diff options
Diffstat (limited to 'docs/reference/synthetic_data_for_UBA.html')
| -rw-r--r-- | docs/reference/synthetic_data_for_UBA.html | 938 |
1 files changed, 130 insertions, 808 deletions
diff --git a/docs/reference/synthetic_data_for_UBA.html b/docs/reference/synthetic_data_for_UBA.html index 97407402..c2794427 100644 --- a/docs/reference/synthetic_data_for_UBA.html +++ b/docs/reference/synthetic_data_for_UBA.html @@ -137,722 +137,24 @@ <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>), <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> -<span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_synth_SFO_lin</span>, <span class='no'>synthetic_data_for_UBA_2014</span><span class='kw'>[[</span><span class='fl'>1</span>]]$<span class='no'>data</span>)</div><div class='output co'>#> 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.</div><div class='output co'>#> $par -#> parent_0 log_k_parent log_k_M1 log_k_M2 f_parent_ilr_1 -#> 102.0624835 -0.3020316 -1.2067882 -3.9007519 0.8491684 -#> f_M1_ilr_1 -#> 0.6780411 +<span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_synth_SFO_lin</span>, <span class='no'>synthetic_data_for_UBA_2014</span><span class='kw'>[[</span><span class='fl'>1</span>]]$<span class='no'>data</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) +<span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>fit</span>)</div><img src='synthetic_data_for_UBA_2014-10.png' alt='' width='540' height='400' /><div class='input'><span class='fu'>summary</span>(<span class='no'>fit</span>)</div><div class='output co'>#> mkin version: 0.9.44.9000 +#> R version: 3.3.2 +#> Date of fit: Fri Nov 18 16:16:12 2016 +#> Date of summary: Fri Nov 18 16:16:12 2016 #> -#> $ssr -#> [1] 201.5169 +#> Equations: +#> d_parent/dt = - k_parent * parent +#> d_M1/dt = + f_parent_to_M1 * k_parent * parent - k_M1 * M1 +#> d_M2/dt = + f_M1_to_M2 * k_M1 * M1 - k_M2 * M2 #> -#> $convergence -#> [1] 0 +#> Model predictions using solution type deSolve #> -#> $iterations -#> [1] 43 +#> Fitted with method Port using 351 model solutions performed in 2.193 s #> -#> $evaluations -#> function gradient -#> 56 281 +#> Weighting: none #> -#> $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 +#> Starting values for parameters to be optimised: #> value type #> parent_0 101.3500 state #> k_parent 0.1000 deparm @@ -861,7 +163,7 @@ #> f_parent_to_M1 0.5000 deparm #> f_M1_to_M2 0.5000 deparm #> -#> $start_transformed +#> Starting values for the transformed parameters actually optimised: #> value lower upper #> parent_0 101.350000 -Inf Inf #> log_k_parent -2.302585 -Inf Inf @@ -870,106 +172,126 @@ #> f_parent_ilr_1 0.000000 -Inf Inf #> f_M1_ilr_1 0.000000 -Inf Inf #> -#> $fixed +#> Fixed parameter values: #> 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" </div><div class='input'> +#> Optimised, transformed parameters with symmetric confidence intervals: +#> Estimate Std. Error Lower Upper +#> parent_0 102.1000 1.71400 98.5800 105.5000 +#> log_k_parent -0.3020 0.04294 -0.3894 -0.2147 +#> log_k_M1 -1.2070 0.07599 -1.3610 -1.0520 +#> log_k_M2 -3.9010 0.06952 -4.0420 -3.7590 +#> f_parent_ilr_1 0.8492 0.18090 0.4812 1.2170 +#> f_M1_ilr_1 0.6780 0.18860 0.2943 1.0620 +#> +#> Parameter correlation: +#> parent_0 log_k_parent log_k_M1 log_k_M2 f_parent_ilr_1 +#> parent_0 1.00000 0.40213 -0.1693 0.02912 -0.4726 +#> log_k_parent 0.40213 1.00000 -0.4210 0.07241 -0.5837 +#> log_k_M1 -0.16931 -0.42102 1.0000 -0.37657 0.7438 +#> log_k_M2 0.02912 0.07241 -0.3766 1.00000 -0.2518 +#> f_parent_ilr_1 -0.47263 -0.58367 0.7438 -0.25177 1.0000 +#> f_M1_ilr_1 0.17148 0.42642 -0.8054 0.52648 -0.8602 +#> f_M1_ilr_1 +#> parent_0 0.1715 +#> log_k_parent 0.4264 +#> log_k_M1 -0.8054 +#> log_k_M2 0.5265 +#> f_parent_ilr_1 -0.8602 +#> f_M1_ilr_1 1.0000 +#> +#> Residual standard error: 2.471 on 33 degrees of freedom +#> +#> Backtransformed parameters: +#> Confidence intervals for internally transformed parameters are asymmetric. +#> t-test (unrealistically) based on the assumption of normal distribution +#> for estimators of untransformed parameters. +#> Estimate t value Pr(>t) Lower Upper +#> parent_0 102.10000 59.55 1.815e-35 98.58000 105.5000 +#> k_parent 0.73930 23.29 2.337e-22 0.67750 0.8068 +#> k_M1 0.29920 13.16 5.552e-15 0.25630 0.3492 +#> k_M2 0.02023 14.38 4.497e-16 0.01756 0.0233 +#> f_parent_to_M1 0.76870 16.90 4.093e-18 0.66380 0.8483 +#> f_M1_to_M2 0.72290 13.53 2.557e-15 0.60260 0.8178 +#> +#> Chi2 error levels in percent: +#> err.min n.optim df +#> All data 8.454 6 17 +#> parent 8.660 2 6 +#> M1 10.583 2 5 +#> M2 3.586 2 6 +#> +#> Resulting formation fractions: +#> ff +#> parent_M1 0.7687 +#> parent_sink 0.2313 +#> M1_M2 0.7229 +#> M1_sink 0.2771 +#> +#> Estimated disappearance times: +#> DT50 DT90 +#> parent 0.9376 3.114 +#> M1 2.3170 7.697 +#> M2 34.2689 113.839 +#> +#> Data: +#> time variable observed predicted residual +#> 0 parent 101.5 1.021e+02 -0.56248 +#> 0 parent 101.2 1.021e+02 -0.86248 +#> 1 parent 53.9 4.873e+01 5.17119 +#> 1 parent 47.5 4.873e+01 -1.22881 +#> 3 parent 10.4 1.111e+01 -0.70773 +#> 3 parent 7.6 1.111e+01 -3.50773 +#> 7 parent 1.1 5.772e-01 0.52283 +#> 7 parent 0.3 5.772e-01 -0.27717 +#> 14 parent NA 3.264e-03 NA +#> 14 parent 3.5 3.264e-03 3.49674 +#> 28 parent NA 1.045e-07 NA +#> 28 parent 3.2 1.045e-07 3.20000 +#> 60 parent NA -1.054e-10 NA +#> 60 parent NA -1.054e-10 NA +#> 90 parent 0.6 -1.875e-11 0.60000 +#> 90 parent NA -1.875e-11 NA +#> 120 parent NA -2.805e-11 NA +#> 120 parent 3.5 -2.805e-11 3.50000 +#> 0 M1 NA 0.000e+00 NA +#> 0 M1 NA 0.000e+00 NA +#> 1 M1 36.4 3.479e+01 1.61089 +#> 1 M1 37.4 3.479e+01 2.61089 +#> 3 M1 34.3 3.937e+01 -5.07027 +#> 3 M1 39.8 3.937e+01 0.42973 +#> 7 M1 15.1 1.549e+01 -0.38714 +#> 7 M1 17.8 1.549e+01 2.31286 +#> 14 M1 5.8 1.995e+00 3.80469 +#> 14 M1 1.2 1.995e+00 -0.79531 +#> 28 M1 NA 3.034e-02 NA +#> 28 M1 NA 3.034e-02 NA +#> 60 M1 0.5 2.111e-06 0.50000 +#> 60 M1 NA 2.111e-06 NA +#> 90 M1 NA 2.913e-10 NA +#> 90 M1 3.2 2.913e-10 3.20000 +#> 120 M1 1.5 3.625e-11 1.50000 +#> 120 M1 0.6 3.625e-11 0.60000 +#> 0 M2 NA 0.000e+00 NA +#> 0 M2 NA 0.000e+00 NA +#> 1 M2 NA 4.455e+00 NA +#> 1 M2 4.8 4.455e+00 0.34517 +#> 3 M2 20.9 2.153e+01 -0.62527 +#> 3 M2 19.3 2.153e+01 -2.22527 +#> 7 M2 42.0 4.192e+01 0.07942 +#> 7 M2 43.1 4.192e+01 1.17942 +#> 14 M2 49.4 4.557e+01 3.83354 +#> 14 M2 44.3 4.557e+01 -1.26646 +#> 28 M2 34.6 3.547e+01 -0.87275 +#> 28 M2 33.0 3.547e+01 -2.47275 +#> 60 M2 18.8 1.858e+01 0.21837 +#> 60 M2 17.6 1.858e+01 -0.98163 +#> 90 M2 10.6 1.013e+01 0.47131 +#> 90 M2 10.8 1.013e+01 0.67131 +#> 120 M2 9.8 5.521e+00 4.27893 +#> 120 M2 3.3 5.521e+00 -2.22107</div><div class='input'> </div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> |