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<h1>Subsetting method for mmkin objects</h1>
</div>
<div class="row">
<div class="col-md-9">
<p>Subsetting method for mmkin objects.</p>
<pre># S3 method for mmkin
[(x, i, j, ..., drop = FALSE)</pre>
<h2>Arguments</h2>
<dl class="dl-horizontal">
<dt>x</dt>
<dd>An <code>mmkin object</code></dd>
<dt>i</dt>
<dd>Row index selecting the fits for specific models</dd>
<dt>j</dt>
<dd>Column index selecting the fits to specific datasets</dd>
<dt>...</dt>
<dd>Not used, only there to satisfy the generic method definition</dd>
<dt>drop</dt>
<dd>If FALSE, the method always returns an mmkin object, otherwise either
a list of mkinfit objects or a single mkinfit object.</dd>
</dl>
<div class="Value">
<h2>Value</h2>
<p>An object of class <code>mmkin</code>.</p>
</div>
<h2 id="examples">Examples</h2>
<pre class="examples"><div class='input'> <span class='co'># Only use one core, to pass R CMD check --as-cran</span>
<span class='no'>fits</span> <span class='kw'><-</span> <span class='fu'>mmkin</span>(<span class='fu'>c</span>(<span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span>), <span class='fu'>list</span>(<span class='kw'>B</span> <span class='kw'>=</span> <span class='no'>FOCUS_2006_B</span>, <span class='kw'>C</span> <span class='kw'>=</span> <span class='no'>FOCUS_2006_C</span>),
<span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
<span class='no'>fits</span>[<span class='st'>"FOMC"</span>, ]</div><div class='output co'>#> dataset
#> model B C
#> FOMC List,42 List,42
#> attr(,"class")
#> [1] "mmkin"
#> </div><div class='input'> <span class='no'>fits</span>[, <span class='st'>"B"</span>]</div><div class='output co'>#> dataset
#> model B
#> 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
#> parent_0 log_alpha log_beta
#> 99.666193 2.549849 5.050586
#>
#> $ssr
#> [1] 28.58291
#>
#> $convergence
#> [1] 0
#>
#> $iterations
#> [1] 21
#>
#> $evaluations
#> function gradient
#> 25 78
#>
#> $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
#> 0.252 0.000 0.253
#>
#> $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: 0x3e9c368>
#>
#> $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: 0x3e9c368>
#>
#> $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] "Thu Oct 6 10:47:28 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 0.067
#>
#> $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: 0x3c1a660>
#>
#> $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: 0x3c1a660>
#>
#> $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] "Thu Oct 6 10:47:28 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]]
#> $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 0.067
#>
#> $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: 0x3c1a660>
#>
#> $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: 0x3c1a660>
#>
#> $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] "Thu Oct 6 10:47:28 2016"
#>
#> attr(,"class")
#> [1] "mkinfit" "modFit"
#>
#> </div></pre>
</div>
<div class="col-md-3">
<h2>Author</h2>
Johannes Ranke
</div>
</div>
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