The 12 datasets were generated using four different models and three different variance components. The four models are either the SFO or the DFOP model with either two sequential or two parallel metabolites.
Variance component 'a' is based on a normal distribution with standard deviation of 3, Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7. Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07 for the increase of the standard deviation with y.
Initial concentrations for metabolites and all values where adding the variance component resulted
in a value below the assumed limit of detection of 0.1 were set to NA.
As an example, the first dataset has the title SFO_lin_a and is based on the SFO model
with two sequential metabolites (linear pathway), with added variance component 'a'.
Compare also the code in the example section to see the degradation models.
synthetic_data_for_UBA_2014
A list containing datasets in the form internally used by the 'gmkin' package. The list has twelfe components. Each of the components is one dataset that has, among others, the following components
titleSFO_lin_adatamkinfitRanke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452
Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for measurement error in analytical chemistry. Technometrics 37(2), 176-184.
m_synth_SFO_lin <- mkinmod(parent = list(type = "SFO", to = "M1"), M1 = list(type = "SFO", to = "M2"), M2 = list(type = "SFO"), use_of_ff = "max")#>m_synth_SFO_par <- mkinmod(parent = list(type = "SFO", to = c("M1", "M2"), sink = FALSE), M1 = list(type = "SFO"), M2 = list(type = "SFO"), use_of_ff = "max")#>m_synth_DFOP_lin <- mkinmod(parent = list(type = "DFOP", to = "M1"), M1 = list(type = "SFO", to = "M2"), M2 = list(type = "SFO"), use_of_ff = "max")#>m_synth_DFOP_par <- mkinmod(parent = list(type = "DFOP", to = c("M1", "M2"), sink = FALSE), M1 = list(type = "SFO"), M2 = list(type = "SFO"), use_of_ff = "max")#>#> Model cost at call 1 : 31054.59 #> Model cost at call 3 : 31054.59 #> Model cost at call 8 : 15089.57 #> Model cost at call 9 : 11464.3 #> Model cost at call 11 : 11464.1 #> Model cost at call 16 : 5723.32 #> Model cost at call 17 : 5723.318 #> Model cost at call 19 : 5723.304 #> Model cost at call 21 : 5723.304 #> Model cost at call 24 : 3968.126 #> Model cost at call 25 : 3968.124 #> Model cost at call 28 : 3968.119 #> Model cost at call 31 : 3416.421 #> Model cost at call 32 : 3416.42 #> Model cost at call 36 : 3416.418 #> Model cost at call 38 : 866.5564 #> Model cost at call 42 : 866.5557 #> Model cost at call 45 : 670.4833 #> Model cost at call 47 : 670.476 #> Model cost at call 53 : 312.9905 #> Model cost at call 57 : 312.9904 #> Model cost at call 58 : 312.9904 #> Model cost at call 61 : 287.8916 #> Model cost at call 63 : 287.8916 #> Model cost at call 66 : 287.8916 #> Model cost at call 69 : 284.5441 #> Model cost at call 71 : 284.5441 #> Model cost at call 73 : 284.5441 #> Model cost at call 76 : 283.4533 #> Model cost at call 78 : 283.4533 #> Model cost at call 83 : 282.1356 #> Model cost at call 85 : 282.1356 #> Model cost at call 88 : 282.1356 #> Model cost at call 90 : 280.7846 #> Model cost at call 92 : 280.7846 #> Model cost at call 95 : 280.7846 #> Model cost at call 97 : 278.4856 #> Model cost at call 98 : 274.5025 #> Model cost at call 99 : 269.2866 #> Model cost at call 101 : 269.2866 #> Model cost at call 102 : 269.2866 #> Model cost at call 103 : 269.2866 #> Model cost at call 106 : 254.1284 #> Model cost at call 108 : 254.1283 #> Model cost at call 109 : 254.1283 #> Model cost at call 112 : 254.128 #> Model cost at call 114 : 233.1376 #> Model cost at call 116 : 233.1376 #> Model cost at call 118 : 233.1375 #> Model cost at call 121 : 227.5879 #> Model cost at call 124 : 227.5879 #> Model cost at call 125 : 227.5878 #> Model cost at call 129 : 217.0041 #> Model cost at call 133 : 217.0041 #> Model cost at call 135 : 217.0041 #> Model cost at call 136 : 215.1367 #> Model cost at call 138 : 215.1367 #> Model cost at call 143 : 213.3794 #> Model cost at call 145 : 213.3794 #> Model cost at call 150 : 211.0201 #> Model cost at call 152 : 211.0201 #> Model cost at call 154 : 211.0201 #> Model cost at call 155 : 211.0201 #> Model cost at call 157 : 210.6426 #> Model cost at call 159 : 210.6426 #> Model cost at call 160 : 210.6425 #> Model cost at call 164 : 207.6331 #> Model cost at call 167 : 207.6331 #> Model cost at call 171 : 206.2366 #> Model cost at call 173 : 206.2366 #> Model cost at call 174 : 206.2366 #> Model cost at call 178 : 204.8117 #> Model cost at call 180 : 204.8117 #> Model cost at call 185 : 204.7988 #> Model cost at call 187 : 204.7988 #> Model cost at call 190 : 204.7988 #> Model cost at call 192 : 203.5122 #> Model cost at call 194 : 203.5122 #> Model cost at call 197 : 203.5122 #> Model cost at call 198 : 203.5122 #> Model cost at call 199 : 203.354 #> Model cost at call 201 : 203.354 #> Model cost at call 204 : 203.354 #> Model cost at call 206 : 202.6825 #> Model cost at call 208 : 202.6825 #> Model cost at call 209 : 202.6825 #> Model cost at call 212 : 202.6825 #> Model cost at call 213 : 202.4582 #> Model cost at call 215 : 202.4582 #> Model cost at call 220 : 202.3261 #> Model cost at call 222 : 202.3261 #> Model cost at call 227 : 202.2306 #> Model cost at call 229 : 202.2306 #> Model cost at call 231 : 202.2306 #> Model cost at call 234 : 202.115 #> Model cost at call 236 : 202.115 #> Model cost at call 238 : 202.115 #> Model cost at call 241 : 202.0397 #> Model cost at call 243 : 202.0397 #> Model cost at call 248 : 201.8989 #> Model cost at call 249 : 201.8551 #> Model cost at call 252 : 201.8551 #> Model cost at call 257 : 201.676 #> Model cost at call 259 : 201.676 #> Model cost at call 264 : 201.6285 #> Model cost at call 266 : 201.6285 #> Model cost at call 270 : 201.6284 #> Model cost at call 271 : 201.5876 #> Model cost at call 272 : 201.5876 #> Model cost at call 278 : 201.5317 #> Model cost at call 279 : 201.5317 #> Model cost at call 286 : 201.5207 #> Model cost at call 287 : 201.5207 #> Model cost at call 289 : 201.5207 #> Model cost at call 293 : 201.5207 #> Model cost at call 294 : 201.5207 #> Model cost at call 296 : 201.5174 #> Model cost at call 301 : 201.5174 #> Model cost at call 304 : 201.5169 #> Model cost at call 305 : 201.5169 #> Model cost at call 306 : 201.5169 #> Model cost at call 309 : 201.5169 #> Model cost at call 312 : 201.5169 #> Model cost at call 314 : 201.5169 #> Model cost at call 322 : 201.5169 #> Model cost at call 325 : 201.5169 #> Model cost at call 340 : 201.5169 #> Optimisation by method Port successfully terminated.#> $par #> parent_0 log_k_parent log_k_M1 log_k_M2 f_parent_ilr_1 #> 102.0624835 -0.3020316 -1.2067882 -3.9007519 0.8491684 #> f_M1_ilr_1 #> 0.6780411 #> #> $ssr #> [1] 201.5169 #> #> $convergence #> [1] 0 #> #> $iterations #> [1] 43 #> #> $evaluations #> function gradient #> 56 281 #> #> $counts #> [1] "relative convergence (4)" #> #> $hessian #> parent_0 log_k_parent log_k_M1 log_k_M2 f_parent_ilr_1 #> parent_0 8.433594 -29.66715 -18.40708 -68.90161 115.9976 #> log_k_parent -29.667146 10561.33531 675.33998 55.94284 1666.8940 #> log_k_M1 -18.407082 675.33998 6274.11801 44.01714 -614.5674 #> log_k_M2 -68.901614 55.94284 44.01714 5021.66991 -2300.4467 #> f_parent_ilr_1 115.997604 1666.89403 -614.56735 -2300.44667 3872.8569 #> f_M1_ilr_1 92.819176 604.06870 1483.45826 -2755.79082 3098.9947 #> f_M1_ilr_1 #> parent_0 92.81918 #> log_k_parent 604.06870 #> log_k_M1 1483.45826 #> log_k_M2 -2755.79082 #> f_parent_ilr_1 3098.99466 #> f_M1_ilr_1 3712.39824 #> #> $residuals #> parent parent parent parent parent parent #> 0.56248353 0.86248353 -5.17118695 1.22881305 0.70772795 3.50772795 #> parent parent parent parent parent parent #> -0.52282962 0.27717038 -3.49673606 -3.19999990 -0.60000000 -3.50000000 #> M1 M1 M1 M1 M1 M1 #> -1.61088639 -2.61088639 5.07026619 -0.42973381 0.38714436 -2.31285564 #> M1 M1 M1 M1 M1 M1 #> -3.80468869 0.79531131 -0.49999789 -3.20000000 -1.50000000 -0.60000000 #> M2 M2 M2 M2 M2 M2 #> -0.34517017 0.62526794 2.22526794 -0.07941701 -1.17941701 -3.83353798 #> M2 M2 M2 M2 M2 M2 #> 1.26646202 0.87274743 2.47274743 -0.21837410 0.98162590 -0.47130583 #> M2 M2 M2 #> -0.67130583 -4.27893112 2.22106888 #> #> $ms #> [1] 5.1671 #> #> $var_ms #> parent M1 M2 #> 6.461983 5.750942 3.664121 #> #> $var_ms_unscaled #> parent M1 M2 #> 6.461983 5.750942 3.664121 #> #> $var_ms_unweighted #> parent M1 M2 #> 6.461983 5.750942 3.664121 #> #> $rank #> [1] 6 #> #> $df.residual #> [1] 33 #> #> $solution_type #> [1] "deSolve" #> #> $transform_rates #> [1] TRUE #> #> $transform_fractions #> [1] TRUE #> #> $method.modFit #> [1] "Port" #> #> $maxit.modFit #> [1] "auto" #> #> $calls #> [1] 351 #> #> $time #> user system elapsed #> 2.116 0.000 2.113 #> #> $mkinmod #> <mkinmod> model generated with #> Use of formation fractions $use_of_ff: max #> Specification $spec: #> $parent #> $type: SFO; $to: M1; $sink: TRUE #> $M1 #> $type: SFO; $to: M2; $sink: TRUE #> $M2 #> $type: SFO; $sink: TRUE #> Coefficient matrix $coefmat available #> Compiled model $cf available #> #> $observed #> name time value override err #> 1 parent 0 101.5 NA 1 #> 2 parent 0 101.2 NA 1 #> 3 parent 1 53.9 NA 1 #> 4 parent 1 47.5 NA 1 #> 5 parent 3 10.4 NA 1 #> 6 parent 3 7.6 NA 1 #> 7 parent 7 1.1 NA 1 #> 8 parent 7 0.3 NA 1 #> 9 parent 14 NA NA 1 #> 10 parent 14 3.5 NA 1 #> 11 parent 28 NA NA 1 #> 12 parent 28 3.2 NA 1 #> 13 parent 60 NA NA 1 #> 14 parent 60 NA NA 1 #> 15 parent 90 0.6 NA 1 #> 16 parent 90 NA NA 1 #> 17 parent 120 NA NA 1 #> 18 parent 120 3.5 NA 1 #> 19 M1 0 NA NA 1 #> 20 M1 0 NA NA 1 #> 21 M1 1 36.4 NA 1 #> 22 M1 1 37.4 NA 1 #> 23 M1 3 34.3 NA 1 #> 24 M1 3 39.8 NA 1 #> 25 M1 7 15.1 NA 1 #> 26 M1 7 17.8 NA 1 #> 27 M1 14 5.8 NA 1 #> 28 M1 14 1.2 NA 1 #> 29 M1 28 NA NA 1 #> 30 M1 28 NA NA 1 #> 31 M1 60 0.5 NA 1 #> 32 M1 60 NA NA 1 #> 33 M1 90 NA NA 1 #> 34 M1 90 3.2 NA 1 #> 35 M1 120 1.5 NA 1 #> 36 M1 120 0.6 NA 1 #> 37 M2 0 NA NA 1 #> 38 M2 0 NA NA 1 #> 39 M2 1 NA NA 1 #> 40 M2 1 4.8 NA 1 #> 41 M2 3 20.9 NA 1 #> 42 M2 3 19.3 NA 1 #> 43 M2 7 42.0 NA 1 #> 44 M2 7 43.1 NA 1 #> 45 M2 14 49.4 NA 1 #> 46 M2 14 44.3 NA 1 #> 47 M2 28 34.6 NA 1 #> 48 M2 28 33.0 NA 1 #> 49 M2 60 18.8 NA 1 #> 50 M2 60 17.6 NA 1 #> 51 M2 90 10.6 NA 1 #> 52 M2 90 10.8 NA 1 #> 53 M2 120 9.8 NA 1 #> 54 M2 120 3.3 NA 1 #> #> $obs_vars #> [1] "parent" "M1" "M2" #> #> $predicted #> name time value #> 1 parent 0.000000 1.020625e+02 #> 2 parent 1.000000 4.872881e+01 #> 3 parent 1.212121 4.165603e+01 #> 4 parent 2.424242 1.700159e+01 #> 5 parent 3.000000 1.110773e+01 #> 6 parent 3.636364 6.939072e+00 #> 7 parent 4.848485 2.832130e+00 #> 8 parent 6.060606 1.155912e+00 #> 9 parent 7.000000 5.771704e-01 #> 10 parent 7.272727 4.717769e-01 #> 11 parent 8.484848 1.925522e-01 #> 12 parent 9.696970 7.858872e-02 #> 13 parent 10.909091 3.207539e-02 #> 14 parent 12.121212 1.309133e-02 #> 15 parent 13.333333 5.343128e-03 #> 16 parent 14.000000 3.263939e-03 #> 17 parent 14.545455 2.180757e-03 #> 18 parent 15.757576 8.900590e-04 #> 19 parent 16.969697 3.632705e-04 #> 20 parent 18.181818 1.482660e-04 #> 21 parent 19.393939 6.051327e-05 #> 22 parent 20.606061 2.469808e-05 #> 23 parent 21.818182 1.008035e-05 #> 24 parent 23.030303 4.114467e-06 #> 25 parent 24.242424 1.679140e-06 #> 26 parent 25.454545 6.853728e-07 #> 27 parent 26.666667 2.797450e-07 #> 28 parent 27.878788 1.142138e-07 #> 29 parent 28.000000 1.044512e-07 #> 30 parent 29.090909 4.657425e-08 #> 31 parent 30.303030 1.900245e-08 #> 32 parent 31.515152 7.760238e-09 #> 33 parent 32.727273 3.164577e-09 #> 34 parent 33.939394 1.291779e-09 #> 35 parent 35.151515 5.261577e-10 #> 36 parent 36.363636 2.132915e-10 #> 37 parent 37.575758 8.767818e-11 #> 38 parent 38.787879 3.442792e-11 #> 39 parent 40.000000 1.827291e-11 #> 40 parent 41.212121 3.771071e-12 #> 41 parent 42.424242 6.084856e-12 #> 42 parent 43.636364 -3.377858e-12 #> 43 parent 44.848485 5.870338e-12 #> 44 parent 46.060606 -6.263257e-12 #> 45 parent 47.272727 8.743492e-12 #> 46 parent 48.484848 -9.381771e-12 #> 47 parent 49.696970 1.403389e-11 #> 48 parent 50.909091 -3.592528e-11 #> 49 parent 52.121212 -8.487459e-11 #> 50 parent 53.333333 -3.309153e-12 #> 51 parent 54.545455 -2.966799e-11 #> 52 parent 55.757576 -4.723329e-11 #> 53 parent 56.969697 7.635833e-11 #> 54 parent 58.181818 -1.887064e-11 #> 55 parent 59.393939 -1.548352e-10 #> 56 parent 60.000000 -1.053819e-10 #> 57 parent 60.606061 5.780435e-12 #> 58 parent 61.818182 9.056244e-11 #> 59 parent 63.030303 -8.889581e-11 #> 60 parent 64.242424 -6.653389e-11 #> 61 parent 65.454545 1.181114e-10 #> 62 parent 66.666667 -9.226329e-12 #> 63 parent 67.878788 -8.897326e-11 #> 64 parent 69.090909 1.984998e-10 #> 65 parent 70.303030 3.255550e-11 #> 66 parent 71.515152 -2.991002e-10 #> 67 parent 72.727273 2.254268e-10 #> 68 parent 73.939394 2.696039e-10 #> 69 parent 75.151515 1.226806e-10 #> 70 parent 76.363636 3.447399e-11 #> 71 parent 77.575758 2.048902e-11 #> 72 parent 78.787879 6.830755e-12 #> 73 parent 80.000000 8.242171e-13 #> 74 parent 81.212121 -5.357740e-12 #> 75 parent 82.424242 2.198907e-11 #> 76 parent 83.636364 3.739511e-11 #> 77 parent 84.848485 -6.616091e-12 #> 78 parent 86.060606 -2.562689e-12 #> 79 parent 87.272727 4.089395e-11 #> 80 parent 88.484848 -2.042159e-11 #> 81 parent 89.696970 -4.088127e-11 #> 82 parent 90.000000 -1.874889e-11 #> 83 parent 90.909091 4.225747e-11 #> 84 parent 92.121212 8.054402e-12 #> 85 parent 93.333333 3.917595e-12 #> 86 parent 94.545455 6.591454e-12 #> 87 parent 95.757576 2.790958e-11 #> 88 parent 96.969697 2.720721e-12 #> 89 parent 98.181818 -1.304470e-12 #> 90 parent 99.393939 1.345055e-11 #> 91 parent 100.606061 -9.662077e-12 #> 92 parent 101.818182 -2.086798e-11 #> 93 parent 103.030303 9.332507e-12 #> 94 parent 104.242424 -6.752606e-12 #> 95 parent 105.454545 -3.326620e-11 #> 96 parent 106.666667 2.500680e-11 #> 97 parent 107.878788 2.184148e-11 #> 98 parent 109.090909 -5.985657e-11 #> 99 parent 110.303030 -8.750836e-14 #> 100 parent 111.515152 1.820588e-12 #> 101 parent 112.727273 -1.261472e-11 #> 102 parent 113.939394 1.455439e-11 #> 103 parent 115.151515 1.945812e-12 #> 104 parent 116.363636 9.598249e-13 #> 105 parent 117.575758 1.724679e-12 #> 106 parent 118.787879 -1.334504e-12 #> 107 parent 120.000000 -2.804801e-11 #> 108 M1 0.000000 0.000000e+00 #> 109 M1 1.000000 3.478911e+01 #> 110 M1 1.212121 3.791354e+01 #> 111 M1 2.424242 4.185645e+01 #> 112 M1 3.000000 3.937027e+01 #> 113 M1 3.636364 3.544167e+01 #> 114 M1 4.848485 2.723995e+01 #> 115 M1 6.060606 2.000711e+01 #> 116 M1 7.000000 1.548714e+01 #> 117 M1 7.272727 1.435144e+01 #> 118 M1 8.484848 1.016177e+01 #> 119 M1 9.696970 7.142649e+00 #> 120 M1 10.909091 4.999441e+00 #> 121 M1 12.121212 3.490801e+00 #> 122 M1 13.333333 2.433954e+00 #> 123 M1 14.000000 1.995311e+00 #> 124 M1 14.545455 1.695664e+00 #> 125 M1 15.757576 1.180746e+00 #> 126 M1 16.969697 8.219589e-01 #> 127 M1 18.181818 5.720991e-01 #> 128 M1 19.393939 3.981531e-01 #> 129 M1 20.606061 2.770793e-01 #> 130 M1 21.818182 1.928162e-01 #> 131 M1 23.030303 1.341758e-01 #> 132 M1 24.242424 9.336844e-02 #> 133 M1 25.454545 6.497152e-02 #> 134 M1 26.666667 4.521101e-02 #> 135 M1 27.878788 3.146041e-02 #> 136 M1 28.000000 3.034005e-02 #> 137 M1 29.090909 2.189192e-02 #> 138 M1 30.303030 1.523362e-02 #> 139 M1 31.515152 1.060040e-02 #> 140 M1 32.727273 7.376345e-03 #> 141 M1 33.939394 5.132870e-03 #> 142 M1 35.151515 3.571730e-03 #> 143 M1 36.363636 2.485406e-03 #> 144 M1 37.575758 1.729482e-03 #> 145 M1 38.787879 1.203467e-03 #> 146 M1 40.000000 8.374380e-04 #> 147 M1 41.212121 5.827347e-04 #> 148 M1 42.424242 4.054989e-04 #> 149 M1 43.636364 2.821681e-04 #> 150 M1 44.848485 1.963481e-04 #> 151 M1 46.060606 1.366297e-04 #> 152 M1 47.272727 9.507439e-05 #> 153 M1 48.484848 6.615797e-05 #> 154 M1 49.696970 4.603629e-05 #> 155 M1 50.909091 3.203434e-05 #> 156 M1 52.121212 2.229196e-05 #> 157 M1 53.333333 1.551223e-05 #> 158 M1 54.545455 1.079420e-05 #> 159 M1 55.757576 7.511255e-06 #> 160 M1 56.969697 5.226640e-06 #> 161 M1 58.181818 3.636450e-06 #> 162 M1 59.393939 2.530191e-06 #> 163 M1 60.000000 2.110651e-06 #> 164 M1 60.606061 1.760625e-06 #> 165 M1 61.818182 1.225095e-06 #> 166 M1 63.030303 8.527010e-07 #> 167 M1 64.242424 5.934161e-07 #> 168 M1 65.454545 4.127474e-07 #> 169 M1 66.666667 2.874114e-07 #> 170 M1 67.878788 2.001921e-07 #> 171 M1 69.090909 1.389331e-07 #> 172 M1 70.303030 9.678549e-08 #> 173 M1 71.515152 6.777214e-08 #> 174 M1 72.727273 4.658761e-08 #> 175 M1 73.939394 3.226837e-08 #> 176 M1 75.151515 2.253752e-08 #> 177 M1 76.363636 1.574843e-08 #> 178 M1 77.575758 1.096303e-08 #> 179 M1 78.787879 7.638209e-09 #> 180 M1 80.000000 5.319996e-09 #> 181 M1 81.212121 3.709993e-09 #> 182 M1 82.424242 2.548810e-09 #> 183 M1 83.636364 1.744629e-09 #> 184 M1 84.848485 1.256081e-09 #> 185 M1 86.060606 8.714672e-10 #> 186 M1 87.272727 5.511830e-10 #> 187 M1 88.484848 4.466725e-10 #> 188 M1 89.696970 3.452654e-10 #> 189 M1 90.000000 2.913252e-10 #> 190 M1 90.909091 1.489262e-10 #> 191 M1 92.121212 1.311985e-10 #> 192 M1 93.333333 9.347248e-11 #> 193 M1 94.545455 6.004640e-11 #> 194 M1 95.757576 1.166926e-11 #> 195 M1 96.969697 2.968203e-11 #> 196 M1 98.181818 2.478228e-11 #> 197 M1 99.393939 -1.291838e-12 #> 198 M1 100.606061 2.366481e-11 #> 199 M1 101.818182 3.472871e-11 #> 200 M1 103.030303 -6.633877e-12 #> 201 M1 104.242424 1.248743e-11 #> 202 M1 105.454545 4.557313e-11 #> 203 M1 106.666667 -3.046261e-11 #> 204 M1 107.878788 -2.693037e-11 #> 205 M1 109.090909 7.816593e-11 #> 206 M1 110.303030 7.276098e-13 #> 207 M1 111.515152 -1.922924e-12 #> 208 M1 112.727273 1.658481e-11 #> 209 M1 113.939394 -1.858452e-11 #> 210 M1 115.151515 -2.368198e-12 #> 211 M1 116.363636 -1.138989e-12 #> 212 M1 117.575758 -2.157011e-12 #> 213 M1 118.787879 1.771568e-12 #> 214 M1 120.000000 3.624738e-11 #> 215 M2 0.000000 0.000000e+00 #> 216 M2 1.000000 4.454830e+00 #> 217 M2 1.212121 6.103803e+00 #> 218 M2 2.424242 1.667567e+01 #> 219 M2 3.000000 2.152527e+01 #> 220 M2 3.636364 2.637280e+01 #> 221 M2 4.848485 3.384106e+01 #> 222 M2 6.060606 3.910279e+01 #> 223 M2 7.000000 4.192058e+01 #> 224 M2 7.272727 4.256708e+01 #> 225 M2 8.484848 4.467909e+01 #> 226 M2 9.696970 4.581396e+01 #> 227 M2 10.909091 4.625927e+01 #> 228 M2 12.121212 4.622588e+01 #> 229 M2 13.333333 4.586473e+01 #> 230 M2 14.000000 4.556646e+01 #> 231 M2 14.545455 4.528249e+01 #> 232 M2 15.757576 4.455394e+01 #> 233 M2 16.969697 4.373119e+01 #> 234 M2 18.181818 4.285048e+01 #> 235 M2 19.393939 4.193685e+01 #> 236 M2 20.606061 4.100759e+01 #> 237 M2 21.818182 4.007456e+01 #> 238 M2 23.030303 3.914584e+01 #> 239 M2 24.242424 3.822688e+01 #> 240 M2 25.454545 3.732133e+01 #> 241 M2 26.666667 3.643154e+01 #> 242 M2 27.878788 3.555901e+01 #> 243 M2 28.000000 3.547275e+01 #> 244 M2 29.090909 3.470463e+01 #> 245 M2 30.303030 3.386887e+01 #> 246 M2 31.515152 3.305190e+01 #> 247 M2 32.727273 3.225371e+01 #> 248 M2 33.939394 3.147416e+01 #> 249 M2 35.151515 3.071300e+01 #> 250 M2 36.363636 2.996993e+01 #> 251 M2 37.575758 2.924463e+01 #> 252 M2 38.787879 2.853672e+01 #> 253 M2 40.000000 2.784585e+01 #> 254 M2 41.212121 2.717163e+01 #> 255 M2 42.424242 2.651368e+01 #> 256 M2 43.636364 2.587163e+01 #> 257 M2 44.848485 2.524511e+01 #> 258 M2 46.060606 2.463374e+01 #> 259 M2 47.272727 2.403716e+01 #> 260 M2 48.484848 2.345502e+01 #> 261 M2 49.696970 2.288698e+01 #> 262 M2 50.909091 2.233268e+01 #> 263 M2 52.121212 2.179181e+01 #> 264 M2 53.333333 2.126404e+01 #> 265 M2 54.545455 2.074905e+01 #> 266 M2 55.757576 2.024653e+01 #> 267 M2 56.969697 1.975618e+01 #> 268 M2 58.181818 1.927770e+01 #> 269 M2 59.393939 1.881081e+01 #> 270 M2 60.000000 1.858163e+01 #> 271 M2 60.606061 1.835523e+01 #> 272 M2 61.818182 1.791068e+01 #> 273 M2 63.030303 1.747690e+01 #> 274 M2 64.242424 1.705363e+01 #> 275 M2 65.454545 1.664061e+01 #> 276 M2 66.666667 1.623759e+01 #> 277 M2 67.878788 1.584433e+01 #> 278 M2 69.090909 1.546059e+01 #> 279 M2 70.303030 1.508615e+01 #> 280 M2 71.515152 1.472078e+01 #> 281 M2 72.727273 1.436425e+01 #> 282 M2 73.939394 1.401636e+01 #> 283 M2 75.151515 1.367690e+01 #> 284 M2 76.363636 1.334566e+01 #> 285 M2 77.575758 1.302244e+01 #> 286 M2 78.787879 1.270705e+01 #> 287 M2 80.000000 1.239929e+01 #> 288 M2 81.212121 1.209899e+01 #> 289 M2 82.424242 1.180597e+01 #> 290 M2 83.636364 1.152004e+01 #> 291 M2 84.848485 1.124103e+01 #> 292 M2 86.060606 1.096878e+01 #> 293 M2 87.272727 1.070313e+01 #> 294 M2 88.484848 1.044391e+01 #> 295 M2 89.696970 1.019097e+01 #> 296 M2 90.000000 1.012869e+01 #> 297 M2 90.909091 9.944151e+00 #> 298 M2 92.121212 9.703312e+00 #> 299 M2 93.333333 9.468307e+00 #> 300 M2 94.545455 9.238993e+00 #> 301 M2 95.757576 9.015233e+00 #> 302 M2 96.969697 8.796892e+00 #> 303 M2 98.181818 8.583839e+00 #> 304 M2 99.393939 8.375946e+00 #> 305 M2 100.606061 8.173088e+00 #> 306 M2 101.818182 7.975143e+00 #> 307 M2 103.030303 7.781992e+00 #> 308 M2 104.242424 7.593520e+00 #> 309 M2 105.454545 7.409611e+00 #> 310 M2 106.666667 7.230157e+00 #> 311 M2 107.878788 7.055049e+00 #> 312 M2 109.090909 6.884182e+00 #> 313 M2 110.303030 6.717454e+00 #> 314 M2 111.515152 6.554763e+00 #> 315 M2 112.727273 6.396012e+00 #> 316 M2 113.939394 6.241107e+00 #> 317 M2 115.151515 6.089953e+00 #> 318 M2 116.363636 5.942460e+00 #> 319 M2 117.575758 5.798538e+00 #> 320 M2 118.787879 5.658103e+00 #> 321 M2 120.000000 5.521069e+00 #> #> $cost #> function (P) #> { #> assign("calls", calls + 1, inherits = TRUE) #> if (trace_parms) #> cat(P, "\n") #> if (length(state.ini.optim) > 0) { #> odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed) #> names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames) #> } #> else { #> odeini <- state.ini.fixed #> names(odeini) <- state.ini.fixed.boxnames #> } #> odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], #> transparms.fixed) #> parms <- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates, #> transform_fractions = transform_fractions) #> out <- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, #> use_compiled = use_compiled, method.ode = method.ode, #> atol = atol, rtol = rtol, ...) #> assign("out_predicted", out, inherits = TRUE) #> mC <- modCost(out, observed, y = "value", err = err, weight = weight, #> scaleVar = scaleVar) #> if (mC$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: 0x3ff8420> #> #> $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: 0x3ff8420> #> #> $hessian_notrans #> parent_0 k_parent k_M1 k_M2 f_parent_to_M1 #> parent_0 8.433594 -40.12785 -61.53042 -3406.469 461.2995 #> k_parent -40.127847 19322.43697 3053.54654 3740.691 8966.4055 #> k_M1 -61.530424 3053.54654 70106.05907 7274.316 -8169.6841 #> k_M2 -3406.468786 3740.69112 7274.31610 12274341.595 -452294.7998 #> f_parent_to_M1 461.299501 8966.40549 -8169.68407 -452294.800 61249.1755 #> f_M1_to_M2 327.648696 2884.22668 17504.38651 -480941.198 43503.6440 #> f_M1_to_M2 #> parent_0 327.6487 #> k_parent 2884.2267 #> k_M1 17504.3865 #> k_M2 -480941.1983 #> f_parent_to_M1 43503.6440 #> f_M1_to_M2 46258.9775 #> #> $start #> value type #> parent_0 101.3500 state #> k_parent 0.1000 deparm #> k_M1 0.1001 deparm #> k_M2 0.1002 deparm #> f_parent_to_M1 0.5000 deparm #> f_M1_to_M2 0.5000 deparm #> #> $start_transformed #> value lower upper #> parent_0 101.350000 -Inf Inf #> log_k_parent -2.302585 -Inf Inf #> log_k_M1 -2.301586 -Inf Inf #> log_k_M2 -2.300587 -Inf Inf #> f_parent_ilr_1 0.000000 -Inf Inf #> f_M1_ilr_1 0.000000 -Inf Inf #> #> $fixed #> value type #> M1_0 0 state #> M2_0 0 state #> #> $data #> time variable observed predicted residual #> 1 0 parent 101.5 1.020625e+02 -0.56248353 #> 2 0 parent 101.2 1.020625e+02 -0.86248353 #> 3 1 parent 53.9 4.872881e+01 5.17118695 #> 4 1 parent 47.5 4.872881e+01 -1.22881305 #> 5 3 parent 10.4 1.110773e+01 -0.70772795 #> 6 3 parent 7.6 1.110773e+01 -3.50772795 #> 7 7 parent 1.1 5.771704e-01 0.52282962 #> 8 7 parent 0.3 5.771704e-01 -0.27717038 #> 9 14 parent NA 3.263939e-03 NA #> 10 14 parent 3.5 3.263939e-03 3.49673606 #> 11 28 parent NA 1.044512e-07 NA #> 12 28 parent 3.2 1.044512e-07 3.19999990 #> 13 60 parent NA -1.053819e-10 NA #> 14 60 parent NA -1.053819e-10 NA #> 15 90 parent 0.6 -1.874889e-11 0.60000000 #> 16 90 parent NA -1.874889e-11 NA #> 17 120 parent NA -2.804801e-11 NA #> 18 120 parent 3.5 -2.804801e-11 3.50000000 #> 19 0 M1 NA 0.000000e+00 NA #> 20 0 M1 NA 0.000000e+00 NA #> 21 1 M1 36.4 3.478911e+01 1.61088639 #> 22 1 M1 37.4 3.478911e+01 2.61088639 #> 23 3 M1 34.3 3.937027e+01 -5.07026619 #> 24 3 M1 39.8 3.937027e+01 0.42973381 #> 25 7 M1 15.1 1.548714e+01 -0.38714436 #> 26 7 M1 17.8 1.548714e+01 2.31285564 #> 27 14 M1 5.8 1.995311e+00 3.80468869 #> 28 14 M1 1.2 1.995311e+00 -0.79531131 #> 29 28 M1 NA 3.034005e-02 NA #> 30 28 M1 NA 3.034005e-02 NA #> 31 60 M1 0.5 2.110651e-06 0.49999789 #> 32 60 M1 NA 2.110651e-06 NA #> 33 90 M1 NA 2.913252e-10 NA #> 34 90 M1 3.2 2.913252e-10 3.20000000 #> 35 120 M1 1.5 3.624738e-11 1.50000000 #> 36 120 M1 0.6 3.624738e-11 0.60000000 #> 37 0 M2 NA 0.000000e+00 NA #> 38 0 M2 NA 0.000000e+00 NA #> 39 1 M2 NA 4.454830e+00 NA #> 40 1 M2 4.8 4.454830e+00 0.34517017 #> 41 3 M2 20.9 2.152527e+01 -0.62526794 #> 42 3 M2 19.3 2.152527e+01 -2.22526794 #> 43 7 M2 42.0 4.192058e+01 0.07941701 #> 44 7 M2 43.1 4.192058e+01 1.17941701 #> 45 14 M2 49.4 4.556646e+01 3.83353798 #> 46 14 M2 44.3 4.556646e+01 -1.26646202 #> 47 28 M2 34.6 3.547275e+01 -0.87274743 #> 48 28 M2 33.0 3.547275e+01 -2.47274743 #> 49 60 M2 18.8 1.858163e+01 0.21837410 #> 50 60 M2 17.6 1.858163e+01 -0.98162590 #> 51 90 M2 10.6 1.012869e+01 0.47130583 #> 52 90 M2 10.8 1.012869e+01 0.67130583 #> 53 120 M2 9.8 5.521069e+00 4.27893112 #> 54 120 M2 3.3 5.521069e+00 -2.22106888 #> #> $atol #> [1] 1e-08 #> #> $rtol #> [1] 1e-10 #> #> $weight.ini #> [1] "none" #> #> $reweight.tol #> [1] 1e-08 #> #> $reweight.max.iter #> [1] 10 #> #> $bparms.optim #> parent_0 k_parent k_M1 k_M2 f_parent_to_M1 #> 102.0624835 0.7393147 0.2991566 0.0202267 0.7686858 #> f_M1_to_M2 #> 0.7229005 #> #> $bparms.fixed #> M1_0 M2_0 #> 0 0 #> #> $bparms.ode #> k_parent f_parent_to_M1 k_M1 f_M1_to_M2 k_M2 #> 0.7393147 0.7686858 0.2991566 0.7229005 0.0202267 #> #> $bparms.state #> parent M1 M2 #> 102.0625 0.0000 0.0000 #> #> $date #> [1] "Fri Nov 18 15:20:48 2016" #> #> attr(,"class") #> [1] "mkinfit" "modFit"