3 m1 12.91 12.98785 -0.077848 2 +#> 3 m1 12.96 12.98785 -0.027848 2 +#> 7 m1 22.97 24.99695 -2.026946 2 +#> 7 m1 24.47 24.99695 -0.526946 2 +#> 14 m1 41.69 36.66353 5.026472 2 +#> 14 m1 33.21 36.66353 -3.453528 2 +#> 21 m1 44.37 41.65681 2.713186 2 +#> 21 m1 46.44 41.65681 4.783186 2 +#> 35 m1 41.22 43.35031 -2.130314 2 +#> 35 m1 37.95 43.35031 -5.400314 2 +#> 50 m1 41.19 41.25637 -0.066368 2 +#> 50 m1 40.01 41.25637 -1.246368 2 +#> 75 m1 40.09 36.46057 3.629429 2 +#> 75 m1 33.85 36.46057 -2.610571 2 +#> 100 m1 31.04 31.96929 -0.929293 2 +#> 100 m1 33.13 31.96929 1.160707 2 +#> 120 m1 25.15 28.76062 -3.610621 2 +#> 120 m1 33.31 28.76062 4.549379 2
f.w.man.irls <- mkinfit(SFO_SFO.ff, dw, err = "err.man", quiet = TRUE, + reweight.method = "obs") +summary(f.w.man.irls)
#> mkin version used for fitting: 0.9.47.6 +#> R version used for fitting: 3.5.2 +#> Date of fit: Thu Jan 31 16:51:31 2019 +#> Date of summary: Thu Jan 31 16:51:31 2019 +#> +#> Equations: +#> d_parent/dt = - k_parent * parent +#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 +#> +#> Model predictions using solution type deSolve +#> +#> Fitted with method Port using 692 model solutions performed in 3.3 s +#> +#> Weighting: manual +#> +#> Iterative reweighting with method obs +#> Final mean squared residuals of observed variables: +#> parent m1 +#> 11.573406 7.407846 +#> +#> Starting values for parameters to be optimised: +#> value type +#> parent_0 100.7500 state +#> k_parent 0.1000 deparm +#> k_m1 0.1001 deparm +#> f_parent_to_m1 0.5000 deparm +#> +#> Starting values for the transformed parameters actually optimised: +#> value lower upper +#> parent_0 100.750000 -Inf Inf +#> log_k_parent -2.302585 -Inf Inf +#> log_k_m1 -2.301586 -Inf Inf +#> f_parent_ilr_1 0.000000 -Inf Inf +#> +#> Fixed parameter values: +#> value type +#> m1_0 0 state +#> +#> Optimised, transformed parameters with symmetric confidence intervals: +#> Estimate Std. Error Lower Upper +#> parent_0 99.67000 1.79200 96.04000 103.300 +#> log_k_parent -2.31200 0.04560 -2.40400 -2.220 +#> log_k_m1 -5.25100 0.12510 -5.50500 -4.998 +#> f_parent_ilr_1 0.03785 0.06318 -0.09027 0.166 +#> +#> Parameter correlation: +#> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 +#> parent_0 1.0000 0.5083 -0.1979 -0.6148 +#> log_k_parent 0.5083 1.0000 -0.3894 -0.6062 +#> log_k_m1 -0.1979 -0.3894 1.0000 0.7417 +#> f_parent_ilr_1 -0.6148 -0.6062 0.7417 1.0000 +#> +#> Residual standard error: 1.054 on 36 degrees of freedom +#> +#> Backtransformed parameters: +#> Confidence intervals for internally transformed parameters are asymmetric. +#> t-test (unrealistically) based on the assumption of normal distribution +#> for estimators of untransformed parameters. +#> Estimate t value Pr(>t) Lower Upper +#> parent_0 99.67000 55.630 8.185e-37 96.040000 1.033e+02 +#> k_parent 0.09906 21.930 1.016e-22 0.090310 1.087e-01 +#> k_m1 0.00524 7.996 8.486e-10 0.004066 6.753e-03 +#> f_parent_to_m1 0.51340 23.000 2.039e-23 0.468100 5.584e-01 +#> +#> Chi2 error levels in percent: +#> err.min n.optim df +#> All data 6.399 4 15 +#> parent 6.466 2 7 +#> m1 4.679 2 8 +#> +#> Resulting formation fractions: +#> ff +#> parent_m1 0.5134 +#> parent_sink 0.4866 +#> +#> Estimated disappearance times: +#> DT50 DT90 +#> parent 6.997 23.24 +#> m1 132.282 439.43 +#> +#> Data: +#> time variable observed predicted residual err.ini err +#> 0 parent 99.46 99.67217 -2.122e-01 1 3.402 +#> 0 parent 102.04 99.67217 2.368e+00 1 3.402 +#> 1 parent 93.50 90.27152 3.228e+00 1 3.402 +#> 1 parent 92.50 90.27152 2.228e+00 1 3.402 +#> 3 parent 63.23 74.04648 -1.082e+01 1 3.402 +#> 3 parent 68.99 74.04648 -5.056e+00 1 3.402 +#> 7 parent 52.32 49.82092 2.499e+00 1 3.402 +#> 7 parent 55.13 49.82092 5.309e+00 1 3.402 +#> 14 parent 27.27 24.90288 2.367e+00 1 3.402 +#> 14 parent 26.64 24.90288 1.737e+00 1 3.402 +#> 21 parent 11.50 12.44765 -9.477e-01 1 3.402 +#> 21 parent 11.64 12.44765 -8.077e-01 1 3.402 +#> 35 parent 2.85 3.11002 -2.600e-01 1 3.402 +#> 35 parent 2.91 3.11002 -2.000e-01 1 3.402 +#> 50 parent 0.69 0.70375 -1.375e-02 1 3.402 +#> 50 parent 0.63 0.70375 -7.375e-02 1 3.402 +#> 75 parent 0.05 0.05913 -9.134e-03 1 3.402 +#> 75 parent 0.06 0.05913 8.661e-04 1 3.402 +#> 0 m1 0.00 0.00000 0.000e+00 2 2.722 +#> 0 m1 0.00 0.00000 0.000e+00 2 2.722 +#> 1 m1 4.84 4.81328 2.672e-02 2 2.722 +#> 1 m1 5.64 4.81328 8.267e-01 2 2.722 +#> 3 m1 12.91 13.04779 -1.378e-01 2 2.722 +#> 3 m1 12.96 13.04779 -8.779e-02 2 2.722 +#> 7 m1 22.97 25.07615 -2.106e+00 2 2.722 +#> 7 m1 24.47 25.07615 -6.062e-01 2 2.722 +#> 14 m1 41.69 36.70729 4.983e+00 2 2.722 +#> 14 m1 33.21 36.70729 -3.497e+00 2 2.722 +#> 21 m1 44.37 41.65050 2.719e+00 2 2.722 +#> 21 m1 46.44 41.65050 4.789e+00 2 2.722 +#> 35 m1 41.22 43.28866 -2.069e+00 2 2.722 +#> 35 m1 37.95 43.28866 -5.339e+00 2 2.722 +#> 50 m1 41.19 41.19339 -3.387e-03 2 2.722 +#> 50 m1 40.01 41.19339 -1.183e+00 2 2.722 +#> 75 m1 40.09 36.43820 3.652e+00 2 2.722 +#> 75 m1 33.85 36.43820 -2.588e+00 2 2.722 +#> 100 m1 31.04 31.98971 -9.497e-01 2 2.722 +#> 100 m1 33.13 31.98971 1.140e+00 2 2.722 +#> 120 m1 25.15 28.80897 -3.659e+00 2 2.722 +#> 120 m1 33.31 28.80897 4.501e+00 2 2.722
#> Successfully compiled differential equation model from auto-generated C code.
fit <- mkinfit(model, subset(mccall81_245T, soil == "Commerce"), quiet = TRUE) +mkinparplot(fit)
#> Successfully compiled differential equation model from auto-generated C code.
#> Successfully compiled differential equation model from auto-generated C code.
+ print(m_synth_SFO_lin)
#> <mkinmod> model generated with +#> Use of formation fractions $use_of_ff: max +#> Specification $spec: +#> $parent +#> $type: SFO; $to: M1; $sink: TRUE +#> $M1 +#> $type: SFO; $to: M2; $sink: TRUE +#> $M2 +#> $type: SFO; $sink: TRUE +#> Coefficient matrix $coefmat available +#> Compiled model $cf available +#> Differential equations: +#> d_parent/dt = - k_parent * parent +#> d_M1/dt = + f_parent_to_M1 * k_parent * parent - k_M1 * M1 +#> d_M2/dt = + f_M1_to_M2 * k_M1 * M1 - k_M2 * M2
#> Successfully compiled differential equation model from auto-generated C code.
+ +fit.ff.2 <- mkinfit(SFO_SFO.ff.2, FOCUS_2006_D, quiet = TRUE) +summary(fit.ff.2, data = FALSE)
#> mkin version used for fitting: 0.9.47.6 +#> R version used for fitting: 3.5.2 +#> Date of fit: Thu Jan 31 16:52:35 2019 +#> Date of summary: Thu Jan 31 16:52:35 2019 +#> +#> Equations: +#> d_parent/dt = - k_parent * parent +#> d_m1/dt = + k_parent * parent - k_m1 * m1 +#> +#> Model predictions using solution type deSolve +#> +#> Fitted with method Port using 104 model solutions performed in 0.482 s +#> +#> Weighting: none +#> +#> Starting values for parameters to be optimised: +#> value type +#> parent_0 100.7500 state +#> k_parent 0.1000 deparm +#> k_m1 0.1001 deparm +#> +#> Starting values for the transformed parameters actually optimised: +#> value lower upper +#> parent_0 100.750000 -Inf Inf +#> log_k_parent -2.302585 -Inf Inf +#> log_k_m1 -2.301586 -Inf Inf +#> +#> Fixed parameter values: +#> value type +#> m1_0 0 state +#> +#> Optimised, transformed parameters with symmetric confidence intervals: +#> Estimate Std. Error Lower Upper +#> parent_0 84.790 2.96500 78.78 90.800 +#> log_k_parent -2.756 0.08088 -2.92 -2.593 +#> log_k_m1 -4.214 0.11150 -4.44 -3.988 +#> +#> Parameter correlation: +#> parent_0 log_k_parent log_k_m1 +#> parent_0 1.0000 0.11058 0.46156 +#> log_k_parent 0.1106 1.00000 0.06274 +#> log_k_m1 0.4616 0.06274 1.00000 +#> +#> Residual standard error: 8.333 on 37 degrees of freedom +#> +#> Backtransformed parameters: +#> Confidence intervals for internally transformed parameters are asymmetric. +#> t-test (unrealistically) based on the assumption of normal distribution +#> for estimators of untransformed parameters. +#> Estimate t value Pr(>t) Lower Upper +#> parent_0 84.79000 28.600 3.939e-27 78.78000 90.80000 +#> k_parent 0.06352 12.360 5.237e-15 0.05392 0.07483 +#> k_m1 0.01478 8.966 4.114e-11 0.01179 0.01853 +#> +#> Chi2 error levels in percent: +#> err.min n.optim df +#> All data 19.66 3 16 +#> parent 17.56 2 7 +#> m1 18.71 1 9 +#> +#> Estimated disappearance times: +#> DT50 DT90 +#> parent 10.91 36.25 +#> m1 46.89 155.75