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| author | Johannes Ranke <jranke@uni-bremen.de> | 2021-10-05 17:33:52 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2021-10-05 17:33:52 +0200 |
| commit | ff83d8b2ba623513d92ac90fac4a1b0ec98c2cb5 (patch) | |
| tree | 7811a1e083951d84bd54bfce9bb62af640da329e /docs/dev/reference/summary.nlmixr.mmkin.html | |
| parent | 121e38654eea03c91aa117da58fc342feff01756 (diff) | |
Update docs
Diffstat (limited to 'docs/dev/reference/summary.nlmixr.mmkin.html')
| -rw-r--r-- | docs/dev/reference/summary.nlmixr.mmkin.html | 790 |
1 files changed, 727 insertions, 63 deletions
diff --git a/docs/dev/reference/summary.nlmixr.mmkin.html b/docs/dev/reference/summary.nlmixr.mmkin.html index 4831bbdf..891e26ea 100644 --- a/docs/dev/reference/summary.nlmixr.mmkin.html +++ b/docs/dev/reference/summary.nlmixr.mmkin.html @@ -258,73 +258,737 @@ nlmixr authors for the parts inherited from nlmixr.</p> quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span>, cores <span class='op'>=</span> <span class='fl'>5</span><span class='op'>)</span> <span class='va'>f_saemix_dfop_sfo</span> <span class='op'><-</span> <span class='fu'>mkin</span><span class='fu'>::</span><span class='fu'><a href='saem.html'>saem</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Thu Sep 16 14:35:21 2021" +#> [1] "Tue Oct 5 17:27:02 2021" #> .... #> Minimisation finished -#> [1] "Thu Sep 16 14:35:33 2021"</div><div class='input'><span class='va'>f_nlme_dfop_sfo</span> <span class='op'><-</span> <span class='fu'>mkin</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span><span class='op'>)</span> +#> [1] "Tue Oct 5 17:27:13 2021"</div><div class='input'><span class='va'>f_nlme_dfop_sfo</span> <span class='op'><-</span> <span class='fu'>mkin</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span><span class='op'>)</span> </div><div class='output co'>#> <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 6, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_m1</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 1.412 0.101 1.525</span></div><div class='input'><span class='co'># The following takes a very long time but gives</span> +</div><div class='output co'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> 1: 1.0127e+02 -3.8515e+00 -2.0719e+00 -3.7271e+00 -1.9335e+00 4.0311e-01 6.9594e+00 1.5021e-01 5.3947e-01 1.9686e-01 3.7429e-01 5.4209e-01 8.4121e+00 7.3391e-02 7.1185e+00 2.5869e-01 +#> 2: 1.0136e+02 -3.8005e+00 -2.3424e+00 -4.0759e+00 -1.6475e+00 1.1598e-01 6.6115e+00 1.4406e-01 5.1249e-01 1.8701e-01 3.5786e-01 5.1499e-01 4.9102e+00 6.2829e-02 4.7230e+00 7.8901e-02 +#> 3: 1.0126e+02 -4.0285e+00 -2.3629e+00 -4.1271e+00 -1.1733e+00 1.7634e-02 6.2809e+00 1.6892e-01 4.8687e-01 1.7766e-01 3.3997e-01 4.8924e-01 3.2256e+00 6.6693e-02 3.3261e+00 8.7190e-02 +#> 4: 1.0105e+02 -4.0894e+00 -2.5516e+00 -4.1037e+00 -1.0816e+00 4.5377e-02 5.9668e+00 1.6048e-01 4.6252e-01 1.6878e-01 3.2297e-01 4.6478e-01 2.4343e+00 7.0557e-02 2.2610e+00 9.2498e-02 +#> 5: 1.0101e+02 -4.1364e+00 -2.4605e+00 -4.0737e+00 -1.0920e+00 -4.7953e-03 5.9593e+00 1.5245e-01 4.3940e-01 1.8078e-01 3.0682e-01 5.4688e-01 1.7424e+00 7.4776e-02 1.5144e+00 1.0787e-01 +#> 6: 1.0042e+02 -4.0933e+00 -2.4472e+00 -4.1090e+00 -9.7996e-01 -9.0472e-02 6.0175e+00 1.4483e-01 4.1743e-01 1.8824e-01 2.9148e-01 5.3033e-01 1.5545e+00 6.8588e-02 1.3401e+00 9.8865e-02 +#> 7: 1.0078e+02 -4.0911e+00 -2.4335e+00 -4.0758e+00 -9.9422e-01 -7.8849e-02 6.6318e+00 1.3759e-01 3.9656e-01 1.7882e-01 2.7691e-01 5.0381e-01 1.3780e+00 6.9978e-02 1.1346e+00 9.6162e-02 +#> 8: 1.0077e+02 -4.0196e+00 -2.4345e+00 -4.0444e+00 -9.3483e-01 -1.1032e-01 6.3002e+00 1.3071e-01 3.7673e-01 1.6988e-01 2.6306e-01 4.8191e-01 1.1774e+00 7.4232e-02 1.0270e+00 9.5616e-02 +#> 9: 1.0118e+02 -4.0436e+00 -2.4649e+00 -4.0207e+00 -8.9829e-01 -1.7784e-01 5.9852e+00 1.2417e-01 3.5789e-01 1.6139e-01 2.4991e-01 5.5466e-01 1.1040e+00 7.1515e-02 1.0342e+00 9.3972e-02 +#> 10: 1.0143e+02 -4.0523e+00 -2.3737e+00 -4.0184e+00 -9.1167e-01 -2.3828e-01 5.8520e+00 1.1797e-01 3.4196e-01 1.5332e-01 2.3741e-01 5.2849e-01 1.0510e+00 7.5719e-02 1.0638e+00 9.3973e-02 +#> 11: 1.0119e+02 -4.0699e+00 -2.3680e+00 -4.0191e+00 -9.4858e-01 -1.7310e-01 6.9958e+00 1.1207e-01 3.6891e-01 1.4565e-01 2.2554e-01 5.0206e-01 1.0247e+00 7.5497e-02 1.0292e+00 9.3707e-02 +#> 12: 1.0121e+02 -4.0189e+00 -2.4198e+00 -4.0139e+00 -9.1693e-01 -2.0613e-01 6.6460e+00 1.0646e-01 3.5046e-01 1.3837e-01 2.1427e-01 5.7696e-01 1.1046e+00 7.6090e-02 9.3689e-01 9.4115e-02 +#> 13: 1.0083e+02 -4.0451e+00 -2.4395e+00 -4.0235e+00 -9.4535e-01 -1.4723e-01 6.3137e+00 1.0114e-01 3.3294e-01 1.3145e-01 2.0355e-01 5.4811e-01 1.0360e+00 7.3381e-02 9.7078e-01 9.1659e-02 +#> 14: 1.0056e+02 -4.0401e+00 -2.4045e+00 -4.0054e+00 -9.4191e-01 -1.3928e-01 5.9980e+00 9.6084e-02 3.4934e-01 1.2488e-01 1.9338e-01 5.2071e-01 1.0303e+00 7.7118e-02 8.8372e-01 9.0469e-02 +#> 15: 1.0070e+02 -4.0388e+00 -2.4210e+00 -4.0113e+00 -9.1136e-01 -1.2702e-01 5.6981e+00 9.1279e-02 3.3187e-01 1.1864e-01 1.8371e-01 4.9467e-01 1.0486e+00 7.2427e-02 7.8179e-01 9.1572e-02 +#> 16: 1.0078e+02 -4.0175e+00 -2.4766e+00 -4.0191e+00 -9.0733e-01 -1.1952e-01 5.4132e+00 8.6716e-02 3.1528e-01 1.1270e-01 1.7452e-01 4.8928e-01 9.7799e-01 8.1464e-02 8.2935e-01 8.6520e-02 +#> 17: 1.0069e+02 -4.0533e+00 -2.5110e+00 -4.0294e+00 -9.1841e-01 -6.8363e-03 5.1426e+00 8.2380e-02 2.9952e-01 1.0707e-01 1.6580e-01 4.6482e-01 9.1609e-01 8.1008e-02 8.1783e-01 8.8818e-02 +#> 18: 99.9647 -4.0672 -2.5327 -4.0416 -0.9273 0.0097 4.8854 0.0783 0.2970 0.1280 0.1941 0.5053 0.9306 0.0764 0.8097 0.0881 +#> 19: 1.0027e+02 -4.0667e+00 -2.4653e+00 -4.0579e+00 -9.2776e-01 3.0417e-02 4.6412e+00 7.4348e-02 3.3694e-01 1.2164e-01 1.8435e-01 5.1797e-01 9.7386e-01 7.4954e-02 7.9297e-01 8.9915e-02 +#> 20: 1.0006e+02 -4.0935e+00 -2.4804e+00 -4.0721e+00 -9.3737e-01 1.9496e-02 4.4091e+00 7.0630e-02 3.3728e-01 1.2544e-01 1.7513e-01 6.0925e-01 1.0232e+00 7.4618e-02 7.9988e-01 8.9642e-02 +#> 21: 1.0043e+02 -4.0542e+00 -2.5168e+00 -4.0623e+00 -9.1553e-01 3.9474e-02 4.1887e+00 6.7099e-02 3.4553e-01 1.1917e-01 1.6638e-01 6.0827e-01 1.0155e+00 8.0771e-02 7.8424e-01 8.6213e-02 +#> 22: 1.0049e+02 -4.0449e+00 -2.5082e+00 -4.0849e+00 -9.2553e-01 4.5424e-02 3.9792e+00 6.3744e-02 3.2825e-01 1.2365e-01 1.5806e-01 5.8922e-01 8.2860e-01 8.3384e-02 8.2525e-01 8.9218e-02 +#> 23: 1.0067e+02 -4.0411e+00 -2.5460e+00 -4.0736e+00 -9.2578e-01 5.2422e-02 3.7803e+00 6.0557e-02 3.1661e-01 1.2306e-01 1.5016e-01 5.8274e-01 9.3412e-01 8.0508e-02 8.1829e-01 8.6377e-02 +#> 24: 1.0091e+02 -4.0314e+00 -2.5298e+00 -4.0566e+00 -8.9743e-01 3.7634e-02 3.5913e+00 5.7529e-02 3.5267e-01 1.2194e-01 1.4265e-01 5.5360e-01 9.6271e-01 7.6960e-02 8.8466e-01 8.5693e-02 +#> 25: 1.0100e+02 -4.0442e+00 -2.5399e+00 -4.0568e+00 -8.9494e-01 1.7415e-02 3.4117e+00 5.4652e-02 3.3504e-01 1.2781e-01 1.3552e-01 5.2592e-01 9.6040e-01 7.7299e-02 8.9561e-01 8.6893e-02 +#> 26: 1.0111e+02 -4.0354e+00 -2.5182e+00 -4.0899e+00 -9.0799e-01 7.6464e-02 4.8614e+00 5.1920e-02 3.1829e-01 1.2142e-01 1.3110e-01 4.9963e-01 9.6997e-01 7.4932e-02 8.2521e-01 9.3659e-02 +#> 27: 1.0159e+02 -4.0653e+00 -2.4934e+00 -4.0803e+00 -9.5632e-01 2.8659e-03 4.6184e+00 4.9324e-02 3.0237e-01 1.1535e-01 1.4743e-01 4.7465e-01 9.4314e-01 7.7860e-02 8.9820e-01 8.8210e-02 +#> 28: 1.0154e+02 -4.0487e+00 -2.4844e+00 -4.0511e+00 -9.6473e-01 -4.7382e-02 4.3874e+00 4.6858e-02 3.2049e-01 1.0958e-01 1.5243e-01 4.5091e-01 9.8808e-01 7.4786e-02 8.6833e-01 8.8720e-02 +#> 29: 1.0144e+02 -4.0414e+00 -2.4105e+00 -4.0504e+00 -9.4039e-01 -3.6753e-02 4.1681e+00 4.4515e-02 3.2754e-01 1.0410e-01 1.4940e-01 4.2837e-01 9.5520e-01 7.8507e-02 8.2408e-01 8.5998e-02 +#> 30: 1.0137e+02 -4.0292e+00 -2.4174e+00 -4.0382e+00 -9.3180e-01 -7.1482e-02 5.4636e+00 4.2289e-02 3.2074e-01 9.8896e-02 1.6877e-01 4.0695e-01 8.8153e-01 7.5106e-02 8.5239e-01 8.8266e-02 +#> 31: 1.0105e+02 -4.0387e+00 -2.4368e+00 -4.0346e+00 -9.1098e-01 -5.4730e-02 5.1904e+00 4.0175e-02 3.0470e-01 9.3951e-02 1.6034e-01 3.8660e-01 8.7853e-01 8.0278e-02 8.7981e-01 8.6404e-02 +#> 32: 1.0147e+02 -4.0435e+00 -2.4530e+00 -4.0365e+00 -9.1241e-01 -7.1281e-02 4.9309e+00 3.8166e-02 2.8947e-01 9.4694e-02 1.7475e-01 3.6727e-01 8.7005e-01 8.1398e-02 8.7784e-01 8.8976e-02 +#> 33: 1.0144e+02 -4.0092e+00 -2.4279e+00 -4.0090e+00 -8.8656e-01 -1.4017e-01 5.2945e+00 3.6258e-02 2.9770e-01 1.0169e-01 1.6601e-01 3.4891e-01 9.2202e-01 7.8841e-02 8.7551e-01 8.4011e-02 +#> 34: 1.0157e+02 -3.9839e+00 -2.4469e+00 -4.0180e+00 -8.3877e-01 -1.4664e-01 6.3506e+00 3.4445e-02 2.8282e-01 1.0831e-01 1.6850e-01 3.3146e-01 8.4403e-01 7.9056e-02 8.4620e-01 8.6363e-02 +#> 35: 1.0149e+02 -3.9928e+00 -2.4771e+00 -4.0106e+00 -8.6974e-01 -1.4219e-01 6.2039e+00 3.2722e-02 2.8123e-01 1.1283e-01 1.6008e-01 3.1489e-01 9.1308e-01 7.8685e-02 7.8939e-01 8.7289e-02 +#> 36: 1.0162e+02 -4.0099e+00 -2.4822e+00 -3.9880e+00 -8.7959e-01 -1.3237e-01 5.8937e+00 3.1086e-02 3.2200e-01 1.0719e-01 1.6077e-01 2.9914e-01 9.0821e-01 8.4066e-02 7.5559e-01 8.4838e-02 +#> 37: 1.0102e+02 -3.9962e+00 -2.4852e+00 -3.9954e+00 -8.8307e-01 -9.2070e-02 5.5991e+00 2.9532e-02 3.3713e-01 1.0183e-01 1.5333e-01 2.8419e-01 8.3918e-01 8.5231e-02 7.6007e-01 8.9541e-02 +#> 38: 1.0102e+02 -3.9987e+00 -2.5129e+00 -3.9833e+00 -8.7454e-01 -1.6469e-01 5.3191e+00 2.8055e-02 3.2027e-01 1.0792e-01 1.4707e-01 2.6998e-01 9.1490e-01 8.4715e-02 7.6778e-01 8.9241e-02 +#> 39: 1.0054e+02 -3.9875e+00 -2.4301e+00 -3.9797e+00 -8.7222e-01 -1.9597e-01 7.3800e+00 2.6653e-02 3.0426e-01 1.0801e-01 1.4393e-01 2.5648e-01 9.5901e-01 7.8320e-02 8.1559e-01 9.2429e-02 +#> 40: 1.0077e+02 -4.0057e+00 -2.4630e+00 -3.9849e+00 -8.6788e-01 -1.9606e-01 7.0110e+00 2.5320e-02 3.0385e-01 1.3164e-01 1.4567e-01 3.0284e-01 9.7123e-01 7.6328e-02 8.3681e-01 8.9349e-02 +#> 41: 1.0069e+02 -4.0143e+00 -2.3805e+00 -3.9962e+00 -8.7503e-01 -1.8532e-01 6.6604e+00 2.4054e-02 3.0707e-01 1.4668e-01 1.5021e-01 3.0404e-01 1.0072e+00 7.3629e-02 9.4494e-01 8.4745e-02 +#> 42: 1.0073e+02 -3.9861e+00 -2.4464e+00 -3.9919e+00 -8.7912e-01 -1.8435e-01 6.3274e+00 2.2851e-02 2.9171e-01 1.3935e-01 1.5080e-01 2.8883e-01 9.6502e-01 7.7470e-02 9.4221e-01 8.2459e-02 +#> 43: 1.0104e+02 -3.9881e+00 -2.4156e+00 -3.9688e+00 -8.9448e-01 -2.3739e-01 6.0110e+00 2.1709e-02 2.7713e-01 1.3238e-01 1.5603e-01 2.7439e-01 9.7714e-01 7.1720e-02 8.5890e-01 8.6635e-02 +#> 44: 1.0084e+02 -4.0117e+00 -2.4455e+00 -3.9753e+00 -8.8716e-01 -2.0112e-01 5.7105e+00 2.0623e-02 2.6327e-01 1.2741e-01 1.5200e-01 2.6067e-01 9.3289e-01 8.0543e-02 8.5055e-01 8.2921e-02 +#> 45: 1.0071e+02 -3.9996e+00 -2.4359e+00 -3.9764e+00 -9.1082e-01 -2.4578e-01 5.4250e+00 1.9592e-02 2.5011e-01 1.3254e-01 1.6132e-01 2.8273e-01 9.5805e-01 7.7734e-02 7.8171e-01 8.4571e-02 +#> 46: 1.0018e+02 -4.0077e+00 -2.4835e+00 -3.9739e+00 -8.6079e-01 -1.6592e-01 5.1537e+00 1.8613e-02 2.3760e-01 1.3830e-01 1.5392e-01 3.0295e-01 1.0931e+00 7.3274e-02 8.9544e-01 8.8388e-02 +#> 47: 99.9834 -3.9991 -2.5292 -3.9863 -0.8820 -0.0796 4.8960 0.0177 0.2348 0.1376 0.1639 0.2878 0.9864 0.0837 0.9094 0.0832 +#> 48: 99.9155 -4.0224 -2.5422 -3.9854 -0.8719 -0.0750 4.6512 0.0184 0.2251 0.1307 0.1596 0.2734 0.9841 0.0835 0.8696 0.0843 +#> 49: 99.6136 -4.0397 -2.5172 -4.0115 -0.8774 -0.0922 5.2402 0.0175 0.2558 0.1242 0.1551 0.2597 0.9060 0.0816 0.8365 0.0869 +#> 50: 99.4747 -4.0542 -2.4192 -3.9834 -0.9041 -0.1798 4.9782 0.0219 0.2695 0.1234 0.1474 0.2468 0.9269 0.0783 0.8593 0.0854 +#> 51: 99.3401 -4.0386 -2.3951 -3.9661 -0.9181 -0.1887 4.7574 0.0213 0.2746 0.1522 0.1400 0.2344 0.9901 0.0781 0.8863 0.0928 +#> 52: 99.7109 -4.0509 -2.4227 -3.9770 -0.9247 -0.1431 4.9004 0.0203 0.2688 0.1446 0.1330 0.2227 0.8999 0.0791 1.0265 0.0890 +#> 53: 99.6496 -4.0397 -2.4398 -3.9752 -0.9193 -0.2119 5.1106 0.0193 0.2795 0.1527 0.1325 0.2116 0.8949 0.0788 0.9447 0.0872 +#> 54: 99.9071 -4.0211 -2.3887 -3.9812 -0.9233 -0.1946 5.0887 0.0183 0.2763 0.1450 0.1365 0.2010 0.8793 0.0875 0.8643 0.0903 +#> 55: 1.0012e+02 -4.0401e+00 -2.4203e+00 -3.9511e+00 -9.0712e-01 -2.5566e-01 5.7301e+00 1.7375e-02 2.7324e-01 1.3780e-01 1.6204e-01 1.9094e-01 9.7803e-01 7.6146e-02 9.0756e-01 8.7636e-02 +#> 56: 1.0032e+02 -4.0207e+00 -2.4263e+00 -3.9533e+00 -8.7574e-01 -2.3076e-01 6.5321e+00 1.6507e-02 3.0821e-01 1.3091e-01 1.5394e-01 1.8139e-01 8.8520e-01 7.6350e-02 9.2796e-01 8.5283e-02 +#> 57: 1.0028e+02 -4.0037e+00 -2.4301e+00 -3.9655e+00 -8.8472e-01 -1.8969e-01 9.8969e+00 1.5681e-02 2.9280e-01 1.2436e-01 1.4624e-01 1.7232e-01 9.2902e-01 7.4974e-02 8.9204e-01 8.4563e-02 +#> 58: 1.0048e+02 -3.9928e+00 -2.4961e+00 -3.9709e+00 -9.0263e-01 -1.4516e-01 9.4021e+00 1.6151e-02 2.7816e-01 1.1814e-01 1.4165e-01 1.6370e-01 9.5145e-01 8.0233e-02 8.2896e-01 8.3498e-02 +#> 59: 1.0060e+02 -4.0181e+00 -2.4963e+00 -3.9751e+00 -9.0684e-01 -1.1186e-01 8.9320e+00 1.9914e-02 3.0097e-01 1.1224e-01 1.4109e-01 1.5552e-01 9.9121e-01 7.3120e-02 8.6454e-01 8.2239e-02 +#> 60: 1.0047e+02 -3.9976e+00 -2.4797e+00 -3.9780e+00 -8.9328e-01 -1.0814e-01 8.4854e+00 1.8918e-02 3.2275e-01 1.1591e-01 1.3404e-01 1.4774e-01 9.6968e-01 7.4984e-02 8.9831e-01 8.1655e-02 +#> 61: 1.0040e+02 -4.0068e+00 -2.5217e+00 -3.9844e+00 -8.6447e-01 -1.0567e-01 8.0611e+00 1.7972e-02 3.1372e-01 1.1011e-01 1.2973e-01 1.4036e-01 9.1698e-01 7.8118e-02 9.1811e-01 8.4420e-02 +#> 62: 1.0076e+02 -4.0080e+00 -2.4931e+00 -3.9623e+00 -8.9789e-01 -8.3896e-02 7.6580e+00 1.7073e-02 3.0460e-01 1.1254e-01 1.2324e-01 1.3334e-01 9.9032e-01 7.7618e-02 8.3808e-01 8.5031e-02 +#> 63: 1.0064e+02 -4.0129e+00 -2.4731e+00 -3.9561e+00 -8.9103e-01 -8.8987e-02 7.2751e+00 1.6220e-02 2.8944e-01 1.1647e-01 1.4845e-01 1.2667e-01 1.0745e+00 7.6375e-02 8.4316e-01 8.6681e-02 +#> 64: 1.0098e+02 -4.0094e+00 -2.4541e+00 -3.9604e+00 -9.1524e-01 -9.3413e-02 6.9114e+00 1.5409e-02 2.7497e-01 1.2065e-01 1.7095e-01 1.2034e-01 1.0963e+00 7.8304e-02 8.7104e-01 8.5727e-02 +#> 65: 1.0070e+02 -4.0433e+00 -2.4793e+00 -3.9722e+00 -9.3012e-01 -6.5917e-02 6.5658e+00 1.4638e-02 2.7040e-01 1.1462e-01 1.9067e-01 1.1432e-01 9.7444e-01 8.4510e-02 8.7028e-01 8.6292e-02 +#> 66: 1.0049e+02 -4.0656e+00 -2.4659e+00 -3.9898e+00 -9.4278e-01 -7.5929e-02 6.2375e+00 1.3906e-02 2.9347e-01 1.1997e-01 1.8114e-01 1.0860e-01 9.9830e-01 8.0902e-02 9.3551e-01 8.5261e-02 +#> 67: 1.0046e+02 -4.0477e+00 -2.4685e+00 -3.9907e+00 -9.1503e-01 -9.8019e-02 5.9256e+00 1.3211e-02 3.2166e-01 1.1506e-01 1.7208e-01 1.0317e-01 8.6453e-01 9.0533e-02 8.3598e-01 8.6343e-02 +#> 68: 1.0077e+02 -4.0575e+00 -2.4709e+00 -3.9523e+00 -9.2903e-01 -8.1099e-02 5.6294e+00 1.2818e-02 3.1005e-01 1.3665e-01 1.6347e-01 9.8015e-02 9.0181e-01 8.7058e-02 8.4937e-01 8.3248e-02 +#> 69: 1.0086e+02 -4.0626e+00 -2.3922e+00 -3.9557e+00 -9.6741e-01 -3.5986e-02 5.3479e+00 1.2844e-02 3.3024e-01 1.2982e-01 1.5530e-01 9.3115e-02 9.8180e-01 8.3132e-02 8.6549e-01 8.8939e-02 +#> 70: 1.0082e+02 -4.0640e+00 -2.4449e+00 -3.9787e+00 -9.5159e-01 -3.2904e-02 5.0805e+00 1.4346e-02 3.1373e-01 1.2333e-01 1.4754e-01 8.8459e-02 1.0129e+00 7.4856e-02 8.6688e-01 8.4769e-02 +#> 71: 1.0072e+02 -4.0642e+00 -2.5069e+00 -3.9493e+00 -9.3453e-01 -4.4116e-02 4.8265e+00 1.3628e-02 3.0428e-01 1.2122e-01 1.4091e-01 8.4036e-02 1.0454e+00 7.7023e-02 8.9566e-01 8.1639e-02 +#> 72: 1.0049e+02 -4.0609e+00 -2.4472e+00 -3.9669e+00 -9.3972e-01 -7.7498e-02 4.5852e+00 1.4441e-02 3.2552e-01 1.3911e-01 1.4144e-01 8.1899e-02 1.0114e+00 7.7019e-02 8.2312e-01 8.2494e-02 +#> 73: 1.0022e+02 -4.0598e+00 -2.4410e+00 -3.9952e+00 -9.2810e-01 -1.1309e-01 4.3559e+00 1.3719e-02 3.3556e-01 1.3303e-01 1.4990e-01 1.1303e-01 9.6726e-01 7.6776e-02 8.6331e-01 8.3048e-02 +#> 74: 1.0024e+02 -4.0628e+00 -2.4358e+00 -3.9977e+00 -9.1347e-01 -9.1966e-02 4.1381e+00 1.3033e-02 3.4332e-01 1.3418e-01 1.8099e-01 1.0738e-01 1.0158e+00 7.4697e-02 8.6366e-01 8.4370e-02 +#> 75: 99.7847 -4.0500 -2.4401 -4.0018 -0.9252 -0.1013 4.4651 0.0124 0.3365 0.1399 0.1817 0.1020 1.0278 0.0779 0.9008 0.0841 +#> 76: 99.9526 -4.0482 -2.4819 -3.9947 -0.9049 -0.0557 4.2419 0.0126 0.3248 0.1494 0.1726 0.1135 1.0493 0.0778 0.9341 0.0804 +#> 77: 99.9982 -4.0184 -2.4951 -4.0043 -0.8927 -0.0688 5.2538 0.0120 0.3696 0.1419 0.1817 0.1078 1.0402 0.0839 0.9605 0.0848 +#> 78: 1.0007e+02 -4.0210e+00 -2.4725e+00 -4.0040e+00 -8.9827e-01 2.3164e-03 6.4464e+00 1.1395e-02 3.7410e-01 1.3481e-01 2.0294e-01 1.0879e-01 9.7822e-01 8.7445e-02 9.9990e-01 8.2845e-02 +#> 79: 99.3513 -4.0171 -2.5065 -4.0078 -0.8962 -0.0029 7.7527 0.0108 0.3554 0.1281 0.1928 0.1069 1.0455 0.0866 0.9982 0.0870 +#> 80: 98.9945 -4.0172 -2.5412 -4.0341 -0.8891 -0.0187 9.8218 0.0103 0.3376 0.1217 0.1831 0.1457 0.9733 0.0894 1.0164 0.0832 +#> 81: 99.0936 -4.0275 -2.5134 -4.0127 -0.8552 -0.0614 12.1567 0.0098 0.3494 0.1156 0.1740 0.1384 0.9509 0.0843 1.0171 0.0855 +#> 82: 99.2481 -3.9996 -2.4945 -4.0011 -0.8914 -0.0492 11.5489 0.0128 0.3792 0.1098 0.1653 0.1315 0.9915 0.0818 1.0405 0.0928 +#> 83: 99.6941 -3.9998 -2.4851 -3.9845 -0.8802 -0.0560 10.9714 0.0146 0.3602 0.1043 0.1570 0.1249 0.9934 0.0852 0.9707 0.0866 +#> 84: 99.2185 -3.9920 -2.4843 -4.0051 -0.8546 -0.0642 10.4228 0.0153 0.3422 0.0991 0.1492 0.1187 0.9923 0.0833 0.9799 0.0873 +#> 85: 98.8470 -3.9956 -2.4652 -4.0201 -0.8483 -0.0414 9.9017 0.0146 0.3251 0.0941 0.1417 0.1128 0.9732 0.0901 0.9035 0.0858 +#> 86: 98.5012 -3.9841 -2.5148 -4.0250 -0.8408 -0.0551 9.4066 0.0148 0.3088 0.0962 0.1346 0.1071 0.8570 0.0932 0.8532 0.0896 +#> 87: 99.0868 -4.0055 -2.5058 -4.0249 -0.8522 -0.0311 10.3528 0.0175 0.2934 0.1013 0.1411 0.1018 0.8802 0.0838 0.8849 0.0862 +#> 88: 99.5158 -4.0031 -2.4437 -3.9866 -0.8894 -0.0963 9.9832 0.0167 0.3049 0.1030 0.1447 0.0967 0.9955 0.0834 0.8861 0.0893 +#> 89: 99.5538 -4.0347 -2.4494 -4.0213 -0.8695 -0.0494 9.4841 0.0158 0.2897 0.0978 0.1543 0.0918 0.8597 0.0904 0.8959 0.0880 +#> 90: 99.4422 -4.0453 -2.4398 -4.0114 -0.9279 -0.0745 9.8221 0.0150 0.2842 0.0929 0.1466 0.0944 0.9009 0.0871 0.8696 0.0924 +#> 91: 98.8721 -4.0328 -2.4996 -4.0041 -0.8832 -0.0689 9.3310 0.0143 0.2700 0.0896 0.1444 0.1137 0.9567 0.0904 0.8680 0.0891 +#> 92: 99.8390 -4.0418 -2.4914 -4.0182 -0.9279 -0.0460 10.9801 0.0136 0.2585 0.0949 0.1461 0.1210 1.0043 0.0908 0.8310 0.0939 +#> 93: 1.0029e+02 -4.0313e+00 -2.4620e+00 -4.0187e+00 -8.9083e-01 -1.0908e-01 1.0431e+01 1.2890e-02 2.4559e-01 9.5757e-02 1.3878e-01 1.1565e-01 9.9174e-01 9.0056e-02 8.9538e-01 8.8925e-02 +#> 94: 99.3285 -4.0295 -2.4523 -4.0235 -0.8828 -0.1190 10.9003 0.0137 0.2333 0.0915 0.1318 0.1212 1.0729 0.0779 0.9543 0.0907 +#> 95: 99.4117 -4.0422 -2.3807 -4.0870 -0.8960 -0.0889 10.3553 0.0130 0.2216 0.0870 0.1253 0.1366 0.9127 0.0864 0.8901 0.0911 +#> 96: 99.3348 -4.0401 -2.4009 -4.0698 -0.8730 -0.0622 9.8375 0.0123 0.2106 0.0826 0.1241 0.1297 0.8504 0.0836 0.9140 0.0881 +#> 97: 99.4898 -4.0419 -2.4310 -4.0589 -0.8932 -0.0634 9.3456 0.0132 0.2000 0.0785 0.1224 0.1233 0.8770 0.0836 0.8715 0.0837 +#> 98: 99.3750 -4.0704 -2.4353 -4.0616 -0.9333 -0.0846 8.8783 0.0136 0.1900 0.0746 0.1245 0.1171 0.8907 0.0838 0.9066 0.0832 +#> 99: 99.6234 -4.0366 -2.3740 -4.0657 -0.9242 -0.0675 8.4344 0.0129 0.1805 0.0708 0.1182 0.1112 0.8814 0.0808 0.9511 0.0863 +#> 100: 1.0025e+02 -4.0420e+00 -2.3557e+00 -4.0579e+00 -9.5051e-01 -6.3418e-02 8.0319e+00 1.2286e-02 1.7150e-01 6.7291e-02 1.1232e-01 1.0568e-01 8.5851e-01 8.7881e-02 8.9363e-01 8.5897e-02 +#> 101: 1.0041e+02 -4.0461e+00 -2.3840e+00 -4.0384e+00 -9.3752e-01 -7.7594e-02 9.5649e+00 1.1672e-02 1.7509e-01 6.3926e-02 1.2760e-01 1.0039e-01 8.6733e-01 8.2748e-02 9.6277e-01 8.4274e-02 +#> 102: 1.0095e+02 -4.0372e+00 -2.3633e+00 -4.0286e+00 -9.1961e-01 -6.5350e-02 1.1428e+01 1.1088e-02 1.8557e-01 6.0730e-02 1.3211e-01 9.5374e-02 9.3928e-01 8.0161e-02 9.7913e-01 8.4081e-02 +#> 103: 1.0019e+02 -4.0236e+00 -2.4105e+00 -4.0337e+00 -9.1362e-01 -7.3859e-02 1.0856e+01 1.0534e-02 1.7629e-01 5.7693e-02 1.2695e-01 9.1362e-02 9.8491e-01 8.1430e-02 9.7682e-01 8.2250e-02 +#> 104: 99.7755 -4.0280 -2.4452 -4.0197 -0.9112 -0.0810 11.0317 0.0100 0.1796 0.0548 0.1301 0.0868 0.9418 0.0816 0.9170 0.0806 +#> 105: 1.0010e+02 -4.0418e+00 -2.4294e+00 -4.0225e+00 -9.1111e-01 -8.9920e-02 1.0480e+01 9.5070e-03 1.7060e-01 5.2068e-02 1.3987e-01 8.2454e-02 9.1944e-01 7.8110e-02 8.9266e-01 8.7228e-02 +#> 106: 1.0025e+02 -4.0507e+00 -2.4134e+00 -4.0343e+00 -9.0244e-01 -8.4683e-02 1.3506e+01 9.0316e-03 1.6207e-01 4.9465e-02 1.5337e-01 7.8331e-02 9.9609e-01 8.4473e-02 8.7046e-01 8.5479e-02 +#> 107: 1.0014e+02 -4.0468e+00 -2.3972e+00 -4.0196e+00 -9.3650e-01 -2.4087e-02 1.2830e+01 8.5801e-03 1.6027e-01 4.6992e-02 1.5429e-01 8.2493e-02 9.8959e-01 8.2626e-02 8.3427e-01 8.8197e-02 +#> 108: 1.0114e+02 -4.0338e+00 -2.4307e+00 -4.0724e+00 -9.1363e-01 1.1952e-02 1.2189e+01 8.1511e-03 1.5563e-01 4.4854e-02 1.7315e-01 7.8368e-02 9.8589e-01 7.8130e-02 9.0460e-01 8.2870e-02 +#> 109: 1.0066e+02 -4.0550e+00 -2.4094e+00 -4.0641e+00 -9.0945e-01 -1.5401e-03 1.3149e+01 7.7435e-03 1.4785e-01 4.2612e-02 1.7232e-01 7.4450e-02 1.0942e+00 7.4816e-02 9.1706e-01 8.5333e-02 +#> 110: 1.0111e+02 -4.0266e+00 -2.4047e+00 -4.0646e+00 -9.0541e-01 -1.7212e-02 1.2492e+01 7.3563e-03 1.4046e-01 4.0481e-02 1.8132e-01 7.0727e-02 1.0508e+00 7.9457e-02 9.8990e-01 8.2975e-02 +#> 111: 1.0155e+02 -4.0274e+00 -2.3645e+00 -4.0663e+00 -9.4902e-01 -1.8882e-02 1.1867e+01 8.7757e-03 1.4436e-01 3.8457e-02 1.7225e-01 6.7191e-02 1.0217e+00 7.7437e-02 9.9196e-01 8.1580e-02 +#> 112: 1.0209e+02 -4.0230e+00 -2.3938e+00 -4.0375e+00 -9.5447e-01 -5.0888e-02 1.4321e+01 8.3370e-03 1.4863e-01 3.6534e-02 1.6778e-01 8.2186e-02 9.3085e-01 8.3291e-02 9.8775e-01 7.9492e-02 +#> 113: 1.0188e+02 -4.0173e+00 -2.3804e+00 -4.0403e+00 -9.6152e-01 -7.7453e-02 1.3605e+01 7.9201e-03 1.5060e-01 3.4708e-02 1.7341e-01 8.4506e-02 9.0783e-01 8.7383e-02 9.4854e-01 8.2648e-02 +#> 114: 1.0239e+02 -4.0081e+00 -2.3724e+00 -4.0332e+00 -9.4315e-01 -7.4933e-02 1.2925e+01 7.5241e-03 1.4307e-01 3.2972e-02 1.6695e-01 8.0281e-02 9.2775e-01 8.4314e-02 9.6195e-01 7.9448e-02 +#> 115: 1.0199e+02 -4.0127e+00 -2.3773e+00 -4.0472e+00 -9.5157e-01 -2.0947e-02 1.2279e+01 7.4483e-03 1.3592e-01 3.1324e-02 1.6705e-01 7.6267e-02 9.4956e-01 7.6989e-02 1.0340e+00 8.5564e-02 +#> 116: 1.0122e+02 -4.0264e+00 -2.4014e+00 -4.0509e+00 -9.1462e-01 -2.3511e-02 1.1665e+01 7.0759e-03 1.2912e-01 2.9757e-02 1.5870e-01 7.2453e-02 9.3580e-01 8.2952e-02 9.3341e-01 8.3302e-02 +#> 117: 1.0112e+02 -4.0326e+00 -2.4093e+00 -4.0559e+00 -8.9743e-01 -2.0572e-02 1.1082e+01 6.7221e-03 1.2266e-01 2.8269e-02 1.5339e-01 6.8831e-02 9.0879e-01 8.4441e-02 9.1432e-01 8.0538e-02 +#> 118: 1.0123e+02 -4.0411e+00 -2.4077e+00 -4.0556e+00 -9.2971e-01 -2.1885e-02 1.0528e+01 6.3860e-03 1.1653e-01 3.3123e-02 1.6947e-01 6.5389e-02 9.7140e-01 8.6671e-02 8.9874e-01 8.1670e-02 +#> 119: 1.0098e+02 -4.0538e+00 -2.3515e+00 -4.0607e+00 -9.5433e-01 -7.5743e-02 1.0001e+01 6.0667e-03 1.1070e-01 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-1.6803e-01 6.4919e+00 3.4600e-03 1.1783e-01 2.2228e-02 1.8333e-01 6.9284e-02 9.4846e-01 8.3087e-02 9.7774e-01 8.2610e-02 +#> 136: 1.0177e+02 -4.0861e+00 -2.2785e+00 -3.9890e+00 -9.9625e-01 -1.8938e-01 6.1673e+00 3.2870e-03 1.1752e-01 2.1116e-02 1.8815e-01 6.5820e-02 9.3634e-01 8.5255e-02 1.1001e+00 8.5332e-02 +#> 137: 1.0200e+02 -4.0928e+00 -2.1946e+00 -3.9974e+00 -1.0098e+00 -1.8810e-01 5.8589e+00 3.1227e-03 1.2394e-01 2.1203e-02 1.7874e-01 7.2232e-02 1.0048e+00 7.3422e-02 1.0222e+00 8.3484e-02 +#> 138: 1.0214e+02 -4.0820e+00 -2.2052e+00 -3.9737e+00 -1.0420e+00 -2.0594e-01 5.5660e+00 3.8937e-03 1.9164e-01 2.0143e-02 1.6980e-01 6.8621e-02 1.0126e+00 7.6106e-02 1.0780e+00 8.2960e-02 +#> 139: 1.0249e+02 -4.0785e+00 -2.1649e+00 -3.9567e+00 -1.0095e+00 -2.8807e-01 5.2877e+00 3.6990e-03 1.8647e-01 1.9135e-02 1.6131e-01 6.5190e-02 1.0030e+00 7.9858e-02 1.0611e+00 8.4109e-02 +#> 140: 1.0184e+02 -4.0847e+00 -2.1800e+00 -3.9565e+00 -9.9415e-01 -2.8869e-01 5.0233e+00 4.0857e-03 1.9502e-01 1.8179e-02 1.6676e-01 6.2879e-02 9.5962e-01 7.8117e-02 9.9649e-01 8.4914e-02 +#> 141: 1.0195e+02 -4.1012e+00 -2.1831e+00 -3.9488e+00 -9.9515e-01 -3.1864e-01 4.7721e+00 3.8814e-03 1.8527e-01 1.7270e-02 1.6797e-01 6.1084e-02 9.0969e-01 8.2722e-02 1.0122e+00 8.2518e-02 +#> 142: 1.0233e+02 -4.1139e+00 -2.1692e+00 -3.9542e+00 -1.0023e+00 -3.3242e-01 4.5335e+00 3.6873e-03 2.0662e-01 1.6406e-02 1.5957e-01 5.8030e-02 9.4761e-01 8.4629e-02 1.0342e+00 8.3954e-02 +#> 143: 1.0217e+02 -4.1103e+00 -2.1380e+00 -3.9511e+00 -1.0300e+00 -2.5992e-01 5.2035e+00 4.7053e-03 1.9629e-01 1.5586e-02 1.5979e-01 5.5128e-02 8.9255e-01 7.9042e-02 1.0461e+00 8.6952e-02 +#> 144: 1.0185e+02 -4.1335e+00 -2.1911e+00 -3.9650e+00 -1.0440e+00 -2.4451e-01 5.0998e+00 4.4700e-03 1.8648e-01 1.9590e-02 1.5534e-01 5.2372e-02 9.7863e-01 8.3932e-02 1.0197e+00 8.7673e-02 +#> 145: 1.0242e+02 -4.1445e+00 -2.1203e+00 -3.9616e+00 -1.0426e+00 -2.7120e-01 4.8448e+00 4.2465e-03 1.7715e-01 1.8611e-02 1.4757e-01 4.9753e-02 1.0024e+00 8.4131e-02 1.0768e+00 8.5388e-02 +#> 146: 1.0236e+02 -4.1519e+00 -2.1958e+00 -3.9779e+00 -9.8615e-01 -2.5863e-01 4.6026e+00 4.0718e-03 1.6829e-01 1.7680e-02 1.6407e-01 4.7266e-02 1.0740e+00 8.2413e-02 1.0706e+00 8.3410e-02 +#> 147: 1.0251e+02 -4.1465e+00 -2.2042e+00 -3.9775e+00 -1.0317e+00 -2.2757e-01 4.3725e+00 3.8682e-03 1.5988e-01 1.6796e-02 1.7016e-01 4.4902e-02 9.7748e-01 8.3376e-02 1.0880e+00 8.1968e-02 +#> 148: 1.0244e+02 -4.1432e+00 -2.1786e+00 -3.9792e+00 -1.0442e+00 -2.2002e-01 4.9671e+00 3.6748e-03 1.5189e-01 1.5956e-02 2.2196e-01 4.2657e-02 1.0412e+00 7.8051e-02 1.1051e+00 8.1618e-02 +#> 149: 1.0219e+02 -4.1384e+00 -2.2318e+00 -3.9757e+00 -1.0438e+00 -2.4124e-01 4.7187e+00 3.4910e-03 1.4429e-01 1.6061e-02 2.1086e-01 4.0524e-02 1.0082e+00 8.0377e-02 1.1455e+00 8.0545e-02 +#> 150: 1.0264e+02 -4.1498e+00 -2.2352e+00 -3.9915e+00 -1.0669e+00 -2.1255e-01 4.4828e+00 3.3165e-03 1.3708e-01 1.7218e-02 2.0032e-01 3.8498e-02 9.5031e-01 8.7248e-02 9.8770e-01 8.3250e-02 +#> 151: 1.0250e+02 -4.1365e+00 -2.1876e+00 -3.9939e+00 -1.0568e+00 -1.8159e-01 4.2587e+00 3.1507e-03 1.3022e-01 1.7383e-02 1.9030e-01 3.6573e-02 9.6938e-01 8.0203e-02 1.0578e+00 8.3430e-02 +#> 152: 1.0256e+02 -4.1370e+00 -2.2238e+00 -4.0047e+00 -1.0406e+00 -1.8764e-01 1.9609e+00 1.4191e-03 1.1882e-01 1.7924e-02 1.6889e-01 4.1216e-02 9.1972e-01 7.8573e-02 1.0717e+00 8.0882e-02 +#> 153: 1.0219e+02 -4.1299e+00 -2.2139e+00 -3.9917e+00 -9.9964e-01 -2.0505e-01 1.8258e+00 1.0432e-03 8.4660e-02 2.1446e-02 1.7634e-01 3.5573e-02 9.3702e-01 8.4860e-02 1.0145e+00 8.3329e-02 +#> 154: 1.0199e+02 -4.1354e+00 -2.2231e+00 -3.9779e+00 -1.0155e+00 -2.2573e-01 2.6463e+00 5.8153e-04 8.8101e-02 2.3167e-02 1.6103e-01 3.3874e-02 9.5360e-01 8.6215e-02 9.5723e-01 8.4603e-02 +#> 155: 1.0234e+02 -4.1239e+00 -2.2137e+00 -3.9802e+00 -1.0070e+00 -2.3158e-01 2.9697e+00 6.6709e-04 1.1190e-01 2.0949e-02 1.8298e-01 3.1557e-02 9.2910e-01 8.2509e-02 9.8680e-01 8.5206e-02 +#> 156: 1.0253e+02 -4.1269e+00 -2.2370e+00 -3.9682e+00 -1.0420e+00 -2.1219e-01 2.7267e+00 6.8451e-04 8.9651e-02 2.4380e-02 1.6613e-01 3.4846e-02 9.3608e-01 8.7506e-02 9.0446e-01 8.1755e-02 +#> 157: 1.0265e+02 -4.1241e+00 -2.2179e+00 -3.9676e+00 -1.0308e+00 -2.2480e-01 2.1278e+00 4.9811e-04 6.7161e-02 1.9758e-02 1.5607e-01 4.4198e-02 9.4162e-01 8.7311e-02 9.9147e-01 7.9857e-02 +#> 158: 1.0239e+02 -4.1219e+00 -2.1615e+00 -3.9781e+00 -1.0384e+00 -2.6750e-01 2.5310e+00 4.8270e-04 6.5662e-02 1.8085e-02 1.7665e-01 4.4020e-02 8.8632e-01 8.6004e-02 1.0425e+00 8.2894e-02 +#> 159: 1.0270e+02 -4.1204e+00 -2.1837e+00 -3.9530e+00 -1.0587e+00 -2.5809e-01 3.4348e+00 5.6788e-04 6.5500e-02 1.9540e-02 1.8629e-01 4.0730e-02 9.5079e-01 8.2399e-02 9.9316e-01 8.3381e-02 +#> 160: 1.0282e+02 -4.1223e+00 -2.1325e+00 -3.9734e+00 -1.0068e+00 -2.8751e-01 3.9652e+00 7.6565e-04 8.5246e-02 1.7068e-02 1.7587e-01 3.0778e-02 9.1802e-01 8.0158e-02 9.9642e-01 8.1564e-02 +#> 161: 1.0330e+02 -4.1180e+00 -2.1879e+00 -3.9743e+00 -1.0268e+00 -2.8812e-01 4.9153e+00 5.8033e-04 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8.9386e-01 9.2727e-02 9.4636e-01 8.4238e-02 +#> 167: 1.0187e+02 -4.1149e+00 -2.2185e+00 -3.9708e+00 -1.0036e+00 -2.5751e-01 1.5355e+00 4.0974e-05 9.9346e-02 2.2030e-02 2.1916e-01 2.6726e-02 9.1055e-01 8.1030e-02 1.0098e+00 7.9180e-02 +#> 168: 1.0172e+02 -4.1167e+00 -2.2673e+00 -3.9702e+00 -9.8388e-01 -2.1404e-01 1.4836e+00 2.7779e-05 7.7509e-02 2.9513e-02 1.9543e-01 3.4526e-02 1.0152e+00 8.1248e-02 9.7482e-01 8.0746e-02 +#> 169: 1.0175e+02 -4.1171e+00 -2.2634e+00 -3.9701e+00 -9.5962e-01 -2.4130e-01 1.4263e+00 4.7370e-05 5.0986e-02 2.8211e-02 2.2554e-01 3.9909e-02 9.8519e-01 7.8842e-02 1.0023e+00 8.5684e-02 +#> 170: 1.0177e+02 -4.1189e+00 -2.2417e+00 -3.9834e+00 -1.0059e+00 -2.6551e-01 9.9010e-01 3.7247e-05 4.2517e-02 2.9791e-02 1.8705e-01 4.2435e-02 9.6604e-01 8.8427e-02 9.6699e-01 8.3986e-02 +#> 171: 1.0182e+02 -4.1187e+00 -2.2464e+00 -3.9953e+00 -9.8154e-01 -2.5146e-01 7.4179e-01 3.2420e-05 5.0690e-02 3.0483e-02 1.7888e-01 6.3177e-02 9.2784e-01 8.4814e-02 1.0018e+00 8.4070e-02 +#> 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-2.3051e+00 -4.0367e+00 -1.0252e+00 -6.9200e-02 9.1625e-02 6.6068e-07 4.7665e-02 2.8907e-02 1.8679e-01 7.5787e-02 9.0272e-01 8.8077e-02 9.2929e-01 8.0385e-02 +#> 199: 1.0192e+02 -4.1204e+00 -2.3163e+00 -4.0506e+00 -1.0152e+00 -5.3872e-02 6.8196e-02 5.5789e-07 6.0471e-02 3.1730e-02 2.0053e-01 6.8557e-02 9.0478e-01 8.5910e-02 9.3814e-01 8.2211e-02 +#> 200: 1.0195e+02 -4.1205e+00 -2.3141e+00 -4.0728e+00 -1.0010e+00 -2.5675e-03 6.5235e-02 6.9762e-07 5.8458e-02 2.8504e-02 2.0377e-01 4.9513e-02 8.5640e-01 8.6640e-02 9.5731e-01 8.4390e-02 +#> 201: 1.0195e+02 -4.1205e+00 -2.3106e+00 -4.0774e+00 -9.9012e-01 5.1724e-03 5.1225e-02 5.4222e-07 6.0577e-02 3.3554e-02 2.0505e-01 4.4738e-02 8.8073e-01 8.5488e-02 9.6928e-01 8.4895e-02 +#> 202: 1.0194e+02 -4.1205e+00 -2.3078e+00 -4.0767e+00 -9.9283e-01 3.9328e-03 4.5461e-02 4.8520e-07 6.7405e-02 3.4599e-02 2.1312e-01 4.6664e-02 9.0528e-01 8.4189e-02 9.8043e-01 8.5266e-02 +#> 203: 1.0193e+02 -4.1205e+00 -2.3029e+00 -4.0790e+00 -9.8990e-01 -9.1380e-03 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2.0126e-01 6.0353e-02 9.2010e-01 8.5244e-02 9.8002e-01 8.2975e-02 +#> 209: 1.0191e+02 -4.1205e+00 -2.3279e+00 -4.0726e+00 -1.0053e+00 -1.5390e-02 4.1064e-02 5.3171e-07 5.8749e-02 3.4510e-02 1.9942e-01 6.3063e-02 9.3192e-01 8.4436e-02 9.8298e-01 8.3187e-02 +#> 210: 1.0191e+02 -4.1205e+00 -2.3310e+00 -4.0705e+00 -1.0061e+00 -1.3507e-02 3.8265e-02 5.2762e-07 5.9344e-02 3.3374e-02 1.9612e-01 6.7006e-02 9.3199e-01 8.4573e-02 9.8382e-01 8.3227e-02 +#> 211: 1.0191e+02 -4.1205e+00 -2.3383e+00 -4.0683e+00 -1.0043e+00 -1.3973e-02 3.6076e-02 5.2584e-07 6.1568e-02 3.2369e-02 1.9504e-01 6.9982e-02 9.4179e-01 8.4625e-02 9.9145e-01 8.3067e-02 +#> 212: 1.0192e+02 -4.1204e+00 -2.3396e+00 -4.0662e+00 -1.0055e+00 -1.8011e-02 3.4746e-02 5.4375e-07 6.2747e-02 3.1588e-02 1.9405e-01 7.2360e-02 9.4525e-01 8.4466e-02 9.9581e-01 8.2952e-02 +#> 213: 1.0192e+02 -4.1204e+00 -2.3407e+00 -4.0649e+00 -1.0066e+00 -2.1077e-02 3.4708e-02 5.5843e-07 6.1940e-02 3.0715e-02 1.9382e-01 7.4602e-02 9.4611e-01 8.4322e-02 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9.4494e-02 9.3151e-01 8.2910e-02 9.9484e-01 8.2760e-02 +#> 309: 1.0188e+02 -4.1204e+00 -2.3530e+00 -4.0685e+00 -1.0107e+00 1.0089e-02 3.6382e-02 5.1081e-07 8.3917e-02 2.2276e-02 1.8728e-01 9.4285e-02 9.3133e-01 8.2875e-02 9.9545e-01 8.2757e-02 +#> 310: 1.0188e+02 -4.1204e+00 -2.3533e+00 -4.0685e+00 -1.0105e+00 1.0805e-02 3.6375e-02 5.1041e-07 8.4245e-02 2.2246e-02 1.8753e-01 9.3894e-02 9.3052e-01 8.2899e-02 9.9500e-01 8.2743e-02 +#> 311: 1.0188e+02 -4.1204e+00 -2.3534e+00 -4.0685e+00 -1.0103e+00 1.1449e-02 3.6311e-02 5.0884e-07 8.4434e-02 2.2231e-02 1.8783e-01 9.3542e-02 9.3039e-01 8.2864e-02 9.9458e-01 8.2733e-02 +#> 312: 1.0188e+02 -4.1204e+00 -2.3535e+00 -4.0685e+00 -1.0102e+00 1.2173e-02 3.6373e-02 5.0821e-07 8.4730e-02 2.2176e-02 1.8769e-01 9.3317e-02 9.2982e-01 8.2916e-02 9.9438e-01 8.2740e-02 +#> 313: 1.0188e+02 -4.1204e+00 -2.3533e+00 -4.0688e+00 -1.0103e+00 1.2812e-02 3.6558e-02 5.0751e-07 8.5211e-02 2.2131e-02 1.8754e-01 9.3387e-02 9.2962e-01 8.2892e-02 9.9458e-01 8.2741e-02 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9.8997e-01 8.2681e-02 +#> 367: 1.0187e+02 -4.1204e+00 -2.3533e+00 -4.0649e+00 -1.0088e+00 5.8610e-03 3.6366e-02 5.0123e-07 9.0732e-02 2.2245e-02 1.8793e-01 9.4666e-02 9.3556e-01 8.2339e-02 9.8945e-01 8.2691e-02 +#> 368: 1.0187e+02 -4.1204e+00 -2.3531e+00 -4.0650e+00 -1.0088e+00 6.1043e-03 3.6424e-02 5.0107e-07 9.0729e-02 2.2248e-02 1.8780e-01 9.4599e-02 9.3554e-01 8.2315e-02 9.8903e-01 8.2705e-02 +#> 369: 1.0187e+02 -4.1204e+00 -2.3530e+00 -4.0650e+00 -1.0088e+00 6.1767e-03 3.6436e-02 5.0046e-07 9.0617e-02 2.2226e-02 1.8787e-01 9.4410e-02 9.3504e-01 8.2361e-02 9.8843e-01 8.2694e-02 +#> 370: 1.0187e+02 -4.1204e+00 -2.3528e+00 -4.0651e+00 -1.0088e+00 6.2532e-03 3.6467e-02 5.0024e-07 9.0741e-02 2.2223e-02 1.8794e-01 9.4288e-02 9.3472e-01 8.2374e-02 9.8781e-01 8.2703e-02 +#> 371: 1.0186e+02 -4.1204e+00 -2.3525e+00 -4.0652e+00 -1.0088e+00 6.2117e-03 3.6465e-02 4.9964e-07 9.0904e-02 2.2220e-02 1.8788e-01 9.4310e-02 9.3470e-01 8.2367e-02 9.8730e-01 8.2731e-02 +#> 372: 1.0186e+02 -4.1204e+00 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1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0629e+00 -1.0096e+00 -9.5438e-04 3.6869e-02 5.1330e-07 9.2176e-02 2.3050e-02 1.8712e-01 9.4766e-02 9.3666e-01 8.2080e-02 9.7996e-01 8.2691e-02 +#> 473: 1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0630e+00 -1.0096e+00 -8.2178e-04 3.6877e-02 5.1283e-07 9.2191e-02 2.3021e-02 1.8703e-01 9.4747e-02 9.3670e-01 8.2072e-02 9.8007e-01 8.2693e-02 +#> 474: 1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0630e+00 -1.0096e+00 -7.0189e-04 3.6927e-02 5.1196e-07 9.2195e-02 2.2989e-02 1.8702e-01 9.4746e-02 9.3669e-01 8.2054e-02 9.8029e-01 8.2689e-02 +#> 475: 1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0630e+00 -1.0095e+00 -7.1989e-04 3.6993e-02 5.1125e-07 9.2159e-02 2.2963e-02 1.8700e-01 9.4813e-02 9.3681e-01 8.2051e-02 9.8027e-01 8.2680e-02 +#> 476: 1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0629e+00 -1.0096e+00 -7.1806e-04 3.7018e-02 5.1105e-07 9.2091e-02 2.2933e-02 1.8696e-01 9.4837e-02 9.3713e-01 8.2067e-02 9.8033e-01 8.2674e-02 +#> 477: 1.0186e+02 -4.1205e+00 -2.3479e+00 -4.0630e+00 -1.0097e+00 -7.3438e-04 3.6986e-02 5.1121e-07 9.2046e-02 2.2909e-02 1.8698e-01 9.4809e-02 9.3743e-01 8.2059e-02 9.8045e-01 8.2693e-02 +#> 478: 1.0186e+02 -4.1205e+00 -2.3478e+00 -4.0630e+00 -1.0096e+00 -7.9338e-04 3.6912e-02 5.1224e-07 9.2056e-02 2.2881e-02 1.8698e-01 9.4791e-02 9.3757e-01 8.2042e-02 9.8072e-01 8.2682e-02 +#> 479: 1.0186e+02 -4.1205e+00 -2.3476e+00 -4.0629e+00 -1.0096e+00 -8.6158e-04 3.6882e-02 5.1284e-07 9.2159e-02 2.2867e-02 1.8694e-01 9.4774e-02 9.3749e-01 8.2051e-02 9.8088e-01 8.2679e-02 +#> 480: 1.0186e+02 -4.1205e+00 -2.3474e+00 -4.0629e+00 -1.0096e+00 -1.1334e-03 3.6851e-02 5.1423e-07 9.2253e-02 2.2869e-02 1.8696e-01 9.4820e-02 9.3751e-01 8.2063e-02 9.8097e-01 8.2693e-02 +#> 481: 1.0186e+02 -4.1205e+00 -2.3470e+00 -4.0629e+00 -1.0096e+00 -1.2444e-03 3.6785e-02 5.1490e-07 9.2397e-02 2.2853e-02 1.8694e-01 9.4838e-02 9.3770e-01 8.2031e-02 9.8124e-01 8.2707e-02 +#> 482: 1.0186e+02 -4.1205e+00 -2.3467e+00 -4.0629e+00 -1.0095e+00 -1.3612e-03 3.6750e-02 5.1658e-07 9.2440e-02 2.2842e-02 1.8683e-01 9.4800e-02 9.3786e-01 8.2041e-02 9.8107e-01 8.2719e-02 +#> 483: 1.0186e+02 -4.1205e+00 -2.3467e+00 -4.0628e+00 -1.0096e+00 -1.5168e-03 3.6783e-02 5.1708e-07 9.2590e-02 2.2804e-02 1.8674e-01 9.4804e-02 9.3790e-01 8.2042e-02 9.8116e-01 8.2719e-02 +#> 484: 1.0186e+02 -4.1205e+00 -2.3466e+00 -4.0628e+00 -1.0097e+00 -1.5218e-03 3.6848e-02 5.1669e-07 9.2717e-02 2.2775e-02 1.8670e-01 9.4940e-02 9.3798e-01 8.2028e-02 9.8106e-01 8.2719e-02 +#> 485: 1.0186e+02 -4.1205e+00 -2.3464e+00 -4.0628e+00 -1.0097e+00 -1.4177e-03 3.6867e-02 5.1615e-07 9.2806e-02 2.2765e-02 1.8669e-01 9.5018e-02 9.3816e-01 8.2020e-02 9.8090e-01 8.2721e-02 +#> 486: 1.0186e+02 -4.1205e+00 -2.3462e+00 -4.0628e+00 -1.0098e+00 -1.5257e-03 3.6968e-02 5.1513e-07 9.3019e-02 2.2762e-02 1.8663e-01 9.5111e-02 9.3816e-01 8.2013e-02 9.8071e-01 8.2732e-02 +#> 487: 1.0186e+02 -4.1205e+00 -2.3460e+00 -4.0628e+00 -1.0097e+00 -1.7055e-03 3.7021e-02 5.1446e-07 9.3161e-02 2.2732e-02 1.8652e-01 9.5373e-02 9.3832e-01 8.1997e-02 9.8078e-01 8.2737e-02 +#> 488: 1.0186e+02 -4.1205e+00 -2.3459e+00 -4.0628e+00 -1.0097e+00 -1.8502e-03 3.7069e-02 5.1391e-07 9.3282e-02 2.2741e-02 1.8641e-01 9.5414e-02 9.3818e-01 8.2001e-02 9.8064e-01 8.2738e-02 +#> 489: 1.0186e+02 -4.1205e+00 -2.3458e+00 -4.0628e+00 -1.0097e+00 -1.9091e-03 3.7017e-02 5.1291e-07 9.3286e-02 2.2738e-02 1.8639e-01 9.5453e-02 9.3808e-01 8.1991e-02 9.8047e-01 8.2728e-02 +#> 490: 1.0186e+02 -4.1205e+00 -2.3456e+00 -4.0628e+00 -1.0097e+00 -1.8766e-03 3.6969e-02 5.1220e-07 9.3297e-02 2.2728e-02 1.8635e-01 9.5468e-02 9.3793e-01 8.1988e-02 9.8034e-01 8.2726e-02 +#> 491: 1.0186e+02 -4.1205e+00 -2.3457e+00 -4.0627e+00 -1.0097e+00 -1.7736e-03 3.6915e-02 5.1153e-07 9.3298e-02 2.2716e-02 1.8634e-01 9.5548e-02 9.3772e-01 8.2005e-02 9.8025e-01 8.2722e-02 +#> 492: 1.0186e+02 -4.1205e+00 -2.3457e+00 -4.0627e+00 -1.0097e+00 -1.7747e-03 3.6877e-02 5.1077e-07 9.3336e-02 2.2697e-02 1.8635e-01 9.5593e-02 9.3778e-01 8.2001e-02 9.8013e-01 8.2725e-02 +#> 493: 1.0186e+02 -4.1205e+00 -2.3456e+00 -4.0628e+00 -1.0094e+00 -1.6324e-03 3.6857e-02 5.1020e-07 9.3348e-02 2.2668e-02 1.8636e-01 9.5735e-02 9.3764e-01 8.1984e-02 9.8019e-01 8.2723e-02 +#> 494: 1.0186e+02 -4.1205e+00 -2.3456e+00 -4.0629e+00 -1.0094e+00 -1.5393e-03 3.6842e-02 5.1022e-07 9.3359e-02 2.2649e-02 1.8637e-01 9.5812e-02 9.3739e-01 8.1982e-02 9.8033e-01 8.2708e-02 +#> 495: 1.0186e+02 -4.1205e+00 -2.3456e+00 -4.0629e+00 -1.0094e+00 -1.5166e-03 3.6841e-02 5.1004e-07 9.3321e-02 2.2642e-02 1.8640e-01 9.5849e-02 9.3716e-01 8.1979e-02 9.8016e-01 8.2700e-02 +#> 496: 1.0186e+02 -4.1205e+00 -2.3456e+00 -4.0630e+00 -1.0095e+00 -1.4947e-03 3.6841e-02 5.0969e-07 9.3236e-02 2.2646e-02 1.8640e-01 9.5916e-02 9.3719e-01 8.1963e-02 9.8028e-01 8.2702e-02 +#> 497: 1.0186e+02 -4.1205e+00 -2.3457e+00 -4.0629e+00 -1.0094e+00 -1.4507e-03 3.6827e-02 5.0937e-07 9.3185e-02 2.2663e-02 1.8638e-01 9.5991e-02 9.3707e-01 8.1954e-02 9.8047e-01 8.2718e-02 +#> 498: 1.0186e+02 -4.1205e+00 -2.3459e+00 -4.0630e+00 -1.0094e+00 -1.2569e-03 3.6805e-02 5.0854e-07 9.3089e-02 2.2677e-02 1.8634e-01 9.5931e-02 9.3719e-01 8.1952e-02 9.8051e-01 8.2718e-02 +#> 499: 1.0186e+02 -4.1205e+00 -2.3460e+00 -4.0630e+00 -1.0093e+00 -1.0466e-03 3.6769e-02 5.0789e-07 9.3029e-02 2.2690e-02 1.8631e-01 9.5862e-02 9.3729e-01 8.1956e-02 9.8046e-01 8.2731e-02 +#> 500: 1.0186e+02 -4.1205e+00 -2.3464e+00 -4.0630e+00 -1.0093e+00 -7.3346e-04 3.6766e-02 5.0769e-07 9.3093e-02 2.2701e-02 1.8633e-01 9.5687e-02 9.3739e-01 8.1977e-02 9.8039e-01 8.2728e-02</div><div class='output co'>#> <span class='message'>Calculating covariance matrix</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT"</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='input'><span class='co'># The following takes a very long time but gives</span> <span class='va'>f_nlmixr_dfop_sfo_focei</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> -#> <span class='message'>cmt(m1);</span> -#> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> -#> <span class='message'>parent(0)=rx_expr_6;</span> -#> <span class='message'>rx_expr_7~ETA[4]+THETA[4];</span> -#> <span class='message'>rx_expr_8~ETA[6]+THETA[6];</span> -#> <span class='message'>rx_expr_9~ETA[5]+THETA[5];</span> -#> <span class='message'>rx_expr_12~exp(rx_expr_7);</span> -#> <span class='message'>rx_expr_13~exp(rx_expr_9);</span> -#> <span class='message'>rx_expr_15~t*rx_expr_12;</span> -#> <span class='message'>rx_expr_16~t*rx_expr_13;</span> -#> <span class='message'>rx_expr_19~exp(-(rx_expr_8));</span> -#> <span class='message'>rx_expr_21~1+rx_expr_19;</span> -#> <span class='message'>rx_expr_26~1/(rx_expr_21);</span> -#> <span class='message'>rx_expr_28~(rx_expr_26);</span> -#> <span class='message'>rx_expr_29~1-rx_expr_28;</span> -#> <span class='message'>d/dt(parent)=-parent*(exp(rx_expr_7-rx_expr_15)/(rx_expr_21)+exp(rx_expr_9-rx_expr_16)*(rx_expr_29))/(exp(-t*rx_expr_12)/(rx_expr_21)+exp(-t*rx_expr_13)*(rx_expr_29));</span> -#> <span class='message'>rx_expr_10~ETA[2]+THETA[2];</span> -#> <span class='message'>rx_expr_14~exp(rx_expr_10);</span> -#> <span class='message'>d/dt(m1)=-rx_expr_14*m1+parent*f_parent_to_m1*(exp(rx_expr_7-rx_expr_15)/(rx_expr_21)+exp(rx_expr_9-rx_expr_16)*(rx_expr_29))/(exp(-t*rx_expr_12)/(rx_expr_21)+exp(-t*rx_expr_13)*(rx_expr_29));</span> -#> <span class='message'>rx_expr_0~CMT==2;</span> -#> <span class='message'>rx_expr_1~CMT==1;</span> -#> <span class='message'>rx_expr_2~1-(rx_expr_0);</span> -#> <span class='message'>rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0);</span> -#> <span class='message'>rx_expr_3~(rx_expr_0);</span> -#> <span class='message'>rx_expr_5~(rx_expr_2);</span> -#> <span class='message'>rx_expr_20~rx_expr_5*(rx_expr_1);</span> -#> <span class='message'>rx_lambda_~rx_expr_20+rx_expr_3;</span> -#> <span class='message'>rx_hi_~rx_expr_20+rx_expr_3;</span> -#> <span class='message'>rx_low_~0;</span> -#> <span class='message'>rx_expr_4~m1*(rx_expr_0);</span> -#> <span class='message'>rx_expr_11~parent*(rx_expr_2);</span> -#> <span class='message'>rx_expr_24~rx_expr_11*(rx_expr_1);</span> -#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_24)*(rx_expr_0)+(rx_expr_4+rx_expr_24)*(rx_expr_2)*(rx_expr_1);</span> -#> <span class='message'>rx_expr_17~Rx_pow_di(THETA[8],2);</span> -#> <span class='message'>rx_expr_18~Rx_pow_di(THETA[7],2);</span> -#> <span class='message'>rx_r_=(Rx_pow_di(((rx_expr_4+rx_expr_24)*(rx_expr_0)+(rx_expr_4+rx_expr_24)*(rx_expr_2)*(rx_expr_1)),2)*rx_expr_17+rx_expr_18)*(rx_expr_0)+(Rx_pow_di(((rx_expr_4+rx_expr_24)*(rx_expr_1)),2)*rx_expr_17+rx_expr_18)*(rx_expr_2)*(rx_expr_1);</span> -#> <span class='message'>parent_0=THETA[1];</span> -#> <span class='message'>log_k_m1=THETA[2];</span> -#> <span class='message'>f_parent_qlogis=THETA[3];</span> -#> <span class='message'>log_k1=THETA[4];</span> -#> <span class='message'>log_k2=THETA[5];</span> -#> <span class='message'>g_qlogis=THETA[6];</span> -#> <span class='message'>sigma_low=THETA[7];</span> -#> <span class='message'>rsd_high=THETA[8];</span> -#> <span class='message'>eta.parent_0=ETA[1];</span> -#> <span class='message'>eta.log_k_m1=ETA[2];</span> -#> <span class='message'>eta.f_parent_qlogis=ETA[3];</span> -#> <span class='message'>eta.log_k1=ETA[4];</span> -#> <span class='message'>eta.log_k2=ETA[5];</span> -#> <span class='message'>eta.g_qlogis=ETA[6];</span> -#> <span class='message'>parent_0_model=rx_expr_6;</span> -#> <span class='message'>k_m1=rx_expr_14;</span> -#> <span class='message'>k1=rx_expr_12;</span> -#> <span class='message'>k2=rx_expr_13;</span> -#> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> -#> <span class='message'>g=1/(rx_expr_21);</span> -#> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_m1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 18.28 0.455 18.73</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_dfop_sfo_focei</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='error'>Error in AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm): object 'f_nlmixr_dfop_sfo_saem' not found</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/summary-methods.html'>summary</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_sfo</span>, data <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT" +#> <span style='font-weight: bold;'>Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation +#> F: Forward difference gradient approximation +#> C: Central difference gradient approximation +#> M: Mixed forward and central difference gradient approximation +#> Unscaled parameters for Omegas=chol(solve(omega)); +#> Diagonals are transformed, as specified by foceiControl(diagXform=) +#> |-----+---------------+-----------+-----------+-----------+-----------| +#> | #| Objective Fun | parent_0 | log_k_m1 |f_parent_qlogis | log_k1 | +#> |.....................| log_k2 | g_qlogis | sigma_low | rsd_high | +#> |.....................| o1 | o2 | o3 | o4 | +#> <span style='text-decoration: underline;'>|.....................| o5 | o6 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 1</span>| 496.98032 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 496.98032 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 496.98032</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | G| Gill Diff. | 57.10 | -0.1453 | -0.1275 | 0.2854 | +#> |.....................| -0.6156 | 0.007043 | -23.49 | -32.87 | +#> |.....................| 3.669 | -17.46 | -13.05 | -13.08 | +#> <span style='text-decoration: underline;'>|.....................| -16.16 | -9.766 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 2</span>| 3094.8373 | 0.2572 | -0.9978 | -0.9392 | -0.9714 | +#> |.....................| -0.9920 | -0.9233 | -0.6037 | -0.4942 | +#> |.....................| -0.9579 | -0.6658 | -0.7293 | -0.7310 | +#> <span style='text-decoration: underline;'>|.....................| -0.6848 | -0.7742 |...........|...........|</span> +#> | U| 3094.8373 | 26.15 | -4.052 | -0.9415 | -2.363 | +#> |.....................| -4.062 | -0.01133 | 0.8386 | 0.08074 | +#> |.....................| 0.6445 | 1.946 | 1.477 | 1.348 | +#> <span style='text-decoration: underline;'>|.....................| 1.794 | 1.297 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 3094.8373</span> | 26.15 | 0.01739 | 0.2806 | 0.09412 | +#> |.....................| 0.01721 | 0.4972 | 0.8386 | 0.08074 | +#> |.....................| 0.6445 | 1.946 | 1.477 | 1.348 | +#> <span style='text-decoration: underline;'>|.....................| 1.794 | 1.297 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 3</span>| 557.60681 | 0.9257 | -0.9995 | -0.9407 | -0.9680 | +#> |.....................| -0.9992 | -0.9232 | -0.8787 | -0.8790 | +#> |.....................| -0.9150 | -0.8703 | -0.8821 | -0.8842 | +#> <span style='text-decoration: underline;'>|.....................| -0.8739 | -0.8885 |...........|...........|</span> +#> | U| 557.60681 | 94.11 | -4.053 | -0.9430 | -2.360 | +#> |.....................| -4.069 | -0.01133 | 0.7386 | 0.06794 | +#> |.....................| 0.6735 | 1.622 | 1.284 | 1.172 | +#> <span style='text-decoration: underline;'>|.....................| 1.513 | 1.165 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 557.60681</span> | 94.11 | 0.01736 | 0.2803 | 0.09444 | +#> |.....................| 0.01709 | 0.4972 | 0.7386 | 0.06794 | +#> |.....................| 0.6735 | 1.622 | 1.284 | 1.172 | +#> <span style='text-decoration: underline;'>|.....................| 1.513 | 1.165 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 4</span>| 543.47785 | 0.9926 | -0.9997 | -0.9408 | -0.9677 | +#> |.....................| -0.9999 | -0.9232 | -0.9062 | -0.9175 | +#> |.....................| -0.9107 | -0.8907 | -0.8974 | -0.8995 | +#> <span style='text-decoration: underline;'>|.....................| -0.8929 | -0.9000 |...........|...........|</span> +#> | U| 543.47785 | 100.9 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7286 | 0.06666 | +#> |.....................| 0.6764 | 1.589 | 1.264 | 1.154 | +#> <span style='text-decoration: underline;'>|.....................| 1.485 | 1.152 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 543.47785</span> | 100.9 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7286 | 0.06666 | +#> |.....................| 0.6764 | 1.589 | 1.264 | 1.154 | +#> <span style='text-decoration: underline;'>|.....................| 1.485 | 1.152 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 5</span>| 544.09017 | 0.9993 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9089 | -0.9213 | +#> |.....................| -0.9103 | -0.8928 | -0.8990 | -0.9010 | +#> <span style='text-decoration: underline;'>|.....................| -0.8948 | -0.9011 |...........|...........|</span> +#> | U| 544.09017 | 101.6 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7276 | 0.06654 | +#> |.....................| 0.6767 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.09017</span> | 101.6 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7276 | 0.06654 | +#> |.....................| 0.6767 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 6</span>| 544.17109 | 0.9999 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8949 | -0.9012 |...........|...........|</span> +#> | U| 544.17109 | 101.6 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.17109</span> | 101.6 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 7</span>| 544.17937 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.17937 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.17937</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 8</span>| 544.18025 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18025 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18025</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 9</span>| 544.18033 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18033 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18033</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 10</span>| 544.18034 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18034 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18034</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 11</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 12</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 13</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 14</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 15</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 16</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 17</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> calculating covariance matrix +#> done</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#> <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#> <span class='warning'>Warning: using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS</span></div><div class='output co'>#> <span class='warning'>Warning: gradient problems with initial estimate and covariance; see $scaleInfo</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_dfop_sfo_focei</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span></div><div class='output co'>#> </div><div class='output co'>#> df AIC +#> f_nlmixr_dfop_sfo_saem$nm 16 Inf +#> f_nlmixr_dfop_sfo_focei$nm 14 886.4573</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/summary-methods.html'>summary</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_sfo</span>, data <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> </div><div class='output co'>#> <span class='error'>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'f_nlmixr_dfop_sfo_sfo' not found</span></div><div class='input'><span class='co'># }</span> </div></pre> |