From e7b2d16306b7d03cde66223c9b27abde928f9447 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 18 Nov 2016 16:28:49 +0100 Subject: Improve examples for showing with pkgdown - Use quiet= TRUE in dontrun sections - Use mkinsub in model definitions - Avoid \code{\link{}} in titles --- docs/reference/Extract.mmkin.html | 711 +---------- docs/reference/endpoints.html | 16 +- docs/reference/index.html | 23 +- docs/reference/mccall81_245T.html | 164 +-- docs/reference/mkinerrmin.html | 9 +- docs/reference/mkinfit.html | 1372 +-------------------- docs/reference/mkinparplot.html | 12 +- docs/reference/mmkin-10.png | Bin 0 -> 30013 bytes docs/reference/mmkin-12.png | Bin 0 -> 30013 bytes docs/reference/mmkin-14.png | Bin 30013 -> 27062 bytes docs/reference/mmkin-16.png | Bin 27062 -> 25359 bytes docs/reference/mmkin-18.png | Bin 25359 -> 18905 bytes docs/reference/mmkin-20.png | Bin 18905 -> 17036 bytes docs/reference/mmkin.html | 1107 +---------------- docs/reference/plot.mmkin.html | 7 +- docs/reference/print.mkinmod.html | 23 +- docs/reference/schaefer07_complex_case-4.png | Bin 0 -> 18198 bytes docs/reference/schaefer07_complex_case.html | 1086 +--------------- docs/reference/summary.mkinfit.html | 8 +- docs/reference/synthetic_data_for_UBA.html | 938 ++------------ docs/reference/synthetic_data_for_UBA_2014-10.png | Bin 0 -> 18905 bytes docs/reference/transform_odeparms.html | 297 +---- 22 files changed, 352 insertions(+), 5421 deletions(-) create mode 100644 docs/reference/mmkin-10.png create mode 100644 docs/reference/mmkin-12.png create mode 100644 docs/reference/schaefer07_complex_case-4.png create mode 100644 docs/reference/synthetic_data_for_UBA_2014-10.png (limited to 'docs') diff --git a/docs/reference/Extract.mmkin.html b/docs/reference/Extract.mmkin.html index 09d7513c..6a75a530 100644 --- a/docs/reference/Extract.mmkin.html +++ b/docs/reference/Extract.mmkin.html @@ -120,7 +120,20 @@ #> SFO List,42 #> FOMC List,42 #> attr(,"class") -#> [1] "mmkin"
fits[, "B", drop = TRUE]$FOMC
#> $par +#> [1] "mmkin"
fits["SFO", "B"]
#> dataset +#> model B +#> SFO List,42 +#> attr(,"class") +#> [1] "mmkin"
+ head( + # This extracts an mkinfit object with lots of components + fits[["FOMC", "B"]] + )
#> +#> Attaching package: ‘utils’
#> The following objects are masked from ‘devtools_shims’: +#> +#> ?, help
#> The following object is masked from ‘package:inline’: +#> +#> package.skeleton
#> $par #> parent_0 log_alpha log_beta #> 99.666193 2.549849 5.050586 #> @@ -139,689 +152,11 @@ #> #> $counts #> [1] "both X-convergence and relative convergence (5)" -#> -#> $hessian -#> parent_0 log_alpha log_beta -#> parent_0 4.123033 -95.69983 93.17699 -#> log_alpha -95.699832 6618.85833 -6352.46648 -#> log_beta 93.176993 -6352.46648 6101.23483 -#> -#> $residuals -#> parent parent parent parent parent parent -#> 1.046192647 -3.322396479 3.655156669 -1.705316770 0.406306255 -0.123734689 -#> parent parent -#> -0.036886982 -0.006240458 -#> -#> $ms -#> [1] 3.572863 -#> -#> $var_ms -#> parent -#> 3.572863 -#> -#> $var_ms_unscaled -#> parent -#> 3.572863 -#> -#> $var_ms_unweighted -#> parent -#> 3.572863 -#> -#> $rank -#> [1] 3 -#> -#> $df.residual -#> [1] 5 -#> -#> $solution_type -#> [1] "analytical" -#> -#> $transform_rates -#> [1] TRUE -#> -#> $transform_fractions -#> [1] TRUE -#> -#> $method.modFit -#> [1] "Port" -#> -#> $maxit.modFit -#> [1] "auto" -#> -#> $calls -#> [1] 111 -#> -#> $time -#> user system elapsed -#> 0.256 0.000 0.255 -#> -#> $mkinmod -#> <mkinmod> model generated with -#> Use of formation fractions $use_of_ff: min -#> Specification $spec: -#> $parent -#> $type: FOMC; $sink: TRUE -#> -#> $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 99.66619265 -#> 2 parent 1.191919 90.41690342 -#> 3 parent 2.383838 82.08630014 -#> 4 parent 3.000000 78.10760352 -#> 5 parent 3.575758 74.57722848 -#> 6 parent 4.767677 67.80342415 -#> 7 parent 5.959596 61.68822425 -#> 8 parent 7.000000 56.83515667 -#> 9 parent 7.151515 56.16343898 -#> 10 parent 8.343434 51.16836285 -#> 11 parent 9.535354 46.64890734 -#> 12 parent 10.727273 42.55683931 -#> 13 parent 11.919192 38.84911158 -#> 14 parent 13.111111 35.48727414 -#> 15 parent 14.000000 33.18468323 -#> 16 parent 14.303030 32.43695565 -#> 17 parent 15.494949 29.66740651 -#> 18 parent 16.686869 27.15109578 -#> 19 parent 17.878788 24.86335532 -#> 20 parent 19.070707 22.78206538 -#> 21 parent 20.262626 20.88737647 -#> 22 parent 21.454545 19.16146324 -#> 23 parent 22.646465 17.58830644 -#> 24 parent 23.838384 16.15349953 -#> 25 parent 25.030303 14.84407724 -#> 26 parent 26.222222 13.64836315 -#> 27 parent 27.414141 12.55583436 -#> 28 parent 28.606061 11.55700107 -#> 29 parent 29.797980 10.64329940 -#> 30 parent 30.000000 10.49630626 -#> 31 parent 30.989899 9.80699593 -#> 32 parent 32.181818 9.04110261 -#> 33 parent 33.373737 8.33930082 -#> 34 parent 34.565657 7.69587362 -#> 35 parent 35.757576 7.10564515 -#> 36 parent 36.949495 6.56392657 -#> 37 parent 38.141414 6.06646759 -#> 38 parent 39.333333 5.60941311 -#> 39 parent 40.525253 5.18926438 -#> 40 parent 41.717172 4.80284421 -#> 41 parent 42.909091 4.44726569 -#> 42 parent 44.101010 4.11990420 -#> 43 parent 45.292929 3.81837216 -#> 44 parent 46.484848 3.54049644 -#> 45 parent 47.676768 3.28429799 -#> 46 parent 48.868687 3.04797350 -#> 47 parent 50.060606 2.82987892 -#> 48 parent 51.252525 2.62851456 -#> 49 parent 52.444444 2.44251172 -#> 50 parent 53.636364 2.27062056 -#> 51 parent 54.828283 2.11169922 -#> 52 parent 56.020202 1.96470393 -#> 53 parent 57.212121 1.82868009 -#> 54 parent 58.404040 1.70275424 -#> 55 parent 59.595960 1.58612677 -#> 56 parent 60.787879 1.47806529 -#> 57 parent 61.979798 1.37789865 -#> 58 parent 62.000000 1.37626531 -#> 59 parent 63.171717 1.28501157 -#> 60 parent 64.363636 1.19883967 -#> 61 parent 65.555556 1.11886504 -#> 62 parent 66.747475 1.04461220 -#> 63 parent 67.939394 0.97564441 -#> 64 parent 69.131313 0.91156031 -#> 65 parent 70.323232 0.85199096 -#> 66 parent 71.515152 0.79659697 -#> 67 parent 72.707071 0.74506609 -#> 68 parent 73.898990 0.69711084 -#> 69 parent 75.090909 0.65246649 -#> 70 parent 76.282828 0.61088912 -#> 71 parent 77.474747 0.57215389 -#> 72 parent 78.666667 0.53605348 -#> 73 parent 79.858586 0.50239663 -#> 74 parent 81.050505 0.47100683 -#> 75 parent 82.242424 0.44172111 -#> 76 parent 83.434343 0.41438896 -#> 77 parent 84.626263 0.38887128 -#> 78 parent 85.818182 0.36503953 -#> 79 parent 87.010101 0.34277481 -#> 80 parent 88.202020 0.32196716 -#> 81 parent 89.393939 0.30251479 -#> 82 parent 90.000000 0.29311302 -#> 83 parent 90.585859 0.28432347 -#> 84 parent 91.777778 0.26730596 -#> 85 parent 92.969697 0.25138141 -#> 86 parent 94.161616 0.23647487 -#> 87 parent 95.353535 0.22251689 -#> 88 parent 96.545455 0.20944302 -#> 89 parent 97.737374 0.19719349 -#> 90 parent 98.929293 0.18571281 -#> 91 parent 100.121212 0.17494947 -#> 92 parent 101.313131 0.16485560 -#> 93 parent 102.505051 0.15538676 -#> 94 parent 103.696970 0.14650163 -#> 95 parent 104.888889 0.13816179 -#> 96 parent 106.080808 0.13033150 -#> 97 parent 107.272727 0.12297753 -#> 98 parent 108.464646 0.11606895 -#> 99 parent 109.656566 0.10957695 -#> 100 parent 110.848485 0.10347470 -#> 101 parent 112.040404 0.09773723 -#> 102 parent 113.232323 0.09234125 -#> 103 parent 114.424242 0.08726506 -#> 104 parent 115.616162 0.08248842 -#> 105 parent 116.808081 0.07799245 -#> 106 parent 118.000000 0.07375954 -#> -#> $cost -#> function (P) -#> { -#> assign("calls", calls + 1, inherits = TRUE) -#> if (trace_parms) -#> cat(P, "\n") -#> if (length(state.ini.optim) > 0) { -#> odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed) -#> names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames) -#> } -#> else { -#> odeini <- state.ini.fixed -#> names(odeini) <- state.ini.fixed.boxnames -#> } -#> odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], -#> transparms.fixed) -#> parms <- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates, -#> transform_fractions = transform_fractions) -#> out <- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, -#> use_compiled = use_compiled, method.ode = method.ode, -#> atol = atol, rtol = rtol, ...) -#> assign("out_predicted", out, inherits = TRUE) -#> mC <- modCost(out, observed, y = "value", err = err, weight = weight, -#> scaleVar = scaleVar) -#> if (mC$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) -#> } -#> <environment: 0x3fc5fa0> -#> -#> $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) -#> } -#> <environment: 0x3fc5fa0> -#> -#> $hessian_notrans -#> parent_0 alpha beta -#> parent_0 4.1230329 -7.473531 0.5968527 -#> alpha -7.4735307 40.365690 -3.1777189 -#> beta 0.5968527 -3.177719 0.2503425 -#> -#> $start -#> value type -#> parent_0 98.62 state -#> alpha 1.00 deparm -#> beta 10.00 deparm -#> -#> $start_transformed -#> value lower upper -#> parent_0 98.620000 -Inf Inf -#> log_alpha 0.000000 -Inf Inf -#> log_beta 2.302585 -Inf Inf -#> -#> $fixed -#> [1] value type -#> <0 rows> (or 0-length row.names) -#> -#> $data -#> time variable observed predicted residual -#> 1 0 parent 98.62 99.66619265 -1.046192647 -#> 2 3 parent 81.43 78.10760352 3.322396479 -#> 3 7 parent 53.18 56.83515667 -3.655156669 -#> 4 14 parent 34.89 33.18468323 1.705316770 -#> 5 30 parent 10.09 10.49630626 -0.406306255 -#> 6 62 parent 1.50 1.37626531 0.123734689 -#> 7 90 parent 0.33 0.29311302 0.036886982 -#> 8 118 parent 0.08 0.07375954 0.006240458 -#> -#> $atol -#> [1] 1e-08 -#> -#> $rtol -#> [1] 1e-10 -#> -#> $weight.ini -#> [1] "none" -#> -#> $reweight.tol -#> [1] 1e-08 -#> -#> $reweight.max.iter -#> [1] 10 -#> -#> $bparms.optim -#> parent_0 alpha beta -#> 99.66619 12.80517 156.11390 -#> -#> $bparms.fixed -#> numeric(0) -#> -#> $bparms.ode -#> alpha beta -#> 12.80517 156.11390 -#> -#> $bparms.state -#> parent -#> 99.66619 -#> -#> $date -#> [1] "Fri Nov 18 15:19:25 2016" -#> -#> attr(,"class") -#> [1] "mkinfit" "modFit"
fits["SFO", "B"]
#> dataset -#> model B -#> SFO List,42 -#> attr(,"class") -#> [1] "mmkin"
fits[["SFO", "B"]] # This is equivalent to
#> $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 elapsed -#> 0.064 0.000 0.066 -#> -#> $mkinmod -#> <mkinmod> model generated with -#> Use of formation fractions $use_of_ff: min -#> Specification $spec: -#> $parent -#> $type: SFO; $sink: TRUE -#> Coefficient matrix $coefmat available -#> -#> $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) -#> } -#> <environment: 0x3b66828> -#> -#> $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) -#> } -#> <environment: 0x3b66828> -#> -#> $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 rows> (or 0-length row.names) -#> -#> $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" -#> -#> $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] "Fri Nov 18 15:19:25 2016" -#> -#> attr(,"class") -#> [1] "mkinfit" "modFit"
fits["SFO", "B", drop = TRUE]
#> [[1]] +#>
+ head( + # The same can be achieved by + fits["SFO", "B", drop = TRUE] + )
#> [[1]] #> $par #> parent_0 log_k_parent_sink #> 99.174072 -2.549028 @@ -894,7 +229,7 @@ #> #> $time #> user system elapsed -#> 0.064 0.000 0.066 +#> 0.068 0.000 0.070 #> #> $mkinmod #> <mkinmod> model generated with @@ -1081,7 +416,7 @@ #> } #> return(mC) #> } -#> <environment: 0x3b66828> +#> <environment: 0x3a3e8a8> #> #> $cost_notrans #> function (P) @@ -1103,7 +438,7 @@ #> scaleVar = scaleVar) #> return(mC) #> } -#> <environment: 0x3b66828> +#> <environment: 0x3a3e8a8> #> #> $hessian_notrans #> parent_0 k_parent_sink @@ -1166,7 +501,7 @@ #> 99.17407 #> #> $date -#> [1] "Fri Nov 18 15:19:25 2016" +#> [1] "Fri Nov 18 16:03:57 2016" #> #> attr(,"class") #> [1] "mkinfit" "modFit" diff --git a/docs/reference/endpoints.html b/docs/reference/endpoints.html index 990ceadf..7e3455c9 100644 --- a/docs/reference/endpoints.html +++ b/docs/reference/endpoints.html @@ -106,6 +106,18 @@ with the advantage that the SFORB model can also be used for metabolites.

A list with the components mentioned above.

+

Examples

+ 
  fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
+  endpoints(fit)
#> $ff
+#> logical(0)
+#> 
+#> $SFORB
+#> logical(0)
+#> 
+#> $distimes
+#>            DT50    DT90 DT50back
+#> parent 1.785233 15.1479 4.559973
+#>
diff --git a/docs/reference/schaefer07_complex_case-4.png b/docs/reference/schaefer07_complex_case-4.png new file mode 100644 index 00000000..b87ccb82 Binary files /dev/null and b/docs/reference/schaefer07_complex_case-4.png differ diff --git a/docs/reference/schaefer07_complex_case.html b/docs/reference/schaefer07_complex_case.html index 9bb6d4ee..1bcc1826 100644 --- a/docs/reference/schaefer07_complex_case.html +++ b/docs/reference/schaefer07_complex_case.html @@ -116,1059 +116,39 @@ A1 = list(type = "SFO", to = "A2"), B1 = list(type = "SFO"), C1 = list(type = "SFO"), - A2 = list(type = "SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.
mkinfit(model, data)
#> Model cost at call 1 : 2511.655 -#> Model cost at call 2 : 2511.655 -#> Model cost at call 11 : 1436.639 -#> Model cost at call 12 : 1436.638 -#> Model cost at call 13 : 1436.566 -#> Model cost at call 21 : 643.6583 -#> Model cost at call 22 : 643.6583 -#> Model cost at call 23 : 643.6582 -#> Model cost at call 29 : 643.6576 -#> Model cost at call 31 : 454.0244 -#> Model cost at call 32 : 454.0241 -#> Model cost at call 34 : 454.0229 -#> Model cost at call 43 : 378.1144 -#> Model cost at call 45 : 378.1143 -#> Model cost at call 53 : 357.245 -#> Model cost at call 55 : 357.2449 -#> Model cost at call 56 : 357.2447 -#> Model cost at call 63 : 354.3415 -#> Model cost at call 64 : 354.3415 -#> Model cost at call 65 : 354.3413 -#> Model cost at call 73 : 332.49 -#> Model cost at call 74 : 332.49 -#> Model cost at call 81 : 332.4899 -#> Model cost at call 83 : 315.2962 -#> Model cost at call 84 : 306.3085 -#> Model cost at call 86 : 306.3084 -#> Model cost at call 87 : 306.3084 -#> Model cost at call 92 : 306.3083 -#> Model cost at call 94 : 290.6377 -#> Model cost at call 96 : 290.6375 -#> Model cost at call 98 : 290.6375 -#> Model cost at call 101 : 290.6371 -#> Model cost at call 105 : 269.09 -#> Model cost at call 107 : 269.0899 -#> Model cost at call 115 : 259.7551 -#> Model cost at call 120 : 259.7549 -#> Model cost at call 123 : 259.7547 -#> Model cost at call 126 : 253.7973 -#> Model cost at call 128 : 253.7972 -#> Model cost at call 137 : 251.7358 -#> Model cost at call 139 : 251.7358 -#> Model cost at call 147 : 250.7394 -#> Model cost at call 149 : 250.7393 -#> Model cost at call 157 : 249.1148 -#> Model cost at call 159 : 249.1148 -#> Model cost at call 167 : 246.8768 -#> Model cost at call 169 : 246.8768 -#> Model cost at call 177 : 244.9758 -#> Model cost at call 179 : 244.9758 -#> Model cost at call 187 : 243.2914 -#> Model cost at call 189 : 243.2914 -#> Model cost at call 190 : 243.2914 -#> Model cost at call 194 : 243.2914 -#> Model cost at call 199 : 242.9202 -#> Model cost at call 201 : 242.9202 -#> Model cost at call 202 : 242.9202 -#> Model cost at call 209 : 242.7695 -#> Model cost at call 211 : 242.7695 -#> Model cost at call 216 : 242.7695 -#> Model cost at call 219 : 242.5771 -#> Model cost at call 221 : 242.5771 -#> Model cost at call 229 : 242.4402 -#> Model cost at call 231 : 242.4402 -#> Model cost at call 239 : 242.1878 -#> Model cost at call 241 : 242.1878 -#> Model cost at call 249 : 242.0553 -#> Model cost at call 251 : 242.0553 -#> Model cost at call 256 : 242.0553 -#> Model cost at call 259 : 241.8761 -#> Model cost at call 260 : 241.7412 -#> Model cost at call 261 : 241.6954 -#> Model cost at call 264 : 241.6954 -#> Model cost at call 275 : 241.5982 -#> Model cost at call 277 : 241.5982 -#> Model cost at call 285 : 241.5459 -#> Model cost at call 287 : 241.5459 -#> Model cost at call 295 : 241.4837 -#> Model cost at call 297 : 241.4837 -#> Model cost at call 305 : 241.3882 -#> Model cost at call 306 : 241.3161 -#> Model cost at call 307 : 241.2315 -#> Model cost at call 309 : 241.2315 -#> Model cost at call 314 : 241.2315 -#> Model cost at call 317 : 240.9738 -#> Model cost at call 322 : 240.9738 -#> Model cost at call 327 : 240.8244 -#> Model cost at call 329 : 240.8244 -#> Model cost at call 337 : 240.7005 -#> Model cost at call 339 : 240.7005 -#> Model cost at call 342 : 240.7005 -#> Model cost at call 347 : 240.629 -#> Model cost at call 350 : 240.629 -#> Model cost at call 357 : 240.6193 -#> Model cost at call 358 : 240.6193 -#> Model cost at call 364 : 240.6193 -#> Model cost at call 367 : 240.6193 -#> Model cost at call 369 : 240.5873 -#> Model cost at call 374 : 240.5873 -#> Model cost at call 380 : 240.578 -#> Model cost at call 382 : 240.578 -#> Model cost at call 390 : 240.5723 -#> Model cost at call 393 : 240.5723 -#> Model cost at call 403 : 240.569 -#> Model cost at call 404 : 240.569 -#> Model cost at call 413 : 240.569 -#> Model cost at call 415 : 240.5688 -#> Model cost at call 416 : 240.5688 -#> Model cost at call 417 : 240.5688 -#> Model cost at call 431 : 240.5686 -#> Model cost at call 432 : 240.5686 -#> Model cost at call 434 : 240.5686 -#> Model cost at call 443 : 240.5686 -#> Model cost at call 444 : 240.5686 -#> Model cost at call 447 : 240.5686 -#> Model cost at call 449 : 240.5686 -#> Model cost at call 450 : 240.5686 -#> Model cost at call 466 : 240.5686 -#> Model cost at call 470 : 240.5686 -#> Model cost at call 485 : 240.5686 -#> Model cost at call 509 : 240.5686 -#> Optimisation by method Port successfully terminated.
#> $par -#> parent_0 log_k_parent log_k_A1 log_k_B1 log_k_C1 -#> 91.9181598 -3.0020485 -4.2735924 -3.9846764 -2.7852180 -#> log_k_A2 f_parent_ilr_1 f_parent_ilr_2 f_A1_ilr_1 -#> -3.7166415 0.4718588 -0.3589948 -0.1477244 -#> -#> $ssr -#> [1] 240.5686 -#> -#> $convergence -#> [1] 0 -#> -#> $iterations -#> [1] 43 -#> -#> $evaluations -#> function gradient -#> 62 441 -#> -#> $counts -#> [1] "relative convergence (4)" -#> -#> $hessian -#> parent_0 log_k_parent log_k_A1 log_k_B1 -#> parent_0 7.3650812 -92.141920 -1.001134e+01 -2.432415e+00 -#> log_k_parent -92.1419204 6632.673492 -4.316240e+01 -1.320833e+01 -#> log_k_A1 -10.0113364 -43.162398 6.071628e+02 0.000000e+00 -#> log_k_B1 -2.4324147 -13.208329 0.000000e+00 1.572303e+02 -#> log_k_C1 -4.7153201 -118.288037 -5.878291e-05 -3.073041e-06 -#> log_k_A2 -0.4360727 -5.304259 -1.977980e+01 0.000000e+00 -#> f_parent_ilr_1 10.5460899 271.145438 -5.299954e+02 1.874235e+02 -#> f_parent_ilr_2 11.6409409 222.570696 -4.773816e+02 -1.159875e+02 -#> f_A1_ilr_1 0.5572072 10.374810 2.850173e+01 0.000000e+00 -#> log_k_C1 log_k_A2 f_parent_ilr_1 f_parent_ilr_2 -#> parent_0 -4.715320e+00 -4.360727e-01 10.54609 11.64094 -#> log_k_parent -1.182880e+02 -5.304259e+00 271.14544 222.57070 -#> log_k_A1 -5.878291e-05 -1.977980e+01 -529.99537 -477.38164 -#> log_k_B1 -3.073041e-06 0.000000e+00 187.42348 -115.98754 -#> log_k_C1 3.372749e+02 -2.395674e-06 56.85184 305.98862 -#> log_k_A2 -2.395674e-06 2.749192e+01 -23.08549 -20.79373 -#> f_parent_ilr_1 5.685184e+01 -2.308549e+01 1256.24941 632.09769 -#> f_parent_ilr_2 3.059886e+02 -2.079373e+01 632.09769 1250.65147 -#> f_A1_ilr_1 3.158891e-06 -3.129286e+01 29.49830 26.56991 -#> f_A1_ilr_1 -#> parent_0 5.572072e-01 -#> log_k_parent 1.037481e+01 -#> log_k_A1 2.850173e+01 -#> log_k_B1 0.000000e+00 -#> log_k_C1 3.158891e-06 -#> log_k_A2 -3.129286e+01 -#> f_parent_ilr_1 2.949830e+01 -#> f_parent_ilr_2 2.656991e+01 -#> f_A1_ilr_1 3.998554e+01 -#> -#> $residuals -#> parent parent parent parent parent parent parent -#> -1.2818402 -1.9372115 -0.5105519 3.8165318 -2.3531716 4.8043342 -2.2775432 -#> parent A1 A1 A1 A1 A1 A1 -#> -5.3608524 4.1967522 2.9032987 -1.3124875 -0.6021093 2.5092324 -1.8861396 -#> B1 B1 B1 B1 B1 C1 C1 -#> 4.3801768 5.5002481 -5.7917184 1.3852658 0.5313301 1.2796458 1.7105311 -#> C1 C1 C1 C1 C1 A2 A2 -#> 3.7116712 -0.1182953 0.5228429 -0.8570298 -3.5476556 -0.5447276 -1.3652404 -#> A2 A2 A2 A2 A2 -#> -0.3330261 -0.5802059 0.1285850 0.2119280 -0.1381990 -#> -#> $ms -#> [1] 7.289956 -#> -#> $var_ms -#> parent A1 B1 C1 A2 -#> 10.3459333 6.3301336 17.0367907 4.5639474 0.3841002 -#> -#> $var_ms_unscaled -#> parent A1 B1 C1 A2 -#> 10.3459333 6.3301336 17.0367907 4.5639474 0.3841002 -#> -#> $var_ms_unweighted -#> parent A1 B1 C1 A2 -#> 10.3459333 6.3301336 17.0367907 4.5639474 0.3841002 -#> -#> $rank -#> [1] 9 -#> -#> $df.residual -#> [1] 24 -#> -#> $solution_type -#> [1] "deSolve" -#> -#> $transform_rates -#> [1] TRUE -#> -#> $transform_fractions -#> [1] TRUE -#> -#> $method.modFit -#> [1] "Port" -#> -#> $maxit.modFit -#> [1] "auto" -#> -#> $calls -#> [1] 523 -#> -#> $time -#> user system elapsed -#> 5.004 0.000 5.004 -#> -#> $mkinmod -#> <mkinmod> model generated with -#> Use of formation fractions $use_of_ff: max -#> Specification $spec: -#> $parent -#> $type: SFO; $to: A1, B1, C1; $sink: FALSE -#> $A1 -#> $type: SFO; $to: A2; $sink: TRUE -#> $B1 -#> $type: SFO; $sink: TRUE -#> $C1 -#> $type: SFO; $sink: TRUE -#> $A2 -#> $type: SFO; $sink: TRUE -#> Coefficient matrix $coefmat available -#> Compiled model $cf available -#> -#> $observed -#> name time value -#> 1 parent 0 93.20 -#> 2 parent 1 89.40 -#> 3 parent 3 79.70 -#> 4 parent 7 61.10 -#> 5 parent 14 48.20 -#> 6 parent 30 15.90 -#> 7 parent 62 6.50 -#> 8 parent 100 6.00 -#> 9 A1 0 NA -#> 10 A1 1 NA -#> 11 A1 3 0.55 -#> 12 A1 7 6.87 -#> 13 A1 14 17.08 -#> 14 A1 30 21.68 -#> 15 A1 62 15.77 -#> 16 A1 100 13.63 -#> 17 B1 0 NA -#> 18 B1 1 NA -#> 19 B1 3 NA -#> 20 B1 7 0.55 -#> 21 B1 14 2.31 -#> 22 B1 30 15.76 -#> 23 B1 62 6.36 -#> 24 B1 100 3.74 -#> 25 C1 0 NA -#> 26 C1 1 0.55 -#> 27 C1 3 3.20 -#> 28 C1 7 5.46 -#> 29 C1 14 12.55 -#> 30 C1 30 10.45 -#> 31 C1 62 4.74 -#> 32 C1 100 4.33 -#> 33 A2 0 NA -#> 34 A2 1 0.55 -#> 35 A2 3 1.41 -#> 36 A2 7 0.55 -#> 37 A2 14 1.29 -#> 38 A2 30 1.95 -#> 39 A2 62 3.54 -#> 40 A2 100 3.86 -#> -#> $obs_vars -#> [1] "parent" "A1" "B1" "C1" "A2" -#> -#> $predicted -#> name time value -#> 1 parent 0.000000 91.918159794 -#> 2 parent 1.000000 87.462788491 -#> 3 parent 1.010101 87.418904506 -#> 4 parent 2.020202 83.139880984 -#> 5 parent 3.000000 79.189448055 -#> 6 parent 3.030303 79.070309209 -#> 7 parent 4.040404 75.199936833 -#> 8 parent 5.050505 71.519013349 -#> 9 parent 6.060606 68.018265517 -#> 10 parent 7.000000 64.916531757 -#> 11 parent 7.070707 64.688874011 -#> 12 parent 8.080808 61.522451197 -#> 13 parent 9.090909 58.511020005 -#> 14 parent 10.101010 55.646993828 -#> 15 parent 11.111111 52.923157412 -#> 16 parent 12.121212 50.332648680 -#> 17 parent 13.131313 47.868941444 -#> 18 parent 14.000000 45.846828365 -#> 19 parent 14.141414 45.525828960 -#> 20 parent 15.151515 43.297408299 -#> 21 parent 16.161616 41.178065468 -#> 22 parent 17.171717 39.162461272 -#> 23 parent 18.181818 37.245517861 -#> 24 parent 19.191919 35.422405939 -#> 25 parent 20.202020 33.688532595 -#> 26 parent 21.212121 32.039529737 -#> 27 parent 22.222222 30.471243081 -#> 28 parent 23.232323 28.979721692 -#> 29 parent 24.242424 27.561208025 -#> 30 parent 25.252525 26.212128463 -#> 31 parent 26.262626 24.929084310 -#> 32 parent 27.272727 23.708843233 -#> 33 parent 28.282828 22.548331117 -#> 34 parent 29.292929 21.444624318 -#> 35 parent 30.000000 20.704334210 -#> 36 parent 30.303030 20.394942302 -#> 37 parent 31.313131 19.396640638 -#> 38 parent 32.323232 18.447204335 -#> 39 parent 33.333333 17.544241506 -#> 40 parent 34.343434 16.685477346 -#> 41 parent 35.353535 15.868748397 -#> 42 parent 36.363636 15.091997098 -#> 43 parent 37.373737 14.353266603 -#> 44 parent 38.383838 13.650695852 -#> 45 parent 39.393939 12.982514879 -#> 46 parent 40.404040 12.347040357 -#> 47 parent 41.414141 11.742671354 -#> 48 parent 42.424242 11.167885303 -#> 49 parent 43.434343 10.621234162 -#> 50 parent 44.444444 10.101340770 -#> 51 parent 45.454545 9.606895375 -#> 52 parent 46.464646 9.136652336 -#> 53 parent 47.474747 8.689426985 -#> 54 parent 48.484848 8.264092640 -#> 55 parent 49.494949 7.859577770 -#> 56 parent 50.505051 7.474863293 -#> 57 parent 51.515152 7.108980009 -#> 58 parent 52.525253 6.761006160 -#> 59 parent 53.535354 6.430065106 -#> 60 parent 54.545455 6.115323117 -#> 61 parent 55.555556 5.815987274 -#> 62 parent 56.565657 5.531303470 -#> 63 parent 57.575758 5.260554508 -#> 64 parent 58.585859 5.003058299 -#> 65 parent 59.595960 4.758166141 -#> 66 parent 60.606061 4.525261085 -#> 67 parent 61.616162 4.303756381 -#> 68 parent 62.000000 4.222456793 -#> 69 parent 62.626263 4.093093997 -#> 70 parent 63.636364 3.892743220 -#> 71 parent 64.646465 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17.575561608 -#> 129 A1 18.181818 18.055089316 -#> 130 A1 19.191919 18.492422399 -#> 131 A1 20.202020 18.889887843 -#> 132 A1 21.212121 19.249695079 -#> 133 A1 22.222222 19.573941783 -#> 134 A1 23.232323 19.864619397 -#> 135 A1 24.242424 20.123618383 -#> 136 A1 25.252525 20.352733211 -#> 137 A1 26.262626 20.553667106 -#> 138 A1 27.272727 20.728036563 -#> 139 A1 28.282828 20.877375640 -#> 140 A1 29.292929 21.003140039 -#> 141 A1 30.000000 21.077890710 -#> 142 A1 30.303030 21.106710984 -#> 143 A1 31.313131 21.189398917 -#> 144 A1 32.323232 21.252447002 -#> 145 A1 33.333333 21.297034466 -#> 146 A1 34.343434 21.324279770 -#> 147 A1 35.353535 21.335243623 -#> 148 A1 36.363636 21.330931858 -#> 149 A1 37.373737 21.312298151 -#> 150 A1 38.383838 21.280246621 -#> 151 A1 39.393939 21.235634295 -#> 152 A1 40.404040 21.179273450 -#> 153 A1 41.414141 21.111933845 -#> 154 A1 42.424242 21.034344838 -#> 155 A1 43.434343 20.947197407 -#> 156 A1 44.444444 20.851146060 -#> 157 A1 45.454545 20.746810660 -#> 158 A1 46.464646 20.634778158 -#> 159 A1 47.474747 20.515604239 -#> 160 A1 48.484848 20.389814887 -#> 161 A1 49.494949 20.257907875 -#> 162 A1 50.505051 20.120354180 -#> 163 A1 51.515152 19.977599327 -#> 164 A1 52.525253 19.830064674 -#> 165 A1 53.535354 19.678148618 -#> 166 A1 54.545455 19.522227762 -#> 167 A1 55.555556 19.362658007 -#> 168 A1 56.565657 19.199775600 -#> 169 A1 57.575758 19.033898126 -#> 170 A1 58.585859 18.865325451 -#> 171 A1 59.595960 18.694340625 -#> 172 A1 60.606061 18.521210729 -#> 173 A1 61.616162 18.346187688 -#> 174 A1 62.000000 18.279232408 -#> 175 A1 62.626263 18.169509043 -#> 176 A1 63.636364 17.991398686 -#> 177 A1 64.646465 17.812067549 -#> 178 A1 65.656566 17.631714275 -#> 179 A1 66.666667 17.450525840 -#> 180 A1 67.676768 17.268678156 -#> 181 A1 68.686869 17.086336636 -#> 182 A1 69.696970 16.903656738 -#> 183 A1 70.707071 16.720784474 -#> 184 A1 71.717172 16.537856901 -#> 185 A1 72.727273 16.355002582 -#> 186 A1 73.737374 16.172342031 -#> 187 A1 74.747475 15.989988127 -#> 188 A1 75.757576 15.808046514 -#> 189 A1 76.767677 15.626615980 -#> 190 A1 77.777778 15.445788814 -#> 191 A1 78.787879 15.265651148 -#> 192 A1 79.797980 15.086283284 -#> 193 A1 80.808081 14.907759996 -#> 194 A1 81.818182 14.730150830 -#> 195 A1 82.828283 14.553520376 -#> 196 A1 83.838384 14.377928535 -#> 197 A1 84.848485 14.203430771 -#> 198 A1 85.858586 14.030078345 -#> 199 A1 86.868687 13.857918547 -#> 200 A1 87.878788 13.686994907 -#> 201 A1 88.888889 13.517347398 -#> 202 A1 89.898990 13.349012635 -#> 203 A1 90.909091 13.182024056 -#> 204 A1 91.919192 13.016412097 -#> 205 A1 92.929293 12.852204356 -#> 206 A1 93.939394 12.689425755 -#> 207 A1 94.949495 12.528098688 -#> 208 A1 95.959596 12.368243159 -#> 209 A1 96.969697 12.209876925 -#> 210 A1 97.979798 12.053015616 -#> 211 A1 98.989899 11.897672861 -#> 212 A1 100.000000 11.743860400 -#> 213 B1 0.000000 0.000000000 -#> 214 B1 1.000000 0.862762059 -#> 215 B1 1.010101 0.871177048 -#> 216 B1 2.020202 1.683497848 -#> 217 B1 3.000000 2.418226457 -#> 218 B1 3.030303 2.440145075 -#> 219 B1 4.040404 3.144139999 -#> 220 B1 5.050505 3.798350490 -#> 221 B1 6.060606 4.405498633 -#> 222 B1 7.000000 4.930176837 -#> 223 B1 7.070707 4.968167964 -#> 224 B1 8.080808 5.488810347 -#> 225 B1 9.090909 5.969752521 -#> 226 B1 10.101010 6.413202316 -#> 227 B1 11.111111 6.821254568 -#> 228 B1 12.121212 7.195896744 -#> 229 B1 13.131313 7.539014282 -#> 230 B1 14.000000 7.810248132 -#> 231 B1 14.141414 7.852395679 -#> 232 B1 15.151515 8.137737320 -#> 233 B1 16.161616 8.396648072 -#> 234 B1 17.171717 8.630653651 -#> 235 B1 18.181818 8.841200774 -#> 236 B1 19.191919 9.029661109 -#> 237 B1 20.202020 9.197335022 -#> 238 B1 21.212121 9.345455150 -#> 239 B1 22.222222 9.475189788 -#> 240 B1 23.232323 9.587646116 -#> 241 B1 24.242424 9.683873262 -#> 242 B1 25.252525 9.764865214 -#> 243 B1 26.262626 9.831563593 -#> 244 B1 27.272727 9.884860284 -#> 245 B1 28.282828 9.925599936 -#> 246 B1 29.292929 9.954582344 -#> 247 B1 30.000000 9.968281596 -#> 248 B1 30.303030 9.972564708 -#> 249 B1 31.313131 9.980263783 -#> 250 B1 32.323232 9.978357919 -#> 251 B1 33.333333 9.967489009 -#> 252 B1 34.343434 9.948264327 -#> 253 B1 35.353535 9.921258285 -#> 254 B1 36.363636 9.887014102 -#> 255 B1 37.373737 9.846045383 -#> 256 B1 38.383838 9.798837632 -#> 257 B1 39.393939 9.745849674 -#> 258 B1 40.404040 9.687515023 -#> 259 B1 41.414141 9.624243169 -#> 260 B1 42.424242 9.556420809 -#> 261 B1 43.434343 9.484413012 -#> 262 B1 44.444444 9.408564328 -#> 263 B1 45.454545 9.329199843 -#> 264 B1 46.464646 9.246626179 -#> 265 B1 47.474747 9.161132446 -#> 266 B1 48.484848 9.072991146 -#> 267 B1 49.494949 8.982459028 -#> 268 B1 50.505051 8.889777910 -#> 269 B1 51.515152 8.795175451 -#> 270 B1 52.525253 8.698865886 -#> 271 B1 53.535354 8.601050726 -#> 272 B1 54.545455 8.501919425 -#> 273 B1 55.555556 8.401650008 -#> 274 B1 56.565657 8.300409672 -#> 275 B1 57.575758 8.198355355 -#> 276 B1 58.585859 8.095634277 -#> 277 B1 59.595960 7.992384454 -#> 278 B1 60.606061 7.888735183 -#> 279 B1 61.616162 7.784807509 -#> 280 B1 62.000000 7.745265792 -#> 281 B1 62.626263 7.680714664 -#> 282 B1 63.636364 7.576562482 -#> 283 B1 64.646465 7.472449799 -#> 284 B1 65.656566 7.368468826 -#> 285 B1 66.666667 7.264705509 -#> 286 B1 67.676768 7.161239868 -#> 287 B1 68.686869 7.058146319 -#> 288 B1 69.696970 6.955493978 -#> 289 B1 70.707071 6.853346953 -#> 290 B1 71.717172 6.751764620 -#> 291 B1 72.727273 6.650801882 -#> 292 B1 73.737374 6.550509419 -#> 293 B1 74.747475 6.450933922 -#> 294 B1 75.757576 6.352118318 -#> 295 B1 76.767677 6.254101979 -#> 296 B1 77.777778 6.156920928 -#> 297 B1 78.787879 6.060608023 -#> 298 B1 79.797980 5.965193142 -#> 299 B1 80.808081 5.870703355 -#> 300 B1 81.818182 5.777163083 -#> 301 B1 82.828283 5.684594257 -#> 302 B1 83.838384 5.593016458 -#> 303 B1 84.848485 5.502447062 -#> 304 B1 85.858586 5.412901366 -#> 305 B1 86.868687 5.324392718 -#> 306 B1 87.878788 5.236932630 -#> 307 B1 88.888889 5.150530889 -#> 308 B1 89.898990 5.065195670 -#> 309 B1 90.909091 4.980933628 -#> 310 B1 91.919192 4.897749999 -#> 311 B1 92.929293 4.815648688 -#> 312 B1 93.939394 4.734632351 -#> 313 B1 94.949495 4.654702481 -#> 314 B1 95.959596 4.575859481 -#> 315 B1 96.969697 4.498102737 -#> 316 B1 97.979798 4.421430686 -#> 317 B1 98.989899 4.345840882 -#> 318 B1 100.000000 4.271330056 -#> 319 C1 0.000000 0.000000000 -#> 320 C1 1.000000 1.829645786 -#> 321 C1 1.010101 1.847087763 -#> 322 C1 2.020202 3.492133303 -#> 323 C1 3.000000 4.910531064 -#> 324 C1 3.030303 4.951772742 -#> 325 C1 4.040404 6.241420142 -#> 326 C1 5.050505 7.375351980 -#> 327 C1 6.060606 8.366785999 -#> 328 C1 7.000000 9.171671206 -#> 329 C1 7.070707 9.227954769 -#> 330 C1 8.080808 9.970174354 -#> 331 C1 9.090909 10.603908370 -#> 332 C1 10.101010 11.138827767 -#> 333 C1 11.111111 11.583866567 -#> 334 C1 12.121212 11.947273869 -#> 335 C1 13.131313 12.236662337 -#> 336 C1 14.000000 12.431704739 -#> 337 C1 14.141414 12.459053419 -#> 338 C1 15.151515 12.620919488 -#> 339 C1 16.161616 12.728223141 -#> 340 C1 17.171717 12.786453805 -#> 341 C1 18.181818 12.800661859 -#> 342 C1 19.191919 12.775490422 -#> 343 C1 20.202020 12.715204956 -#> 344 C1 21.212121 12.623720845 -#> 345 C1 22.222222 12.504629065 -#> 346 C1 23.232323 12.361220091 -#> 347 C1 24.242424 12.196506142 -#> 348 C1 25.252525 12.013241882 -#> 349 C1 26.262626 11.813943686 -#> 350 C1 27.272727 11.600907551 -#> 351 C1 28.282828 11.376225763 -#> 352 C1 29.292929 11.141802382 -#> 353 C1 30.000000 10.972842888 -#> 354 C1 30.303030 10.899367648 -#> 355 C1 31.313131 10.650491354 -#> 356 C1 32.323232 10.396595286 -#> 357 C1 33.333333 10.138964763 -#> 358 C1 34.343434 9.878759358 -#> 359 C1 35.353535 9.617022857 -#> 360 C1 36.363636 9.354692485 -#> 361 C1 37.373737 9.092607481 -#> 362 C1 38.383838 8.831517041 -#> 363 C1 39.393939 8.572087685 -#> 364 C1 40.404040 8.314910084 -#> 365 C1 41.414141 8.060505385 -#> 366 C1 42.424242 7.809331068 -#> 367 C1 43.434343 7.561786371 -#> 368 C1 44.444444 7.318217302 -#> 369 C1 45.454545 7.078921287 -#> 370 C1 46.464646 6.844151456 -#> 371 C1 47.474747 6.614120611 -#> 372 C1 48.484848 6.389004885 -#> 373 C1 49.494949 6.168947129 -#> 374 C1 50.505051 5.954060026 -#> 375 C1 51.515152 5.744428970 -#> 376 C1 52.525253 5.540114721 -#> 377 C1 53.535354 5.341155842 -#> 378 C1 54.545455 5.147570951 -#> 379 C1 55.555556 4.959360784 -#> 380 C1 56.565657 4.776510102 -#> 381 C1 57.575758 4.598989433 -#> 382 C1 58.585859 4.426756673 -#> 383 C1 59.595960 4.259758556 -#> 384 C1 60.606061 4.097932000 -#> 385 C1 61.616162 3.941205338 -#> 386 C1 62.000000 3.882970158 -#> 387 C1 62.626263 3.789499444 -#> 388 C1 63.636364 3.642728760 -#> 389 C1 64.646465 3.500802233 -#> 390 C1 65.656566 3.363624171 -#> 391 C1 66.666667 3.231095021 -#> 392 C1 67.676768 3.103112069 -#> 393 C1 68.686869 2.979570086 -#> 394 C1 69.696970 2.860361903 -#> 395 C1 70.707071 2.745378939 -#> 396 C1 71.717172 2.634511667 -#> 397 C1 72.727273 2.527650041 -#> 398 C1 73.737374 2.424683880 -#> 399 C1 74.747475 2.325503203 -#> 400 C1 75.757576 2.229998536 -#> 401 C1 76.767677 2.138061182 -#> 402 C1 77.777778 2.049583458 -#> 403 C1 78.787879 1.964458908 -#> 404 C1 79.797980 1.882582485 -#> 405 C1 80.808081 1.803850715 -#> 406 C1 81.818182 1.728161832 -#> 407 C1 82.828283 1.655415900 -#> 408 C1 83.838384 1.585514911 -#> 409 C1 84.848485 1.518362874 -#> 410 C1 85.858586 1.453865880 -#> 411 C1 86.868687 1.391932162 -#> 412 C1 87.878788 1.332472134 -#> 413 C1 88.888889 1.275398429 -#> 414 C1 89.898990 1.220625918 -#> 415 C1 90.909091 1.168071723 -#> 416 C1 91.919192 1.117655227 -#> 417 C1 92.929293 1.069298066 -#> 418 C1 93.939394 1.022924125 -#> 419 C1 94.949495 0.978459525 -#> 420 C1 95.959596 0.935832597 -#> 421 C1 96.969697 0.894973866 -#> 422 C1 97.979798 0.855816021 -#> 423 C1 98.989899 0.818293881 -#> 424 C1 100.000000 0.782344364 -#> 425 A2 0.000000 0.000000000 -#> 426 A2 1.000000 0.005272357 -#> 427 A2 1.010101 0.005377817 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2.020977853 -#> 459 A2 30.000000 2.078585030 -#> 460 A2 30.303030 2.103068270 -#> 461 A2 31.313131 2.183746011 -#> 462 A2 32.323232 2.262919231 -#> 463 A2 33.333333 2.340505852 -#> 464 A2 34.343434 2.416432940 -#> 465 A2 35.353535 2.490636111 -#> 466 A2 36.363636 2.563058979 -#> 467 A2 37.373737 2.633652622 -#> 468 A2 38.383838 2.702375089 -#> 469 A2 39.393939 2.769190926 -#> 470 A2 40.404040 2.834070737 -#> 471 A2 41.414141 2.896990764 -#> 472 A2 42.424242 2.957932489 -#> 473 A2 43.434343 3.016882265 -#> 474 A2 44.444444 3.073830964 -#> 475 A2 45.454545 3.128773647 -#> 476 A2 46.464646 3.181709250 -#> 477 A2 47.474747 3.232640290 -#> 478 A2 48.484848 3.281572591 -#> 479 A2 49.494949 3.328515022 -#> 480 A2 50.505051 3.373479253 -#> 481 A2 51.515152 3.416479521 -#> 482 A2 52.525253 3.457532417 -#> 483 A2 53.535354 3.496656681 -#> 484 A2 54.545455 3.533873012 -#> 485 A2 55.555556 3.569203883 -#> 486 A2 56.565657 3.602673379 -#> 487 A2 57.575758 3.634307034 -#> 488 A2 58.585859 3.664131686 -#> 489 A2 59.595960 3.692175334 -#> 490 A2 60.606061 3.718467012 -#> 491 A2 61.616162 3.743036663 -#> 492 A2 62.000000 3.751927986 -#> 493 A2 62.626263 3.765915028 -#> 494 A2 63.636364 3.787133539 -#> 495 A2 64.646465 3.806724217 -#> 496 A2 65.656566 3.824719582 -#> 497 A2 66.666667 3.841152565 -#> 498 A2 67.676768 3.856056426 -#> 499 A2 68.686869 3.869464684 -#> 500 A2 69.696970 3.881411040 -#> 501 A2 70.707071 3.891929316 -#> 502 A2 71.717172 3.901053396 -#> 503 A2 72.727273 3.908817168 -#> 504 A2 73.737374 3.915254472 -#> 505 A2 74.747475 3.920399054 -#> 506 A2 75.757576 3.924284521 -#> 507 A2 76.767677 3.926944303 -#> 508 A2 77.777778 3.928411610 -#> 509 A2 78.787879 3.928719404 -#> 510 A2 79.797980 3.927900364 -#> 511 A2 80.808081 3.925986861 -#> 512 A2 81.818182 3.923010926 -#> 513 A2 82.828283 3.919004234 -#> 514 A2 83.838384 3.913998077 -#> 515 A2 84.848485 3.908023347 -#> 516 A2 85.858586 3.901110518 -#> 517 A2 86.868687 3.893289633 -#> 518 A2 87.878788 3.884590288 -#> 519 A2 88.888889 3.875041619 -#> 520 A2 89.898990 3.864672297 -#> 521 A2 90.909091 3.853510511 -#> 522 A2 91.919192 3.841583970 -#> 523 A2 92.929293 3.828919886 -#> 524 A2 93.939394 3.815544978 -#> 525 A2 94.949495 3.801485462 -#> 526 A2 95.959596 3.786767051 -#> 527 A2 96.969697 3.771414951 -#> 528 A2 97.979798 3.755453860 -#> 529 A2 98.989899 3.738907968 -#> 530 A2 100.000000 3.721800959 -#> -#> $cost -#> function (P) -#> { -#> assign("calls", calls + 1, inherits = TRUE) -#> if (trace_parms) -#> cat(P, "\n") -#> if (length(state.ini.optim) > 0) { -#> odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed) -#> names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames) -#> } -#> else { -#> odeini <- state.ini.fixed -#> names(odeini) <- state.ini.fixed.boxnames -#> } -#> odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], -#> transparms.fixed) -#> parms <- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates, -#> transform_fractions = transform_fractions) -#> out <- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, -#> use_compiled = use_compiled, method.ode = method.ode, -#> atol = atol, rtol = rtol, ...) -#> assign("out_predicted", out, inherits = TRUE) -#> mC <- modCost(out, observed, y = "value", err = err, weight = weight, -#> scaleVar = scaleVar) -#> if (mC$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) -#> } -#> <environment: 0x36a83b0> -#> -#> $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) -#> } -#> <environment: 0x36a83b0> -#> -#> $hessian_notrans -#> parent_0 k_parent k_A1 k_B1 -#> parent_0 7.365081 -1854.5113 -7.186039e+02 -1.307858e+02 -#> k_parent -1854.511330 2686790.7676 -6.235542e+04 -1.429363e+04 -#> k_A1 -718.603865 -62355.4211 3.128242e+06 0.000000e+00 -#> k_B1 -130.785796 -14293.6348 0.000000e+00 4.545506e+05 -#> k_C1 -76.404274 -38575.9391 1.190516e-02 -9.422820e-04 -#> k_A2 -17.933942 -4390.5079 -5.838973e+04 0.000000e+00 -#> f_parent_to_A1 75.150866 43257.2599 -1.733841e+05 0.000000e+00 -#> f_parent_to_B1 29.265575 17940.1132 0.000000e+00 -6.150198e+04 -#> f_parent_to_C1 20.661354 19692.5582 -6.146186e-05 -1.990817e-03 -#> f_A1_to_A2 1.593279 597.0744 5.849840e+03 0.000000e+00 -#> k_C1 k_A2 f_parent_to_A1 f_parent_to_B1 -#> parent_0 -7.640427e+01 -1.793394e+01 7.515087e+01 2.926558e+01 -#> k_parent -3.857594e+04 -4.390508e+03 4.325726e+04 1.794011e+04 -#> k_A1 1.190516e-02 -5.838973e+04 -1.733841e+05 0.000000e+00 -#> k_B1 -9.422820e-04 0.000000e+00 0.000000e+00 -6.150198e+04 -#> k_C1 8.855106e+04 4.105787e-04 -1.354551e-03 5.852620e-04 -#> k_A2 4.105787e-04 4.649850e+04 -4.327086e+03 0.000000e+00 -#> f_parent_to_A1 -1.354551e-03 -4.327086e+03 1.813234e+04 0.000000e+00 -#> f_parent_to_B1 5.852620e-04 0.000000e+00 0.000000e+00 1.376213e+04 -#> f_parent_to_C1 -1.658031e+04 2.903794e-04 1.946385e-03 1.325258e-03 -#> f_A1_to_A2 -4.367402e-05 -3.679910e+03 3.844249e+02 0.000000e+00 -#> f_parent_to_C1 f_A1_to_A2 -#> parent_0 2.066135e+01 1.593279e+00 -#> k_parent 1.969256e+04 5.970744e+02 -#> k_A1 -6.146186e-05 5.849840e+03 -#> k_B1 -1.990817e-03 0.000000e+00 -#> k_C1 -1.658031e+04 -4.367402e-05 -#> k_A2 2.903794e-04 -3.679910e+03 -#> f_parent_to_A1 1.946385e-03 3.844249e+02 -#> f_parent_to_B1 1.325258e-03 0.000000e+00 -#> f_parent_to_C1 4.483759e+03 -3.796730e-05 -#> f_A1_to_A2 -3.796730e-05 3.269288e+02 -#> -#> $start -#> value type -#> parent_0 93.2000000 state -#> k_parent 0.1000000 deparm -#> k_A1 0.1001000 deparm -#> k_B1 0.1002000 deparm -#> k_C1 0.1003000 deparm -#> k_A2 0.1004000 deparm -#> f_parent_to_A1 0.3333333 deparm -#> f_parent_to_B1 0.3333333 deparm -#> f_parent_to_C1 0.3333333 deparm -#> f_A1_to_A2 0.5000000 deparm -#> -#> $start_transformed -#> value lower upper -#> parent_0 93.200000 -Inf Inf -#> log_k_parent -2.302585 -Inf Inf -#> log_k_A1 -2.301586 -Inf Inf -#> log_k_B1 -2.300587 -Inf Inf -#> log_k_C1 -2.299590 -Inf Inf -#> log_k_A2 -2.298593 -Inf Inf -#> f_parent_ilr_1 0.000000 -Inf Inf -#> f_parent_ilr_2 0.000000 -Inf Inf -#> f_A1_ilr_1 0.000000 -Inf Inf -#> -#> $fixed -#> value type -#> A1_0 0 state -#> B1_0 0 state -#> C1_0 0 state -#> A2_0 0 state -#> -#> $data -#> time variable observed predicted residual -#> 1 0 parent 93.20 91.918159794 1.2818402 -#> 2 1 parent 89.40 87.462788491 1.9372115 -#> 3 3 parent 79.70 79.189448055 0.5105519 -#> 4 7 parent 61.10 64.916531757 -3.8165318 -#> 5 14 parent 48.20 45.846828365 2.3531716 -#> 6 30 parent 15.90 20.704334210 -4.8043342 -#> 7 62 parent 6.50 4.222456793 2.2775432 -#> 8 100 parent 6.00 0.639147580 5.3608524 -#> 9 0 A1 NA 0.000000000 NA -#> 10 1 A1 NA 1.685461006 NA -#> 11 3 A1 0.55 4.746752202 -4.1967522 -#> 12 7 A1 6.87 9.773298725 -2.9032987 -#> 13 14 A1 17.08 15.767512526 1.3124875 -#> 14 30 A1 21.68 21.077890710 0.6021093 -#> 15 62 A1 15.77 18.279232408 -2.5092324 -#> 16 100 A1 13.63 11.743860400 1.8861396 -#> 17 0 B1 NA 0.000000000 NA -#> 18 1 B1 NA 0.862762059 NA -#> 19 3 B1 NA 2.418226457 NA -#> 20 7 B1 0.55 4.930176837 -4.3801768 -#> 21 14 B1 2.31 7.810248132 -5.5002481 -#> 22 30 B1 15.76 9.968281596 5.7917184 -#> 23 62 B1 6.36 7.745265792 -1.3852658 -#> 24 100 B1 3.74 4.271330056 -0.5313301 -#> 25 0 C1 NA 0.000000000 NA -#> 26 1 C1 0.55 1.829645786 -1.2796458 -#> 27 3 C1 3.20 4.910531064 -1.7105311 -#> 28 7 C1 5.46 9.171671206 -3.7116712 -#> 29 14 C1 12.55 12.431704739 0.1182953 -#> 30 30 C1 10.45 10.972842888 -0.5228429 -#> 31 62 C1 4.74 3.882970158 0.8570298 -#> 32 100 C1 4.33 0.782344364 3.5476556 -#> 33 0 A2 NA 0.000000000 NA -#> 34 1 A2 0.55 0.005272357 0.5447276 -#> 35 3 A2 1.41 0.044759575 1.3652404 -#> 36 7 A2 0.55 0.216973934 0.3330261 -#> 37 14 A2 1.29 0.709794102 0.5802059 -#> 38 30 A2 1.95 2.078585030 -0.1285850 -#> 39 62 A2 3.54 3.751927986 -0.2119280 -#> 40 100 A2 3.86 3.721800959 0.1381990 -#> -#> $atol -#> [1] 1e-08 -#> -#> $rtol -#> [1] 1e-10 -#> -#> $weight.ini -#> [1] "none" -#> -#> $reweight.tol -#> [1] 1e-08 -#> -#> $reweight.max.iter -#> [1] 10 -#> -#> $bparms.optim -#> parent_0 k_parent k_A1 k_B1 k_C1 -#> 91.91815979 0.04968519 0.01393165 0.01859846 0.06171564 -#> k_A2 f_parent_to_A1 f_parent_to_B1 f_parent_to_C1 f_A1_to_A2 -#> 0.02431549 0.38096192 0.19546676 0.42357132 0.44796066 -#> -#> $bparms.fixed -#> A1_0 B1_0 C1_0 A2_0 -#> 0 0 0 0 -#> -#> $bparms.ode -#> k_parent f_parent_to_A1 f_parent_to_B1 f_parent_to_C1 k_A1 -#> 0.04968519 0.38096192 0.19546676 0.42357132 0.01393165 -#> f_A1_to_A2 k_B1 k_C1 k_A2 -#> 0.44796066 0.01859846 0.06171564 0.02431549 -#> -#> $bparms.state -#> parent A1 B1 C1 A2 -#> 91.91816 0.00000 0.00000 0.00000 0.00000 -#> -#> $date -#> [1] "Fri Nov 18 15:20:45 2016" -#> -#> attr(,"class") -#> [1] "mkinfit" "modFit"
+ A2 = list(type = "SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.
+ fit <- mkinfit(model, data, quiet = TRUE) + plot(fit)
endpoints(fit)
#> $ff +#> parent_A1 parent_B1 parent_C1 parent_sink A1_A2 A1_sink +#> 0.3809619 0.1954668 0.4235713 0.0000000 0.4479607 0.5520393 +#> +#> $SFORB +#> logical(0) +#> +#> $distimes +#> DT50 DT90 +#> parent 13.95078 46.34349 +#> A1 49.75343 165.27732 +#> B1 37.26906 123.80513 +#> C1 11.23131 37.30959 +#> A2 28.50640 94.69621 +#>
+ # Compare with the results obtained in the original publication + print(schaefer07_complex_results)
#> compound parameter KinGUI ModelMaker deviation +#> 1 parent degradation rate 0.0496 0.0506 2.0 +#> 2 parent DT50 13.9900 13.6900 2.2 +#> 3 metabolite A1 formation fraction 0.3803 0.3696 2.9 +#> 4 metabolite A1 degradation rate 0.0139 0.0136 2.2 +#> 5 metabolite A1 DT50 49.9600 50.8900 1.8 +#> 6 metabolite B1 formation fraction 0.1866 0.1818 2.6 +#> 7 metabolite B1 degradation rate 0.0175 0.0172 1.7 +#> 8 metabolite B1 DT50 39.6100 40.2400 1.6 +#> 9 metabolite C1 formation fraction 0.4331 0.4486 3.5 +#> 10 metabolite C1 degradation rate 0.0638 0.0700 8.9 +#> 11 metabolite C1 DT50 10.8700 9.9000 9.8 +#> 12 metabolite A2 formation fraction 0.4529 0.4559 0.7 +#> 13 metabolite A2 degradation rate 0.0245 0.0244 0.4 +#> 14 metabolite A2 DT50 28.2400 28.4500 0.7