diff options
Diffstat (limited to 'docs/reference/Extract.mmkin.html')
| -rw-r--r-- | docs/reference/Extract.mmkin.html | 711 |
1 files changed, 23 insertions, 688 deletions
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"</div><div class='input'> <span class='no'>fits</span>[, <span class='st'>"B"</span>, <span class='kw'>drop</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>]$<span class='no'>FOMC</span></div><div class='output co'>#> $par +#> [1] "mmkin"</div><div class='input'> <span class='no'>fits</span>[<span class='st'>"SFO"</span>, <span class='st'>"B"</span>]</div><div class='output co'>#> dataset +#> model B +#> SFO List,42 +#> attr(,"class") +#> [1] "mmkin"</div><div class='input'> + <span class='fu'>head</span>( + <span class='co'># This extracts an mkinfit object with lots of components</span> + <span class='no'>fits</span><span class='kw'>[[</span><span class='st'>"FOMC"</span>, <span class='st'>"B"</span>]] + )</div><div class='output co'>#> <span class='message'></span> +#> <span class='message'>Attaching package: ‘utils’</span></div><div class='output co'>#> <span class='message'>The following objects are masked from ‘devtools_shims’:</span> +#> <span class='message'></span> +#> <span class='message'> ?, help</span></div><div class='output co'>#> <span class='message'>The following object is masked from ‘package:inline’:</span> +#> <span class='message'></span> +#> <span class='message'> package.skeleton</span></div><div class='output co'>#> $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" </div><div class='input'> <span class='no'>fits</span>[<span class='st'>"SFO"</span>, <span class='st'>"B"</span>]</div><div class='output co'>#> dataset -#> model B -#> SFO List,42 -#> attr(,"class") -#> [1] "mmkin"</div><div class='input'> <span class='no'>fits</span><span class='kw'>[[</span><span class='st'>"SFO"</span>, <span class='st'>"B"</span>]] <span class='co'># This is equivalent to</span></div><div class='output co'>#> $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" </div><div class='input'> <span class='no'>fits</span>[<span class='st'>"SFO"</span>, <span class='st'>"B"</span>, <span class='kw'>drop</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>]</div><div class='output co'>#> [[1]] +#> </div><div class='input'> + <span class='fu'>head</span>( + <span class='co'># The same can be achieved by</span> + <span class='no'>fits</span>[<span class='st'>"SFO"</span>, <span class='st'>"B"</span>, <span class='kw'>drop</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>] + )</div><div class='output co'>#> [[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" |