"["(x, i, j, ..., drop = FALSE)
mmkin objectSubsetting method for mmkin objects.
mmkin.
# 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 parent parent 1.046192647 -3.322396479 3.655156669 -1.705316770 0.406306255 -0.123734689 -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 0.268 0.000 0.269 $mkinmod <mkinmod> model generated with Use of formation fractions $use_of_ff: min Specification $spec: $parent $type: FOMC $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: 0x2d96b00> $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: 0x2d96b00> $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 $date [1] "Mon Nov 9 10:11:49 2015" attr(,"class") [1] "mkinfit" "modFit"fits["SFO", "B"]dataset model B SFO List,42 attr(,"class") [1] "mmkin"fits[["SFO", "B"]] # This is equivalent to$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 parent parent 0.55407218 -2.98452128 4.20445742 -1.68599939 -0.58185357 -0.72033730 -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 0.112 0.000 0.112 $mkinmod <mkinmod> model generated with Use of formation fractions $use_of_ff: min Specification $spec: $parent $type: SFO 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: 0x2fa3d28> $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: 0x2fa3d28> $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] "Mon Nov 9 10:11:48 2015" 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 parent parent 0.55407218 -2.98452128 4.20445742 -1.68599939 -0.58185357 -0.72033730 -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 0.112 0.000 0.112 $mkinmod <mkinmod> model generated with Use of formation fractions $use_of_ff: min Specification $spec: $parent $type: SFO 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: 0x2fa3d28> $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: 0x2fa3d28> $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] "Mon Nov 9 10:11:48 2015" attr(,"class") [1] "mkinfit" "modFit"