Subsetting method for mmkin objects.

# S3 method for mmkin
[(x, i, j, ..., drop = FALSE)

Arguments

x
An mmkin object
i
Row index selecting the fits for specific models
j
Column index selecting the fits to specific datasets
...
Not used, only there to satisfy the generic method definition
drop
If FALSE, the method always returns an mmkin object, otherwise either a list of mkinfit objects or a single mkinfit object.

Value

An object of class mmkin.

Examples

# Only use one core, to pass R CMD check --as-cran fits <- mmkin(c("SFO", "FOMC"), list(B = FOCUS_2006_B, C = FOCUS_2006_C), cores = 1, quiet = TRUE) fits["FOMC", ]
#> dataset #> model B C #> FOMC List,42 List,42 #> attr(,"class") #> [1] "mmkin" #>
fits[, "B"]
#> dataset #> model B #> SFO List,42 #> FOMC List,42 #> attr(,"class") #> [1] "mmkin" #>
fits[, "B", drop = TRUE]$FOMC
#> $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 #> 0.256 0.000 0.256 #> #> $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: 0x435ac28> #> #> $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: 0x435ac28> #> #> $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 09:39:06 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 #> 0.068 0.000 0.068 #> #> $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: 0x4da08d0> #> #> $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: 0x4da08d0> #> #> $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 09:39:06 2016" #> #> attr(,"class") #> [1] "mkinfit" "modFit" #>
fits["SFO", "B", drop = TRUE]
#> [[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 #> 0.068 0.000 0.068 #> #> $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: 0x4da08d0> #> #> $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: 0x4da08d0> #> #> $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 09:39:06 2016" #> #> attr(,"class") #> [1] "mkinfit" "modFit" #> #>

Author

Johannes Ranke